Discussion 4 yusle

Ace your studies with our custom writing services! We've got your back for top grades and timely submissions, so you can say goodbye to the stress. Trust us to get you there!


Order a Similar Paper Order a Different Paper

To answer this week’s discussion questions will require that you read the articles on dual processing theory and reducing diagnostic errors ATTACHED. You are expected to apply the course readings mentioned below .YOU WILL NOT BE ABLE TO ANSWER THIS WEEK’S DISCUSSION QUESTION WITHOUT READING THE ASSIGNED ARTICLES. 

Djulbegovic et al. (2012)

Monteiro et al., (2019)

Pierret (2016 

Tsalatsanis et al., (2015)

Case: 1

Chief Complaint: “Pain in Right Side” A 40-year-old man presents to his primary care provider (PCP) with right upper quadrant (RUQ) pain for 2 days. The pain is described as “sore” and rated 4 on 1 to 10 pain scale. The pain is intermittent and not worsening. He reports food does not seem to make it better or worse. No nausea or vomiting or diarrhea or constipation are reported. 

Vital signs: heart rate, 75; blood pressure, 122/78; respiration rate, 15; afebrile. 

Examination: No acute distress. Abdomen: mildly tender on palpation at RUQ; no masses, hepatomegaly or splenomegaly. 

Diagnosis: Gallbladder disease. 

Plan: Abdominal ultrasound with reflexive cholescintigraphy (hepatobiliary iminodiacetic acid) scan within 1 week. Patient instructed to call provider if worsening symptoms occur. He is also told to avoid any fatty foods or alcohol consumption. The patient is agreeable to plan. 

Follow-up: Two days after the initial visit, the patient calls his PCP with worsening RUQ pain. Ultrasound imaging was scheduled for later that day. Patient then started having shortness of breath while at home and went to the local emergency department (ED). Computed tomography angiography of the chest revealed a right-sided pulmonary embolism. Patient did not have any family history of clotting disorders and no recent surgery, immobilization, or travel. Patient had been on testosterone injections for several years for low testosterone levels, and this was not updated in his medical record at his PC

Case 2

Chief Complaint: “Fever and Sleepy” A 3-year-old girl presents with her mother to a walk-in clinic with fever, nasal drainage, and fatigue for 2 days. She was observed hiding her head into her mother’s chest during the examination. 

Presentation occurred during flu season. The clinician had 6 positive flu tests that day, all with similar symptoms, but most including a cough. 

Vital signs: heart rate, 125; respiration rate, 20; blood pressure, 100/72; temperature, 100.8F. 

Examination: Lungs clear, heart rate regular, no murmur. Head, eyes, ears, nose, and throat: normocephalic, conjunctivae clear, tympanic membrane without bulging or redness, pharynx normal, nares normal with clear drainage, tonsils 1þ, no erythema or exudate. Patient did not want to look at the clinician in a brightly lit room. The patient was lethargic and had limited tearing when crying. Rapid flu test: Negative. 

Diagnosis: Presumptive seasonal influenza. 

Plan: Supportive care, including encouraging fluids, Over the counter acetaminophen for fever, and age-appropriate antiviral medication for the flu was prescribed. 

Follow-up: Parents were unable to keep her fever down over the next 1 day, and she progressively became more lethargic. Patient was taken to the ED, and a diagnosis of viral meningitis and dehydration was made. Patient spent several days in the hospital, but did completely recover.

  1. Describe the Dual Process Theory and Reasoning Process and how it applies to making decisions for the advanced practice nurse. 
  2. What are cognitive dispositions to respond? How are these applied in the APN setting. 
  3. Describe cognitive debiasing?
  4. Describe how Type 1 (System 1) and Type 2 (System 2) processes and strategies can be applied to each case to help the NP make decisions and to decrease potential diagnostic error? 
  5. What considerations for change to practice should the NP consider in each situation as a way to decrease the chance of future diagnostic and care decisions. 

All discussion posts must be minimum 250 words, references must be cited in APA format 7th Edition, and must include minimum of 2 scholarly resources published within the past 5-7 years.

ONE PAGE TO ANSWER THE 2 CASES ABOVE

NO PLAGIARISM, NEED BE ORIGINAL WORK

REFERENCES IN APA, AND NEED IN TEXT CITATIONS.

DUE DATE JULY 19,2023 NO LATER

66  |  wileyonlinelibrary.com/journal/medu Medical Education. 2020;54:66–73.© 2019 John Wiley & Sons Ltd and The Association
for the Study of Medical Education

1  | INTRODUC TION

Virtually everyone would agree that a primary, yet
insufficiently met, goal of schooling is to enable stu-
dents to think critically. (Willingham, 20071)

Not everyone. We do not agree. In this paper, we defend the po-
sition that the above assertion (ie that the central focus of education
should be to inculcate general skills like critical thinking, problem solv-
ing, clinical reasoning and reflection) is indeed a myth. Although the
idea of general thinking skills has a long history, it first emerged as a

major focus of curriculum reform and research effort in the 1960s, was
discounted by evidence in the 1970s and 1980s, but has re- emerged
under different banners in the new millennium.

Our central claim is that the preponderance of evidence, in medical
education and cognitive psychology, does not support this assertion.
Instead, the evidence demonstrates again and again that the essence
of expertise is the possession of a large, organised and retrievable
body of both formal and experiential knowledge, not any kind of gen-
eral thinking skills. In this paper, we annotate a brief history of the
rise and fall, and rise again, of this assertion, providing the perspective
from both cognitive psychology and medical education research.

Received: 30 May 2018  |  Revised: 10 October 2018  |  Accepted: 13 February 2019

DOI: 10.1111/medu.13872

P R A C T I C E

Critical thinking, biases and dual processing: The enduring
myth of generalisable skills

Sandra Monteiro1,2  | Jonathan Sherbino2,3 | Matthew Sibbald2,3  |
Geoff Norman1,2

1Health Research Methods, Evidence and
Impact, McMaster University, Hamilton,
Ontario, Canada
2McMaster Education Research, Innovation
and Theory Programme, McMaster
University, Hamilton, Ontario, Canada
3Department of Medicine, McMaster
University, Hamilton, Ontario, Canada

Correspondence
Sandra Monteiro, David Braley Health
Sciences Centre, McMaster University, 100
Main Street, 5th Floor, Hamilton, ON L8P
1H6, Canada.
Email: [email protected]

Abstract
Context: The myth of generalisable thinking skills in medical education is gaining
popularity once again. The implications are significant as medical educators decide
on how best to use limited resources to prepare trainees for safe medical practice.
This myth- busting critical review cautions against the proliferation of curricular inter-
ventions based on the acquisition of generalisable skills.
Structure: This paper begins by examining the recent history of general thinking skills,
as defined by research in cognitive psychology and medical education. We describe
three distinct epochs: (a) the Renaissance, which marked the beginning of cognitive
psychology as a discipline in the 1960s and 1970s and was paralleled by educational
reforms in medical education focused on problem solving and problem- based learn-
ing; (b) the Enlightenment, when an accumulation of evidence in psychology and in
medical education cast doubt on the assumption of general reasoning or problem-
solving skill and shifted the focus to consideration of the role of knowledge in expert
clinical performance; and (c) the Counter- Enlightenment, in the current time, when
the notion of general thinking skills has reappeared under different guises, but the
fundamental problems related to lack of generality of skills and centrality of knowl-
edge remain.
Conclusions: The myth of general thinking skills persists, despite the lack of evi-
dence. Progress in medical education is more likely to arise from devising strategies
to improve the breadth and depth of experiential knowledge.

     |  67MONTEIRO ET al.

Not surprisingly, the noble idea that teaching and learning should
be about thinking, not knowledge, is not unique to medicine. One
area in which it featured prominently was American education
around the turn of the last century, when, as in classical European
education, the emphasis was on the development of “mental fac-
ulties” exemplified by the study of Latin, Greek and logic. Evidence
to the contrary was presented by Thorndike in 1906, who showed
that typical transfer effects across dissimilar tasks were very low
(described in Lehman et al2): general skills (“mental faculties”) did not
exist. However, as many health professionals can attest, the course
in Latin remained in the public school curriculum long after the ratio-
nale for its existence had disappeared.

In psychology, “mental faculties” were replaced by behaviourism,
which dominated scientific psychology, particularly in the USA, to
mid- century. However, in the latter decades of the 20th century,
this mechanistic and reductionist theoretical perspective was even-
tually superseded by cognitive psychology. Concurrently, the focus
of health professions education moved away from behavioural ob-
jectives towards the development of underlying thinking processes.

2  | THE RENAISSANCE: 1960 – 1980

2.1 | The emergence of information- processing models

2.1.1 | Cognitive psychology

A driving force behind the cognitive revolution was the develop-
ment of computers as a metaphor for human “information pro-
cessing.” Early forays into the machine as a metaphor of the mind
led to research in artificial intelligence such as Newell and Simon’s
“general problem solver” (the name says it all), based on the premise
that human (and machine) problem solving was a matter of adopting
general strategies that could then be mobilised with specific knowl-
edge bases to solve problems.3,4 The lay literature was permeated
by numerous content- free strategies—brainstorming, lateral think-
ing, synectics—that purported to lead to large increases in problem
solving and creativity, but did not. We now know that the metaphor
was taken too literally; human minds do not work in the same way as
the computers that were designed in the 1970s and 1980s.5

2.1.2 | Medical education

The origins of research on expert diagnostic reasoning are credited
to seminal work conducted in the 1970s based on the assumption
that careful observation of expert clinicians would help identify a
set of expert problem- solving skills that could be taught directly to
trainees.6-8

A general process did emerge from these studies. It consisted
of two stages: an initial “hypothesis generation” stage occurring in
the first few seconds or minutes of the encounter, followed by a
long, sequential and systematic search for additional confirmatory
data. However, three additional findings arose which presented a
serious challenge to the underlying assumption. Firstly, the process

was too general. Everyone, from first- year medical student to expert
clinician, was essentially using the same process of generating and
testing hypotheses; it was simply noted that experts were doing it
better. Secondly, when the outcome of the process—diagnostic ac-
curacy—was examined, it was found to relate primarily to only one
variable: the content of the hypothesis. If participants thought of
the diagnosis early on, they got the correct outcome; if they didn’t,
well, they didn’t. Finally, and critically, success on one problem was
no guarantee of success on another. The typical correlation across
problems was 0.1- 0.3. Thus, the notion of general problem- solving
strategies failed an empirical test.

Elstein et al, in a later paper, summarised these findings ele-
gantly: “A purely formal syntax of clinical reasoning stripped of con-
text and content could account neither for difference in the quality
of hypotheses generated nor for clinicians’ variability across cases.
It seemed, rather, that differences in domain- specific knowledge
must lie behind the ability to generate better hypotheses in specific
cases.”9

3  | THE ENLIGHTENMENT: 1980 – 20 0 0

3.1 | The role of knowledge and specificity of skills

3.1.1 | Cognitive psychology

It soon emerged that human problem solving was not a matter of ac-
quiring elaborate skills; rather, it amounted to fairly simple strategies
operating on extensive and rich knowledge networks.4

This central role of knowledge in reasoning then led to a new
field of research based on understanding expertise in different
domains. Phenomena such as “deliberate practice” emerged di-
rectly from these findings.10 In this view, expertise has nothing to
do with general strategies and everything to do with experiential
knowledge acquired from practice with many, indeed, thousands
of problems.

The limited generalisability of cognitive skills emerged in an-
other research domain: studies of transfer. An extensive research
programme examining transfer—using knowledge acquired in one
context to solve problems with the same conceptual structure in a
different context—revealed consistently that far transfer was exqui-
sitely difficult.11

Finally, the continued use of computers to simulate human
problem solving revealed that programs using general methods like
“means–end analysis,” while useful for simple problems, were inef-
fective in knowledge- rich domains and were referred to as “weak
methods.”4 Alternatives based on expert knowledge, called “expert
systems,” were far superior in specific domains.12

3.1.2 | Medical education

The early findings pointing to the centrality of knowledge led to a
change of emphasis in the study of clinical reasoning. Instead of fur-
ther pursuing some generalisable reasoning skill, researchers began

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

68  |     MONTEIRO ET al.

to explore the kinds of knowledge structures that experts use. There
emerged a plethora of possibilities: exemplars, prototypes, verbal
propositions and semantic axes.13-15 Regrettably, the very number
of possibilities led the field into disarray as the available research
methods were not sufficiently powerful to distinguish one represen-
tation from another. Indeed, it is likely that there is no one central
form of knowledge; rather, one hallmark of expertise is the mastery
of vast domains of knowledge ranging from analytical—base rates
and physiological mechanisms—to experiential.

Further developments in artificial intelligence led to computer
“decision support systems” based on expert knowledge and applica-
ble to limited domains.16 Although computer power was minuscule
in comparison with that of today’s hardware, a number of computer
applications were shown to provide a small but consistent benefit to
diagnosticians.17

4  | THE COUNTER- ENLIGHTENMENT:
20 0 0 TO THE PRESENT

4.1 | The re- emergence of thinking skills

The new millennium found a resurgence of interest in general think-
ing strategies, both in general education and psychology, and in
medical education. However, the names changed. Terms like “criti-
cal thinking,” “metacognition” and “reflection” entered the lexicon.
Moreover, “dual process” theories of decision making came to domi-
nate discourse on problem solving and diagnosis.

4.2 | Cognitive psychology

4.2.1 | Critical thinking

Critical thinking is often described as a skill that students can de-
velop in parallel with (but not necessarily connected to) knowledge
acquisition.18 Abrami et al wrote: “According to psychological views,
critical thinking requires gaining mastery of a series of discrete skills
or mental operations and dispositions that can be generalised across
a variety of contexts.”18

By far the majority of the literature assumes that critical thinking
is a general, context- independent set of skills: “judgement, analysis,
evaluation, inference” and attitudes “inquisitive, well- informed…
open- minded, flexible… prudent…” and so on.19 The effect of general
critical thinking instruction is commonly assessed by measures such
as the Watson–Glaser Critical Thinking Appraisal (WGCTA), de-
signed to test general critical thinking processes.20 Studies use it ei-
ther as an independent variable to show that scores on the WGCTA
are correlated with in- course grades or other outcomes (suggesting
critical thinking is a trait or stable skill), or as a dependent variable to
show that some instructional intervention or just years of education
improve test scores (suggesting it is educable).18 However, there is
no unanimity on the subject; the review cited above18 specifically
discusses the issue of general versus content- specific thinking, as
well as multiple problems in interpretation.

4.2.2 | Metacognition

Metacognition refers to awareness of one’s thinking. Most research
on metacognition assumes that it consists of general self- regulatory
activities that can be learned distinct from knowledge and that
“help[ing] students monitor, reflect upon, and improve their strat-
egies for learning and problem solving.”21 However, Bransford and
Schwartz point out: “Research also suggests that metacognitive
activities have strong knowledge requirements; they are not gen-
eral skills that people learn “once and for all.” For example, without
well- differentiated knowledge of the performance requirements of
a particular task, people cannot accurately assess whether they are
prepared to perform that task.”11

Thus, although descriptions of metacognition and critical think-
ing emphasise generality, and instruction and assessment tend to
look at general processes, there are occasional acknowledgements
of the role of knowledge.

4.2.3 | Dual process theories and cognitive biases

Dual process theories posit two underlying thinking processes: a
fast, unconscious, contextually bound process (System 1 or Type 1)
and a slow, conscious, effortful, decontextualised process (System 2
or Type 2).22 These theories are descriptive, not predictive; they de-
scribe how ambiguous problems are solved rather than prescribing
how they should be solved or predicting how they will be solved. It
is quite germane to understanding the source of the myth that many
authors have interpreted this theory to offer a predictive model of
cognitive error.

The dominant dual process theory, called “default-
interventionist,” posits System 1 as the default strategy and System 2
as a backup with which to intervene as appropriate to correct the
inevitable errors of System 1. System 1 reasoning makes heavy use
of cognitive shortcuts or “heuristics” as a consequence of the limita-
tions of human information processing. These in turn are expected
to be error- prone and to lead to bias.23 Most errors are presumed
to arise in System 1 as a consequence of biases in System 1 and can
only be resolved using System 2. Dual process models of diagnosis
amount to a direct test of the effectiveness of general, analytical
methods (System 2) against strategies that depend on local content
and contextual knowledge (System 1).

The assumption that errors originate in System 1 and are cor-
rected by System 2 runs directly counter to the findings discussed
earlier in the Enlightenment section, in which simple strategies
based on elaborate specific knowledge consistently outperform
more general, knowledge- lean strategies. Although it is obviously
extreme to suggest that System 2 strategies are characterised by
rationality, not specific knowledge, domain knowledge does not fea-
ture prominently in this discourse.

The claim that errors arise uniquely in System 1 has been chal-
lenged by some “dual processing” theorists. Evans and Stanovich
state unequivocally: “Perhaps the most persistent fallacy… is the
idea that Type 1 processes (intuitive, heuristic) are responsible for all

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

     |  69MONTEIRO ET al.

bad thinking and that Type 2 processes (reflective, analytic) neces-
sarily lead to correct responses. Thus, various forms of dual process
theory have blamed Type 1 processing for cognitive biases in reason-
ing and judgement research… Correspondingly, logical reasoning, ra-
tional decision making, and nonstereotypical judgements have been
attributed to Type 2 processing.”22

Although the notion that errors originate in System 1 and are
ameliorated in System 2 is pervasive, recent theory and evidence
suggest otherwise.

4.3 | Medical education

4.3.1 | Critical thinking

Similar to the psychology literature, some studies examine the rela-
tionship between scores on a critical thinking test and outcomes such
as clinical decision making or clerkship performance,24-26 implicitly
assuming that critical thinking is trait- like. Alternatively, studies look
for improvement in critical thinking with years of education,27 which
presumes it is skill- like, gradually increasing over time. In general, as-
sociations are modest, leading authors of review articles to conclude
“limited concurrent validity,”28 “evidence… is still unsubstantiated,”24
and “results… are mixed and contradictory.”28

4.3.2 | Metacognition

Within medicine, awareness of one’s own thinking has been trans-
lated into the act of reflection. Many education programmes incor-
porate formal and informal reflective activities to help develop skills
related to professionalism. Formally, being a reflective practitioner
or having a reflective professional practice is associated with the
classic work of Donald Schön, who emphasises activity that allows
examination of one’s actions and thoughts from the near “reflection
in action” or distant “reflection on action” past.29 Typically, this activ-
ity occurs organically, in response to complexity, ambiguity or uncer-
tainty.30 This has been interpreted by some authors to suggest that
a general strategy of meta- awareness and reflection used routinely
will improve performance.31,32

We are aware of very few attempts to operationalise reflection
and conduct systematic study of its effectiveness. One exception is
the programme of research initiated by Mamede et al.33-37 However,
in their studies, “reflection” is designed as a strategy to mobilise rele-
vant knowledge using questions like “What features go against your
diagnosis?” and “What other diagnoses might be relevant?”

Several studies have incorporated these specific “reflection”
instructions in either cross- sectional designs (in which one group
uses some variant on a reflective protocol and another group
does not) or longitudinal designs (in which participants initially
go through cases quickly and then are encouraged to revisit the
cases). The structured strategy33-37 shows relatively consistent
results—a benefit for simple cases with students and complex
cases with residents. Other studies evaluating a less structured
approach (ie instructions to simply take another look or to be

systematic, consistent with realistic constraints of practice) have
shown no effect for simple or complex cases.38,39

Moreover, physicians and students have difficulty recognis-
ing when they have made an error.38 For example, in a study by
Monteiro et al,38 physicians were unable to determine which cases
required further reflection. Similarly, Friedman et al found that stu-
dents, residents and physicians were consistently overconfident in
their diagnoses.17,40

Metacognitive strategies to improve reasoning have received
some attention in the literature, often with suggestions of im-
proved outcomes resulting from training in self- monitoring.41,42
However, upon closer inspection these improved outcomes do not
transfer to diagnostic accuracy. For example, targeted instruction
in solving challenging syllogisms reduced overconfidence, but had
no impact on accuracy.41 Specific instructions in metacognition
focusing on the process used in diagnosis were shown to lead to
improved “metacognitive accuracy” but no improvement in diag-
nostic accuracy.42

4.3.3 | Dual process theories and cognitive biases

As indicated, a dual process model of thinking has become the ac-
cepted theory of clinical reasoning. Central to the theory is the no-
tion that successful reasoning reflects effective thinking processes
and, conversely, that errors are a consequence of flawed reasoning,
originating in cognitive heuristics. This involves two strong assump-
tions about the nature of clinical reasoning, both of which are sub-
ject to empirical testing.

Assumption 1
Almost all errors of diagnosis are a consequence of cognitive biases
originating in System 1 thinking.43

The notion that System 1 thinking and cognitive biases are
primary causes of diagnostic error has been described by many
authors43-46 and features in the Institute of Medicine report.47
However, the supporting evidence is weak. Although the numbers
of biases described in the literature range from 30 to 130, three sys-
tematic reviews of cognitive biases in medicine identify a total of
24 biases for which there is evidence.48-50 However, these encom-
pass not just diagnostic errors, but also errors of management and
prognosis, as well as patient- related biases; only seven are related
to diagnostic error. Moreover, only three biases—availability, confir-
mation and hindsight—are cited in all three systematic reviews.48-50
Each has issues related to interpretation with respect to the asser-
tion that cognitive bias is intimately linked to System 1 thinking.

Here, we examine the established definitions of three common
biases.

Availability bias: the disposition to judge things as
being more likely, or frequently occurring, if they
readily come to mind. Thus, recent experience with
a disease may inflate the likelihood of its being
diagnosed.43

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

70  |     MONTEIRO ET al.

Availability is one of the commonest cognitive biases identified
by reviewers of diagnostic errors occurring in episodes of care.51,52
However, this is logically impossible. An observer, or an auditor review-
ing a written case, has no way of knowing what “readily came to mind”
to the clinician. For that matter, as System 1 thinking is characterised as
an unconscious, intuitive process, neither does the clinician who man-
aged the patient.

Confirmation bias: the tendency to look for confirm-
ing evidence to support a diagnosis rather than look
for disconfirming evidence to refute it, despite the
latter often being more persuasive and definitive.43

Confirmation bias was first studied by Wason53 using sets of num-
ber sequences generated by an analytical rule. In a clinical context,
as the definition suggests, this arises in the systematic gathering and
weighting of evidence to preferentially support hypotheses. This is not
a consequence of System 1 reasoning.

Hindsight bias: knowing the outcome may profoundly
influence the perception of past events and prevent a
realistic appraisal of what actually occurred.43

Hindsight arises when the outcome is known. Thus, the determina-
tion of underlying causes of error is a central issue for those reviewing
clinical cases, but is not a problem for a clinician seeking a diagnosis.
Hindsight bias is not related to System 1 thinking.

As one example of hindsight, Zwaan et al54 experimentally ma-
nipulated scenarios with two equi- probable diagnoses and gave
them to expert reviewers who were asked to identify biases. When
the conclusion of the scenario did not align with the initial diagnostic
approach, raters identified twice as many biases as when it was con-
sistent with the diagnosis, an illustration of hindsight bias.

On close inspection, many of the definitions appear to overlap.
An empirical question concerns the extent to which observers can
consistently and reliably identify cognitive biases. The study by
Zwaan et al54 systematically explored inter- rater agreement on the
presence or absence of cognitive biases and demonstrated that reli-
ability for six common biases was effectively zero.

There are also problems with the methods used to investigate
biases. Two broad strategies have been used: retrospective observa-
tion and experimental study. We have already discussed the problem
of hindsight in retrospective review.

Experimental studies typically use written scenarios that are ex-
plicitly designed to illustrate particular cognitive biases. The strategy
is to create situations in which the most likely response based on
experience is at variance with the normative (correct) response.55
The question then concerns the extent to which participants choose
the intuitive or normative response.56

What makes this approach problematic is that the scenarios are
atypical in three fundamental ways. Firstly, they are designed to
contain one or more cognitive biases and hence the prevalence of
bias in this population is 100%. Secondly, they are designed so that

the intuitive and normative responses are in opposition in order to
detect the presence of bias, but this automatically makes them atyp-
ical. Finally, they typically do not examine a relationship to expertise.
However, expertise does affect cognitive bias; three studies have
shown that bias disappears with expertise.57-59

Assumption 2
Because errors are a consequence of cognitive biases, error re-
duction strategies should focus on approaches that help clinicians
identify cognitive biases and effectively use System 2 reasoning to
correct the errors inherent in System 1 thinking (eg cognitive forcing
strategies).43,51,60

Cognitive forcing strategies are general strategies for im-
proving metacognition and reasoning “in the moment.”61 The
basic concept is to increase self- awareness of one’s own think-
ing and identify potential errors by avoiding common cognitive
biases.43,60,61

Perhaps the simplest “cognitive forcing strategy” is some form
of instruction to caution the clinician to be systematic, to slow
down or to consider alternatives. In a number of studies, rapid
diagnosis has been compared with slower systematic reflec-
tion.38,39,62-64 A uniform finding is that instructions to be system-
atic and thorough result in longer processing time, but have no
impact on diagnostic errors.

Several reviews have examined the effectiveness of strat-
egies designed to teach students to recognise biases.32,49,50,65
Training increases awareness of cognitive biases.66-68 However,
studies of the effect of debiasing on diagnostic errors have been
negative.69-71

4.4 | Summary

The evidence shows that generalised, content- independent strate-
gies—debiasing, reflection or whatever—to reduce errors have no or
minimal effectiveness for the simple reason that errors derive not
from inadequate thinking skills but from inadequate knowledge.
Reflection strategies may have a small benefit, although effects are
uneven.65 Debiasing strategies have shown uniformly null effects.65
Thus, attempts to encourage clinicians to reflect on the process or
reasoning, or to apply general analytical approaches, may not be ef-
fective for the simple reason that they focus on analytical skills, or
on weak methods, not knowledge.

As Dhaliwal said: “If you have not heard about myasthenia gravis,
you cannot cognitively debias your way into that diagnosis. […] In the
realm of expert performance, knowledge is king.”72

5  | DISCUSSION

In light of the accumulated evidence, we must address why the myth
of general skills has persisted in medical education for half a cen-
tury. One reason may be that it offers a shortcut to mastery of the
many areas of knowledge required in the practice of medicine. In

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

     |  71MONTEIRO ET al.

2002, Croskerry wrote: “…we need to know whether we can make
clinicians better decision makers without simply waiting for the
improved judgement that comes with the benefit of accumulated
experience.”60

Medical education curricula suffer from restricted time and re-
sources. Perhaps medical educators and curriculum designers are
inevitably drawn towards generalisable skills that have the illusion
of transfer, thereby avoiding the necessity of learning and practice
in myriad different knowledge domains. The myth persists because
everyone is too busy.

The movement towards competency- based education illus-
trates the pervasiveness of the myth. Although some competen-
cies are based on specific knowledge, such as a lumbar puncture or
intubation, many are framed around content and the context- free
competencies to be acquired, such as “performing a complete and
systematic history” or “demonstrating interprofessional collabora-
tion skills.” Underlying the “good history” is an implicit assumption
of generalisability. It should not matter how or even if the approach
is paired with specific clinical content; the approach should transfer
easily. However, the evidence we have presented, from both psy-
chology and medical education, casts doubt on the generalisability
of any cognitive skill.

Finally, the assumption of debiasing strategies is that the root
cause of error is cognitive bias. This has some appeal: it absolves
the clinician from blame; cognitive bias is simply part of the human
condition. However, if cognitive bias is so central to diagnostic error,
then training in critical thinking or cognitive debiasing should result
in improved reasoning skill and reduced error. Evidence for such an
effect is conspicuously absent.

The consequence of the persistence of the myth is that valuable
instructional and learning time may be devoted to mastering inter-
ventions that, in the end, are not effective. Although it may be use-
ful to draw attention to the prevalence of diagnostic error, the long
litany of cognitive biases has no added value. Ample evidence has
demonstrated repeatedly that identifying biases is not equivalent to
reducing error.

Reducing error rates is a laudable goal. However, strategies
based on the broad assumption that this can be achieved by tun-
ing up a general problem- solving process in order to compensate for
the perceived defects of intuitive reasoning have been shown to be
ineffective. Progress in this area is more likely to arise from accept-
ing the power of System 1 reasoning and devising strategies such as
interleaved practice to improve the breadth and depth of the experi-
ential knowledge used by System 1.

6  | CONCLUSIONS

We have shown that the current models of reasoning and thinking
popularised in medical education perpetrate a theoretical position
that is inconsistent with the evidence accrued for over a century in
psychology and half a century in medicine. There is no justification
for the position that knowledge- rich, efficient strategies, as used by

experts, can be viewed as inferior to general analytical and “rational”
approaches.

We are not the first to challenge the assertion that rational gen-
eralisable rules are more effective than heuristics derived from case
experience.73,74 Dreyfus, acknowledged as the guru of artificial in-
telligence, wrote: “We must be prepared to abandon the traditional
view that runs from Plato to… Piaget … Chomsky… that a beginner
starts with specific cases and… as he or she becomes more profi-
cient, abstracts and interiorises more and more sophisticated rules.
It might turn out that skill acquisition moves in just the opposite di-
rection: from abstract rules to particular cases.”75

The deification of rational, decontextualised strategies is consis-
tent with some claims that they represent an evolutionary adapta-
tion from contextualised heuristic approaches. It would be a marvel
indeed if human cognition were able to evolve purely through men-
tal effort. In the current era of specialised expertise, there is every
reason to challenge the assertion we introduced at the beginning of
this paper: that the central focus of education should be to incul-
cate general skills like critical thinking, problem solving, clinical rea-
soning and reflection. Perhaps Homo sapiens—“wise man”—should
be replaced by “Homo sciens”—“knowing man”—at the apex of
evolution.

ACKNOWLEDG EMENTS

None.

CONFLIC T OF INTERE S T

None.

AUTHOR CONTRIBUTIONS

SM contributed to the conceptual design and layout of the manu-
script. SM wrote the first draft and contributed to significant revi-
sions of subsequent drafts. JS contributed to the conceptual design
and layout of the manuscript. He contributed edits to the first draft
of the manuscript. MS contributed to the conceptual design of the
manuscript. MS contributed to revisions of the first draft of the man-
uscript. GN contributed to the conceptual design and layout of the
manuscript. GN contributed to significant revisions of subsequent
drafts.

E THIC AL APPROVAL

Not applicable.

ORCID

Sandra Monteiro https://orcid.org/0000-0001-8723-5942

Matthew Sibbald https://orcid.org/0000-0002-0022-2370

Geoff Norman https://orcid.org/0000-0003-1053-7998

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

72  |     MONTEIRO ET al.

R E FE R E N C E S

1. Willingham DT. Critical thinking. Am Ed. 2007;31(3):8-19.
2. Lehman DR, Lempert RO, Nisbett RE. The effects of graduate train-

ing on reasoning: formal discipline and thinking about everyday- life
events. Am Psychol. 1988;43(6):431-442.

3. Newell A, Simon HA. Human Problem Solving. Englewood Cliffs, NJ:
Prentice-Hall; 1972.

4. Perkins DN, Salomon G. Are cognitive skills context- bound? Ed Res.
1989;18(1):16-25.

5. Searle JR. Minds, brains, and programs. Behav Brain Sci.
1980;3(3):417-424.

6. Elstein AS, Shulman LS, Sprafka SA. Medical Problem Solving an
Analysis of Clinical Reasoning. Cambridge, MA: Harvard University
Press; 1978.

7. Barrows HS, Norman GR, Neufeld VR, Feightner JW. The clinical
reasoning of randomly selected physicians in general medfvical
practice. Clin Invest Med. 1982;5(1):49-55.

8. Neufeld VR, Norman GR, Feightner JW, Barrows HS. Clinical
problem- solving by medical students: a cross- sectional and longi-
tudinal analysis. Med Educ. 1981;15(5):315-322.

9. Elstein AS, Shulman LS, Sprafka SA. Medical problem solving: a ten-
year retrospective. Eval Health Prof. 1990;13(1):5-36.

10. Ericsson KA, Krampe RT, Tesch-Römer C. The role of deliberate
practice in the acquisition of expert performance. Psychol Rev.
1993;100(3):363.

11. Bransford JD, Schwartz DL. Chapter 3: Rethinking trans-
fer: a simple proposal with multiple implications. Rev Res Educ.
1999;24(1):61-100.

12. Schoenfeld AH. Mathematical Problem Solving. New York, NY:
Academic Press; 1985.

13. Norman G, Young M, Brooks L. Non- analytical models of clinical rea-
soning: the role of experience. Med Educ. 2007;41(12):1140-1145.

14. Bordage G, Zacks R. The structure of medical knowledge in the
memories of medical students and general practitioners: categories
and prototypes. Med Educ. 1984;18(6):406-416.

15. Patel VL, Groen GJ. Knowledge based solution strategies in medical
reasoning. Cog Sci. 1986;10(1):91-116.

16. Miller RA, Pople HE Jr, Myers JD. Internist- I, an experimental
computer- based diagnostic consultant for general internal medi-
cine. New Engl J Med. 1982;307(8):468-476.

17. Friedman CP, Elstein AS, Wolf FM, et al. Enhancement of clinicians’
diagnostic reasoning by computer- based consultation: a multisite
study of 2 systems. JAMA. 1999;282(19):1851-1856.

18. Abrami PC, Bernard RM, Borokhovski E, et al. Instructional inter-
ventions affecting critical thinking skills and dispositions: a stage 1
meta- analysis. Rev Educ Res. 2008;78(4):1102-1134.

19. Facione PA. Critical Thinking: A Statement of Expert Consensus for
Purposes of Educational Assessment and Instruction. Millbrae, CA:
The California Academic Press; 1990.

20. Watson G, Glaser EM. Watson-Glaser Critical Thinking Appraisal. San
Antonio: PsychCorp; 1980.

21. Flavell JH. Metacognitive aspects of problem solving. In: Resnick
LB, ed. The Nature of Intelligence. Hillsdale, NJ: Lawrence Erlbaum
Associates; 1976:231-235.

22. Evans JS, Stanovich KE. Dual- process theories of higher cognition:
advancing the debate. Persp Psychol Sci. 2013;8(3):223-241.

23. Kahneman D. Thinking, Fast and Slow. New York, NY: Farrar, Straus
and Giroux; 2011.

24. Lee DS, Abdullah KL, Subramanian P, Bachmann RT, Ong SL. An
integrated review of the correlation between critical thinking
ability and clinical decision- making in nursing. J Clin Nurs. 2017
Dec;26(23–24):4065-4079.

25. Scott JN, Markert RJ. Relationship between critical thinking skills
and success in preclinical courses. Acad Med. 1994;69(11):920-924.

26. Ross D, Loeffler K, Schipper S, Vandermeer B, Allan GM. Do
scores on three commonly used measures of critical think-
ing correlate with academic success of health professions
trainees? A systematic review and meta- analysis. Acad Med.
2013;88(5):724-734.

27. Scott JN, Markert RJ, Dunn MM. Critical thinking: change during
medical school and relationship to performance in clinical clerk-
ships. Med Educ. 1998;32(1):14-18.

28. Adams BL. Nursing education for critical thinking: an integrative
review. J Nurs Educ. 1999;38(3):111-119.

29. Mann K, Gordon J, MacLeod A. Reflection and reflective practice
in health professions education: a systematic review. Adv Health Sci
Ed. 2009;14(4):595-621.

30. Mamede S, Schmidt HG. The structure of reflective in medicine.
Med Educ. 2004;38(12):1302-1308.

31. Chisholm CD, Dornfeld AM, Nelson DR, Cordell WH. Work in-
terrupted: a comparison of workplace interruptions in emer-
gency departments and primary care offices. Ann Emerg Med.
2001;38(2):146-151.

32. Graber ML, Kissam S, Payne VL, et al. Cognitive interventions
to reduce diagnostic error: a narrative review. BMJ Qual Saf.
2012;21(7):535-557.

33. Mamede S, Schmidt HG, Penaforte JC. Effects of reflec-
tive practice on the accuracy of medical diagnoses. Med Educ.
2008;42(5):468-475.

34. Mamede S, van Gog T, van den Berge K, et al. Effect of availability
bias and reflective reasoning on diagnostic accuracy among internal
medicine residents. JAMA. 2010 Sep 15;304(11):1198-1203.

35. Mamede S, Splinter TA, van Gog T, Rikers RM, Schmidt HG.
Exploring the role of salient distracting clinical features in the emer-
gence of diagnostic errors and the mechanisms through which re-
flection counteracts mistakes. BMJ Qual Saf. 2012;21:295-300.

36. Mamede S, Schmidt HG. Reflection in diagnostic reasoning: what
really matters? Acad Med. 2014;89:959-960.

37. Mamede S, van Gog T, van den Berge K, van Saase JL, Schmidt HG.
Why do doctors make mistakes? A study of the role of salient dis-
tracting clinical features. Acad Med. 2014;89:114-120.

38. Monteiro SD, Sherbino J, Patel A, Mazzetti I, Norman GR, Howey E.
Reflecting on diagnostic errors: taking a second look is not enough.
J Gen Intern Med. 2015;30(9):1270-1274.

39. Monteiro SD, Sherbino JD, Ilgen JS, et al. Disrupting diagnostic
reasoning: do interruptions, instructions, and experience affect the
diagnostic accuracy and response time of residents and emergency
physicians? Acad Med. 2015;90(4):511-517.

40. Friedman CP, Gatti GG, Franz TM, et al. Do physicians know when
their diagnoses are correct? J Gen Intern Med. 2005;20(4):334-339.

41. Ackerman R, Thompson VA. Meta- reasoning: monitor-
ing and control of thinking and reasoning. Trends Cogn Sci.
2017;21(8):607-617.

42. Feyzi-Behnagh R, Azevedo R, Legowski E, Reitmeyer K, Tseytlin
E, Crowley RS. Metacognitive scaffolds improve self- judgments
of accuracy in a medical intelligent tutoring system. Instr Sci.
2014;42(2):159-181.

43. Croskerry P. The importance of cognitive errors in diagnosis and
strategies to minimize them. Acad Med. 2003;7(8):775-780.

44. Royce CS, Hayes MM, Schwartzstein RM. Teaching critical thinking:
a case for instruction in cognitive biases to reduce diagnostic errors
and improve patient safety. Acad Med. 2018;94(2):187-194.

45. Redelmeier DA. The cognitive psychology of missed diagnoses. Ann
Intern Med. 2005;142(2):115-120.

46. Chapman GB, Elstein AS. Cognitive processes and biases in med-
ical decision making. Decision making in health care: theory, psy-
chology, and applications. In: Chapman GB, Sonnenberg FA, eds.
Decision Making in Health Care: Theory, Psychology, and Applications.
New York, NY: Cambridge University Press; 2000:183-210.

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

     |  73MONTEIRO ET al.

47. Corrigan JM. Crossing the quality chasm. In: Corrigan JM, ed. Building
a Better Delivery System: A new Engineering/Health Care Partnership.
Washington, DC: National Academies Press; 2005:95-97.

48. Blumenthal-Barby JS, Krieger H. Cognitive biases and heuristics in
medical decision making: a critical review using a systematic search
strategy. Med Decis Making. 2015;35(4):539-557.

49. Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive biases
associated with medical decisions: a systematic review. BMC Med
Inform Decis. 2016;16(1):138.

50. Lambe KA, O’Reilly G, Kelly BD, Curristan S. Dual- process cognitive
interventions to enhance diagnostic reasoning: a systematic review.
BMJ Qual Saf. 2016;25:808-820.

51. Graber ML, Franklin N, Gordon R. Diagnostic error in internal med-
icine. Arch Intern Med. 2005;165(13):1493-1499.

52. Zwaan L, Thijs A, Wagner C, van der Wal G, Timmermans DRM.
Relating faults in diagnostic reasoning with diagnostic errors and
patient harm. Acad Med. 2012;87(2):149-156.

53. Wason PC. On the failure to eliminate hypotheses in a conceptual
task. Q J Exp Psychol. 1960;12(3):129-140.

54. Zwaan L, Monteiro S, Sherbino J, Ilgen J, Howey B, Norman G. Is
bias in the eye of the beholder? A vignette study to assess recog-
nition of cognitive biases in clinical case workups. BMJ Qual Saf.
2017;26(2):104-110.

55. Lopes LL. The rhetoric of irrationality. Theor Psychol. 1991;1(1):65-82.
56. Lopes LL. Three misleading assumptions in the customary rhetoric

of the bias literature. Theor Psychol. 1992;2(2):231-236.
57. Weber EU, Böckenholt U, Hilton DJ, Wallace B. Determinants of di-

agnostic hypothesis generation: effects of information, base rates,
and experience. J Exp Psych: Learn. 1993;19(5):1151-1164.

58. Christensen C, Heckerling PS, Mackesy ME, Bernstein LM, Elstein
AS. Framing bias among expert and novice physicians. Acad Med.
1991;66(9):76-78.

59. Hatala RA, Norman GR, Brooks LR. The effect of clinical history on
physician’s ECG interpretation skills. In: Scherpbier AJJA, van der
Vleuten CPM, Rethans JJ, van der Steeg AFW. Advances in Medical
Education. Maastricht: Springer, Dordrecht; 1997: 608-610.

60. Croskerry P. Achieving quality in clinical decision making:
cognitive strategies and detection of bias. Acad Emerg Med.
2002;9(11):1184-1204.

61. Croskerry P. Cognitive forcing strategies in clinical decisionmaking.
Ann Emerg Med. 2003;41(1):110-120.

62. Norman G, Sherbino J, Dore K, et al. The etiology of diagnostic er-
rors: a controlled trial of system 1 versus system 2 reasoning. Acad
Med. 2014;89(2):277-284.

63. Sherbino J, Dore KL, Wood TJ, et al. The relationship between re-
sponse time and diagnostic accuracy. Acad Med. 2012;87(6):785-791.

64. Ilgen JS, Bowen JL, Yarris LM, Fu R, Lowe RA, Eva K. Adjusting our
lens: can developmental differences in diagnostic reasoning be
harnessed to improve health professional and trainee assessment?
Acad Emerg Med. 2011;18(suppl 2):S79-S86.

65. Norman GR, Monteiro SD, Sherbino J, Ilgen JS, Schmidt HG,
Mamede S. The causes of errors in clinical reasoning: cognitive
biases, knowledge deficits, and dual process thinking. Acad Med.
2017;92(1):23-30.

66. Reilly JB, Ogdie AR, Von Feldt JM, Myers JS. Teaching about how
doctors think: a longitudinal curriculum in cognitive bias and diag-
nostic error for residents. BMJ Qual Saf. 2013;22(12):1044-1050.

67. Bond WF, Deitrick LM, Arnold DC, et al. Using simulation to instruct
emergency medicine residents in cognitive forcing strategies. Acad
Med. 2004;79(5):438-446.

68. Ogdie AR, Reilly JB, Pang MW, et al. Seen through their eyes: res-
idents’ reflections on the cognitive and contextual components of
diagnostic errors in medicine. Acad Med. 2012;87(10):1361-1367.

69. Sherbino J, Dore KL, Siu E, Norman GR. The effectiveness of cogni-
tive forcing strategies to decrease diagnostic error: an exploratory
study. Teach Learn Med. 2011;23(1):78-84.

70. Sherbino J, Kulasegaram K, Howey E, Norman G. Ineffectiveness of
cognitive forcing strategies to reduce biases in diagnostic reason-
ing: a controlled trial. Can J Emerg Med. 2014;16(1):34-40.

71. Shimizu T, Matsumoto K, Tokuda Y. Effects of the use of differen-
tial diagnosis checklist and general de- biasing checklist on diagnos-
tic performance in comparison to intuitive diagnosis. Med Teach.
2013;35:e1218-e1229.

72. Dhaliwal G. Premature closure? Not so fast. BMJ Qual Saf.
2017;26:87-89.

73. Marewski JN, Gaissmaier W, Gigerenzer G. We favor formal models
of heuristics rather than lists of loose dichotomies: a reply to Evans
and Over. Cogn Process. 2010;11:177-179.

74. Boreham NC. The dangerous practice of thinking. Med Educ.
1994;28(3):172-179.

75. Dreyfus HL. Skilled Coping as Higher Intelligibility in Heidegger’s’ Being
and Time’. London: Uitgeverij Van Gorcum; 2008.

How to cite this article: Monteiro S, Sherbino J, Sibbald M,
Norman G. Critical thinking, biases and dual processing: The
enduring myth of generalisable skills. Med Educ. 2020;54:66–
73. https ://doi.org/10.1111/medu.13872

13652923, 2020, 1, D
ow

nloaded from
https://onlinelibrary.w

iley.com
/doi/10.1111/m

edu.13872, W
iley O

nline L
ibrary on [03/07/2023]. See the T

erm
s and C

onditions (https://onlinelibrary.w
iley.com

/term
s-and-conditions) on W

iley O
nline L

ibrary for rules of use; O
A

articles are governed by the applicable C
reative C

om
m

ons L
icense

ORIGINAL RESEARCH

Nurse Practitioners’ Versus Physicians’
Diagnostic Reasoning Style and Use
of Maxims: A Comparative Study
Alison M. Pirret, PhD, NP

ABSTRACT
The study used an intuitive/analytic reasoning instrument and maxims questionnaire to compare 1) the
diagnostic reasoning style of 30 nurse practitioners (NPs) and 16 resident doctors and 2) its influence on their
diagnostic reasoning abilities of a complex case. The results showed NPs incorporated more system I
(intuitive) processes when compared with residents; however, both groups identified with certain maxims.
Diagnostic reasoning style was not related to participants’ diagnostic reasoning abilities, indicating they
triggered system II (analytic) processes when required. Although system I processes are essential, clinicians
need to be aware of the value and pitfalls associated with them.

Keywords: diagnostic reasoning style, maxims, nurse practitioner
� 2016 Elsevier Inc. All rights reserved.

he first New Zealand (NZ) nurse practi-
tioner (NP) was registered in 2002, with

Tthe 100th NP being registered in early

2012.1 In NZ, the title NP is legally protected and
can only be used by nurses with a master’s degree
who have passed rigorous assessment processes.2

Legislation in NZ allows NPs to practice
independently without physician supervision.2

Research shows NPs and resident doctors have
similar patient outcomes and diagnostic reasoning
abilities.2 Diagnostic reasoning requires clinicians to
collect relevant assessment data, retrieve memorized
knowledge, and integrate data in the working
memory. Because of limited capacity in the working
memory, this process can overstretch the cognitive
resources and create congitive overload, which risks
diagnostic error.3 Singh et al4 suggest 12 million
United States adults are affected by diagnostic error
every year. Because diagnostic reasoning style
impacts on diagnostic accuracy,5 it is worthy of
further exploration.

This study compared NPs’ and resident doctors’
diagnostic reasoning style and use of maxims to guide
their diagnostic reasoning. It answered 3 questions:
1) How does NP diagnostic reasoning style compare
with that of residents? 2) How do maxims used
by NPs in their diagnostic reasoning compare with

www.npjournal.org

those used by residents? and 3) Are NPs’ and resi-
dents’ diagnostic reasoning ability scores described
by Pirret et al2 influenced by their diagnostic
reasoning style and use of maxims in everyday
practice? The first 2 questions were based on the
assumption that as NZ NPs were expected to
have more years of experience than residents, they
were more likely to use system I processes in their
diagnostic reasoning.

BACKGROUND
Dual process theory identifies diagnostic reasoning
uses system I (intuitive) and system II (analytic)
processes; the degree to which each is used is
dependent on the clinical situation.5,6 System I
processes are fast and are used automatically when
clinicians are involved in familiar case presentations.
They use cognitive shortcuts or rules of thumb,
commonly termed heuristics, to reduce the cognitive
load and simplify the diagnostic reasoning process.5,7

These heuristics based on experience, patient
characteristics, and the context in which the
patient presents enable clinicians to reach a
diagnosis without proceeding with the time-
expensive process of exploring unlikely diagnoses.5

If the patient presentation is not initially recog-
nized, time does not permit, or the clinician is

The Journal for Nurse Practitioners – JNP 381

uncertain, the slower, logical, and deliberate system II
processes are used.7 Both system I and II processes
need to fail for diagnostic error to occur, system I
for making an error and system II for not detecting
and correcting it.8 An example of this is when
contextual factors, such as clinician overconfidence
or fatigue, are combined with a complex case
presentation with features that reflect multiple
diagnoses,9 such as those described in Box 1.
Contextual factors triggered by system I processes
create the error, whereas failure to take a complete
patient history or perform the appropriate physical
examination leads to system II failure. Failure of
both the system I and II process leads to diagnostic
error and an inappropriate management plan.

Different types of diagnostic reasoning use either
system I or II processes. Intuition and pattern
recognition use system I processes, whereas the
hypothetico-deductive model and clinical guidelines
use system II processes. Intuition is based on past
experiences, is generated without mental effort,
and is commonly described as a gut feeling.6 Pattern
recognition is when the clinician makes a diagnosis
based on a few pieces of critical information
gained from the clinical context and memorized
signs and symptoms5; this process allows an almost
instantaneous realization that the patient’s
presentation resembles memories of previous cases.

Maxims also serve as a heuristic and are thought
to aid in memory. They are succinct sayings devel-
oped by experienced clinicians.10 Each maxim is
case specific and not suitable to be applied to all
patients.10

Box 1. Example of

A 61-year-old noneEnglish-speaking woman presents w

posterior chest pain, productive cough with purulent sp

pain. She is accompanied by her daughter, who is able

3 days ago and was prescribed an antibiotic but feels s

managed type II diabetes, hypertension, hyperlipidemi

takes acetaminophen and a nonsteroidal anti-inflamma

weight loss of 8 kg over the past 6 weeks. She is a sm

She is hypertensive with a slightly raised respiratory ra

has dullness over the right base on percussion. Right-s

coughing. Abdominal palpation reveals right upper qu

The Journal for Nurse Practitioners – JNP382

The hypothetic-deductive model is an approach
predominantly used by novice clinicians. It uses
inductive and deductive reasoning to guide clinicians
to the most correct diagnoses.7 Clinical guidelines
are used to interpret and treat certain conditions
and are thought to be useful in improving the
performance of novice clinicians.11

NP Diagnostic Reasoning Styles
Most research exploring NP diagnostic reasoning
style was published 10 to 20 years ago.12 These
studies identified NPs used system I and II processes
including intuition, pattern recognition, maxims,
and the hypothetic-deductive model.12,13 Intuition
was used to alert NPs to issues, which was then
followed by a search for more objective data to
support their concerns.13 The maxims NPs used
included “real disease declares itself, follow-up
everything, and common things occur commonly.”13

Medical Doctor Diagnostic Reasoning Styles
System I and II processes are incorporated into
medical doctor (MD) diagnostic reasoning, but
experience determines how they are used. When
using pattern recognition, novice doctors use
familiar and irrelevant factors to support diagnoses,
such as name, occupation, age, and similar situa-
tions.14 This is in contrast to experts who support
diagnoses with memorized signs and symptoms
learned from experience.15

Although MDs use the hypothetico-deductive
model, it is only used by experts when analyzing
complex or unfamiliar cases.16 Medicine is beginning

a Complex Case

ith a 1-week history of generalized malaise, fevers,

utum, increased shortness of breath, and abdominal

to translate. She visited her general practitioner

he is getting worse. She has a history of poorly

a, and osteoarthritis of her right knee for which she

tory drug. She is obese but has had an unintentional

oker with a 40epack year history.

te. She has right basal crackles on auscultation and

ided chest pain is present on inspiration and

adrant tenderness.

Volume 12, Issue 6, June 2016

to value intuitionwith suggestions thatMDs should be
trained to develop confidence in their gut feelings.11

METHODS
The study used a comparative research design.
This aspect of the analysis used an intuitive/analytic
reasoning instrument and a maxims questionnaire to
compare NPs’ and residents’ diagnostic reasoning
style and the maxims they used to guide their diag-
nostic reasoning.

Intuitive/Analytic Reasoning Instrument
The intuitive/analytic reasoning instrument was
initially designed to measure registered nurse (RN)
decision making. The instrument was validated using
1,460 RNs from multiple specialties and countries.17

The instrument uses a 5-point Likert-type scale to
measure 56 items, 14 items for each decision-making
stage including data collection, data processing,
problem identification, and action planning. Of the
56 items, half measure analytic processes, and half
measure intuitive processes. Analysis showed there
were positive correlations between the items
measuring analytic (P ¼ .01-P � .001) and intuitive
(P � .001) processes. The total summed scores gained
from the recoded 56 items determine 4 types of
decision-making styles. Scores < 160 indicate an
analytic style, scores � 160 to � 170 indicate an
analytic-intuitive or intuitive-analytic style, and
scores > 170 indicate an intuitive style.17

Because the instrument was designed to measure
RNs’ decision making, for this analysis, the
instrument’s wording was reviewed and altered to
reflect both NP and resident diagnostic reasoning
language. The wording changes did not alter the
instrument’s intent, and the instrument’s author
approved the changes. Thus, for this analysis, the
instrument contained items thatmeasured participants’
use of system I or II processes in their daily practice at
each stage of the diagnostic reasoning process (ie, data
collection, identifying diagnoses and problems, plan-
ning care, and implementing the action plan).

Maxim Questionnaire
The maxims questionnaire used a 5-point Likert-type
scale to explore the use of 13 maxims in participants’
daily practice. The questionnaire included maxims

www.npjournal.org

identified in the literature review and identified by
Bernstein.18

Participants
Because this analysis was part of a wider study that
used the qualitative think aloud technique,2 statistical
advice determined 30 NPs and 30 residents would
keep the sample size manageable and show group
differences if they were present. Purposeful sampling
recruited 30 NPs and 16 residents working in NZ.
This left the study underpowered and at risk of a type
II error, meaning there was a risk of the research
identifying no statistically significant difference
between the 2 groups when there was one. Failure
to recruit the desired number of residents may have
been related to medical resistance to introducing
NPs at the time.19

Inclusion criteria required recruited residents to
have more than 2 years of experience after their
graduation. Residents were used as the comparative
group because they often manage complex cases
and seek guidance from medical specialists as
required. Because NPs practice independently but
collaborate with medical specialists when required,
they use similar referral/collaboration lines to resi-
dents; hence, they may have similar diagnostic
reasoning. Because the analysis was part of a larger
study that also assessed participants’ diagnostic
reasoning abilities of a complex case using a case
scenario, participants unlikely to be exposed to the
case were excluded. This included those working
in child and mental health and specialist areas,
such as ophthalmology and organ transplant. No
randomization was performed because only 59 NPs
at the time of recruitment were eligible for the
study. Being able to recruit over 51% of eligible
NPs meant the sample was likely representative
of NPs working in general adult areas of practice.

Ethical Considerations
The Massey University Human Ethics Committee
approved the study. All participants provided writ-
ten consent.

Data Collection/Analysis
Data were collected between February 2011 and
March 2012 (inclusive). Each participant accessed

The Journal for Nurse Practitioners – JNP 383

the instrument and questionnaire via a Survey-
Monkey (www.surveymonkey.com) web link at
a computer and time convenient to them.

Collected data were imported from Survey-
Monkey into the Statistical SPSS version 1920 within
the instrument enabled calculation of each participant’s
total score. These scores indicated diagnostic
reasoning style.

The data pertaining to maxims used frequently
by NPs were transformed to determine maxims
participants mostly identified with in their daily
practice. Computing of participants’ data enabled
these maxims to be calculated into a single score
variable and group comparisons made.

The diagnostic reasoning style and maxim scores
were then correlated to participants’ previously
reported diagnostic reasoning ability scores related to
a complex case.2 This assessed the effect participants’
everyday diagnostic reasoning style and maxims had
on their diagnostic reasoning abilities. The alpha level
for statistical significance was set at 0.05.

Validity and Reliability
An expert panel consisting of a professor of general
practice, an associate professor of rheumatology,
and an NP reviewed the instrument’s wording to
ensure the wording reflected diagnostic reasoning
language. The instrument’s reliability was assessed
using Cronbach alpha (Cronbach a ¼ 0.89). The
maxims were assessed by the expert panel as being
suitable to include in the questionnaire. Content
validity was improved using purposeful sampling
to ensure the NP and resident groups reflected similar
specialty areas. Reliability was addressed by ensuring
the methods used to assess participants were clearly
outlined. The questionnaires used in the research are
available online13 to allow the study to be reproduced
by others researching similar populations.

RESULTS
The data of 30 NPs and 16 residents were analyzed.
They worked in multiple specialty areas including
general practice, gerontology, cardiology, emer-
gency, and palliative and respiratory care. The largest
numbers of participants in both the NP and resident
groups worked in general practice, and the smallest
numbers worked in palliative care.

The Journal for Nurse Practitioners – JNP384

PARTICIPANT DEMOGRAPHICS
NPs
Twenty-seven female and 3 male NPs participated in
the study. They had a mean (M) of 2.2 years (standard
deviation [SD] ¼ 1.6) of NZ NP experience.
Twenty-seven (90.00%) NPs had prescribing au-
thority, with 21 (70.00%) having less than 2 years of
prescribing experience.

The NPs’ median RN experience was 29.50 years
(interquartile range [IQR]¼ 7.25, range¼ 5.00-40.00).
The NPs had worked within their specialty area as
an RN for a median of 15.50 years (IQR ¼ 9.50,
range ¼ 5.00-39.00) before registering as an NP.
Most NPs (n ¼ 27, 90.00%) completed their master’s
degree in NZ.

Residents
Nine female (56.25%) and 7 male (43.75%) residents
participated in the study. They had an average of
6.30 years (SD ¼ 2.43) of resident experience.
During the data collection period, they practiced
in a variety of specialty areas; however, their specialty
training areas included general practice (n ¼ 5,
31.25%), cardiology (n ¼ 3, 18.75%), respiratory
(n ¼ 1, 6.25%), emergency care (n ¼ 2, 12.50%),
gerontology (n ¼ 1, 6.25%), and general medicine
(n ¼ 4, 25.00%). One resident was previously a
medical specialist but was retraining in another
specialty, and 4 (25.00%) had previously worked in
other residency programs.

Most residents (n ¼ 13, 81.25%) had completed
the first part of their current specialty training. The
residents had completed a median of 1.50 years
(IQR ¼ 1.00, range ¼ 0.00-5.00) before part 1
examinations and a mean of 1.80 years (SD ¼ 1.53)
after part 1 examinations.

Diagnostic Reasoning Style
As a group, NPs used an analytic-intuitive diagnostic
reasoning style (M ¼ 160.8, SD ¼ 5.91), whereas
residents favored an analytic style (M ¼ 157.2,
SD ¼ 6.61). The 2-tailed independent t test revealed
a trend toward NPs incorporating more system I
processes in their diagnostic reasoning (t44 ¼ 1.91,
P ¼ .06). Figure 1 shows the varying types of
diagnostic reasoning styles used by participants. The
individual groups’ intuitive/analytic reasoning scores

Volume 12, Issue 6, June 2016

Figure 1. Participants’ diagnostic reasoning style.

Table 1. Participants’ Intuitive/Analytic Reasoning Scores

NPs’ Intuitive/Analytic Reasoning Scores Residents’ Intuitive/Analytic Reasoning Scores

Percentile RanksScore Frequency, n (%) z Score Score Frequency, n (%) z Score

150 1 (3.3) �1.83 145 1 (6.3) �1.85 �1.09

152 1 (3.3) �1.49 149 1 (6.3) �1.24 �0.79

153 1 (3.3) �1.32 150 1 (6.3) �1.09 �0.63

154 1 (3.3) �1.16 151 1 (6.3) �0.94 �0.79

155 3 (10.0) �0.99 154 1 (6.3) �0.48 �0.33

156 2 (6.7) �0.82 155 2 (12.5) �0.33 �0.18

157 1 (3.3) �0.65 157 1 (6.3) �0.03 �0.03

158 3 (10.0) �0.48 158 2 (12.5) 0.12 0.12

159 1 (3.3) �0.31 159 1 (6.3) 0.27 0.27

160 1 (3.3) �0.14 162 2 (12.5) 0.73 0.43

162 2 (6.7) 0.20 165 1 (6.3) 1.18 0.73

163 2 (6.7) 0.37 166 1 (6.3) 1.33 0.88

164 2 (6.7) 0.54 169 1 (6.3) 1.79 1.03

165 1 (3.3) 0.70 1.18

166 1 (3.3) 0.87 1.33

167 1 (3.3) 1.04 1.49

168 5 (16.7) 1.21 1.64

173 1 (3.3) 2.06 2.39

www.npjournal.org The Journal for Nurse Practitioners – JNP 385

are outlined in Table 1. The scores of NPs reflecting
an analytic or mostly analytic style (scores � 164)
revealed an analytic diagnostic reasoning style was
dominant in 70.0% (n ¼ 21) of NPs.

Because of the risk of a type II error when per-
forming the independent t test, percentile ranks
were applied to the data to show how far the NPs’
individual intuitive/analytic scores were from the
residents’ mean score (Table 1). Percentile ranks
showed 32.0% of NPs have a more analytic style
of diagnostic reasoning when compared with the
resident mean. This implies 68.0% of NPs have
a less analytic style when compared with the
resident mean.

The Mann-Whitney U test showed sex was
not related to diagnostic reasoning style in the NP
(U ¼ 40.00, z ¼ �0.04, P ¼ .97) or resident
(t14 ¼ �0.27, P ¼ .79) groups. When diagnostic
reasoning style was compared with participants’
previously reported diagnostic reasoning ability
scores,2 the Spearman rho coefficient found that

Table 2. Differences Between Maxims Used by Participants

Maxim NP Mean R

When facing competing diagnoses favor the

simplest one

19.12

If you don’t know what to do, don’t do anything 19.88

Consider multiple separate diseases of a patient

when the result of the history and physical

examination are atypical of any one condition

26.97

Common things occur commonly 19.45

All bleeding eventually stops 25.70

All drugs work by poisoning some aspect of

normal physiology

24.20

Don’t order a test unless you know what you

will do with the results

22.70

Real disease declares itself 21.15

Treat the patient not the x-ray 24.48

Never worry alone, get a consultation 24.90

Never give two diagnoses when you can find

one that explains everything

20.73

If what you are doing is working, keep doing it.

If what you are doing is not working, stop doing it

26.33

Follow up everything 25.15

a Indicates statistical significance.

The Journal for Nurse Practitioners – JNP386

diagnostic reasoning style was not related to the
accuracy of diagnostic reasoning in either the
NP (rs ¼ �0.14, n ¼ 30, P ¼ .46) or resident
(rs ¼ 0.03, n ¼ 16, P ¼ .90) groups.

Maxims Used to Guide Diagnostic Reasoning
NPs and residents were questioned on their use
of 13 maxims in their daily practice. The Mann-
Whitney U test was used to identify differences
between the 2 groups in how they identified with
each maxim (Table 2). The maxims NPs mostly
identified with included the following:

1. “Never worry alone; get a consultation.”
2. “If what you are doing is working, keep

doing it. If what you are doing is not
working, stop doing it.”

3. “Follow up everything.”
4. “Consider multiple separate diseases of

a patient when the result of the history
and physical examination are atypical of
any 1 condition.”

ank Resident Mean Rank Significance

31.72 U ¼ 108.5, z ¼ �3.16, P ¼ .002a

30.28 U ¼ 131.5, z ¼ �2.77, P ¼ .006a

17.00 U ¼ 136.0, z ¼ �2.55, P ¼ .01a

31.09 U ¼ 118.5, z ¼ �3.17, P ¼ .002a

19.38 U ¼ 174.0, z ¼ �1.58, P ¼ .12

22.19 U ¼ 219.0, z ¼ �0.50, P ¼ .62

25.00 U ¼ 216.0, z ¼ �0.64, P ¼ .53

27.91 U ¼ 169.5, z ¼ �1.74, P ¼ .08

21.66 U ¼ 210.5, z ¼ �0.76, P ¼ .45

20.88 U ¼ 198.0, z ¼ �1.26, P ¼ .20

28.69 U ¼ 157.0, z ¼ �1.98, P ¼ .05

18.19 U ¼ 155.0, z ¼ �2.17, P ¼ .03a

20.41 U ¼ 190.5, z ¼ �1.27, P ¼ .20

Volume 12, Issue 6, June 2016

5. “Treat the patient not the x-ray.”
6. “Do not order a test unless you know what

you will do with the results.”
7. “Common things occur commonly.”
The Fisher exact test analyzed how residents

differed from NPs in the way they identified
with these 7 maxims (Table 3). The residents
only identified with 6 of these maxims, with only
50.0% (n ¼ 8) of residents identifying with the
maxim “consider multiple separate diseases of a
patient when the result of the history and physical
examination are atypical of any one condition.”
Residents were more likely to identify with the
maxim “real disease declares itself,” with 56.3%
(n ¼ 9) of residents identifying with this maxim,
compared with 30.0% (n ¼ 9) of NPs (Fisher exact
test, P ¼ .12). The 2-tailed independent t test showed
no difference in how frequently NPs and residents
identified with these maxims in their daily practice
(t44 ¼ �0.89, P ¼ .38).

Spearman rho indicated no relationship between
participants’ identification with maxims and diag-
nostic reasoning style in either the NP (rs ¼ 0.10,
n ¼ 30, P ¼ .61) or resident (rs ¼ 0.38, n ¼ 16,
P ¼ .15) groups. Identifying with these maxims
was not related to participants’ previously reported
diagnostic reasoning ability scores2 in either the
NP (rs ¼ �0.17, n ¼ 30, P ¼ .37) or resident
(rs ¼ �0.08, n ¼ 16, P ¼ .77) groups.

Table 3. Maxims Nurse Practitioners Identified With Most

Maxim

Never worry alone, get a consultation

If what you are doing is working, keep doing it.

If what you are doing is not working, stop doing it

Follow up everything

Consider multiple separate diseases of a patient when the resu

the history and physical examination are atypical of any one c

Treat the patient not the x-ray

Don’t order a test unless you know what you will do with the r

Common things occur commonly

a Indicates statistical significance.

www.npjournal.org

DISCUSSION
The analysis identified NPs, when compared with
residents, incorporated more system I processes in
their diagnostic reasoning; however, both NPs’ and
residents’ identification with frequently used maxims
was similar. Participants’ diagnostic reasoning style
and identification with maxims were not related to
their diagnostic reasoning ability scores reported in
Pirret et al2; although both groups identified with
certain maxims and therefore used heuristics, this
indicates they successfully triggered system II
processes to analyze the complex case. However,
Pirret et al2 identified more diagnostic reasoning
errors were made by participants completing the
case in the fastest times, suggesting some participants
likely used system I processes when system II
processes were required.

The system II processes favored by residents
when measuring their diagnostic reasoning style
may reflect their training system. System II processes
are developed through formal training15; thus,
residents participating in formal specialist training
programs are likely to reflect the diagnostic reasoning
style of their training system. Although all NPs
completed a master’s degree, their numerous years of
RN experience may mean they are less exposed to,
and influenced by, solely system II approaches. It
is now recognized that diagnostic reasoning requires
both system I and II processes.15,16

NP

Frequency

Resident

Frequency

Significancen (%) n (%)

28 (93.3) 14 (87.5) FET, P ¼ .60

27 (90.0) 13 (81.3) FET, P ¼ .41

27 (90.0) 12 (75.0) FET, P ¼ .22

lt of

ondition

25 (83.4) 8 (50.0) FET, P ¼ .04a

25 (83.3) 14 (87.6) FET, P ¼ 1.0

esults 24 (80.0) 16 (100.0) FET, P ¼ .08

21 (70.0) 16 (100.0) FET, P ¼ .02a

The Journal for Nurse Practitioners – JNP 387

System I processes are necessary to ensure clini-
cians manage their clinical workload,16 and, in
most cases, it provides the correct diagnoses.7

When used by experienced clinicians, system I
processes reduce the need for clinicians to ask
unnecessary questions and order unnecessary
diagnostic tests.9 It is when system I processes are used
inappropriately that diagnostic error occurs.7

Clinicians are often unaware of the effect heuris-
tics, biases, and contextual factors, such as patients’
social circumstances and clinicians’ overconfidence,
emotions, and fatigue, have on diagnostic accuracy.11

System I processes are strongly affected by contextual
factors,11 meaning the risk of diagnostic error
increases when heuristics, biases, and contextual
factors are combined.

More recently, researchers are proposing diag-
nostic errors may be less about heuristics and biases
and more about knowledge, experience, and clinical
expertise.3,8 Experts with a high level of specialty
knowledge and clinical expertise still make diagnostic
errors; however, they are better at recovering
from them. Error recovery requires expert
knowledge, and if that knowledge is not applied,
these errors can negatively impact on patients’
progress or outcome.21

Croskerry15 argues clinicians need to understand
the complexity of their diagnostic reasoning and how
heuristics and biases affect their individual diagnostic
reasoning accuracy. With an international focus on
reducing diagnostic error, it is now timely for
educators preparing NPs and residents to consider
including system I and II processes in training
curriculums. This will make new clinicians aware
of factors that contribute to diagnostic error and
strategies that reduce it.

LIMITATIONS
The analysis reported in this article has a number
of limitations. The instrument and questionnaire relied
on self-reporting and may reflect perceived rather than
actual diagnostic reasoning behaviors. Difficulty in
recruiting residents meant the resident group was small,
which makes it difficult to generalize the results to a
wider resident population. The study was inadequately
powered for the 2-tailed between-group independent
t test. Because this test identified a trend toward

The Journal for Nurse Practitioners – JNP388

NPs using more system I processes than residents,
percentage ranks were applied to the data to aid in
the interpretation of the results. The academic and
registration requirements for NPs in NZ mean the
results of this analysis may not reflect the diagnostic
reasoning styles and identification with maxims by
NPs in countries with differing academic and regis-
tration requirements.

Because little research is available on maxims
used by NPs and residents, the study did not use
a previously validated questionnaire; rather, it
focused on exploring if NPs and residents identified
with them when performing their diagnostic
reasoning roles.

CONCLUSION
The analysis showed NPs, when compared with
residents, trended toward including more system I
processes in their diagnostic reasoning; however,
both groups identified with maxims similarly.
Diagnostic reasoning style and identification with
maxims did not influence their diagnostic accuracy
of a complex case, suggesting both NPs and residents
triggered system II processes when required. With
an international focus on reducing diagnostic error,
the results of this study, highlighting that NPs and
residents use system I and II processes, provide an
opportunity for NPs and those involved in NP
training to reflect on NPs’ diagnostic reasoning
styles and how it contributes to diagnostic error
and error recovery.

References

1. Gagan MJ, Boyd M, Wysocki K, Williams DJ. The first decade of nurse

practitioners in New Zealand: a survey of evolving practice. J Am Assoc

Nurse Pract. 2014;26(11):612-619.

2. Pirret AM, Neville SJ, La Grow SJ. Nurse practitioner versus doctors

diagnostic reasoning in a complex case presentation to an acute tertiary

hospital: a comparative study. Int J Nurs Stud. 2015;52(3):716-726.

3. McLaughlin K, Eva KW, Norman GR. Reexamining our bias against heuristics.

Adv Health Sci Educ. 2014;19(3):457-464.

4. Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in

outpatient care: estimations from three large observational studies involving

US adult populations. BMJ Qual Saf. 2014;23(9):727-731.

5. Pelaccia T, Tardif J, Triby E, Charlin B. An analysis of clinical reasoning

through a recent and comprehensive approach: the dual-process theory.

Med Educ Online. 2011;16(5890).

6. Stolper E, Van De Wiel M, Van Royen P, Van Bokhoven M, Van Der Weijden T,

Dinant GJ. Gut feelings as a third track in general practitioners’ diagnostic

reasoning. J Gen Intern Med. 2011;26(2):197-203.

7. Elia F, Aprà F, Verhovez A, Crupi V. “First, know thyself”: cognition and

error in medicine. Acta Diabetol. 2015 May 5 [Epub ahead of print].

8. Sherbino J, Norman GR. Reframing diagnostic error: maybe it’s content, and

not process, that leads to error. Acad Emerg Med. 2014;21(8):931-933.

9. Norman GR, Eva KW. Diagnostic error and clinical reasoning. Med Educ.

2010;44(1):94-100.

Volume 12, Issue 6, June 2016

10. Levine D, Bleakley A. Maximising medicine through aphorism. Med Educ.

2012;46(2):153-162.

11. Lucchiari C, Pravettoni G. Cognitive balanced model: a conceptual scheme

of diagnostic decision making. J Eval Clin Pract. 2012;18(1):82-88.

12. Durham CO, Fowler T, Kennedy S. Teaching dual-process diagnostic

reasoning to doctor of nursing practice students: problem-based learning

and the illness script. J Nurs Educ. 2014;53(11):646-650.

13. Pirret AM. Nurse practitioner diagnostic reasoning. Massey University. http://

mro.massey.ac.nz/handle/10179/4929. 2013. Accessed August 31, 2014.

14. Young ME, Brooks LR, Norman GR. The influence of familiar non-diagnostic

information on the diagnostic decisions of novices. Med Educ. 2011;45(4):

407-414.

15. Croskerry P. A universal model of diagnostic reasoning. Acad Med. 2009;

84(8):1022-1028.

16. Elstein AS. Thinking about diagnostic thinking: a 30-year perspective. Adv

Health Sci Educ Theory Pract. 2009;14(suppl 1):7-18.

17. Lauri S, Salantera S. Developing an instrument tomeasure and describe clinical

decision making in different nursing fields. J Prof Nurs. 2002;18(2):93-100.

18. Bernstein M. Medical Maxims, Pearls and Principles: Do You Think Your

doctor Is Using Any of These? If You Were a Doctor Would You? Los Angeles,

CA; 2009. http://bioethicsdiscussion.blogspot.co.nz/2009/08/medical-maxims

-pearls-and-principles-do.html. Accessed July 31, 2014.

19. Gorman D. The nurse practitioner provides a substantive opportunity for task

substitution in primary care. J Prim Health Care. 2009;1(2):142-143.

www.npjournal.org

20. IBM Corp. IBN SPSS statistics for Windows. Version 19.0. Armonk, NY: IBM

Corp; 2010.

21. Patel VL, Cohen T, Murarka T, et al. Recovery at the edge of error: debunking

the myth of the infallible expert. J Biomed Inform. 2011;44(3):413-424.

Alison M. Pirret, PhD, NP, is a senior lecturer at the school of
nursing at the College of Health, Massey University and a nurse
practitioner in the critical care complex at Middlemore Hospital in
Auckland, New Zealand. She can be reached at A.M.Pirret@
massey.ac.nz. In compliance with national ethical guidelines,
the author reports no relationships with business or industry
that would pose a conflict of interest.

1555-4155/16/$ see front matter

© 2016 Elsevier Inc. All rights reserved.

http://dx.doi.org/10.1016/j.nurpra.2016.02.006

The Journal for Nurse Practitioners – JNP 389

Reproduced with permission of the copyright owner. Further reproduction prohibited without
permission.

  • Nurse Practitioners’ Versus Physicians’ Diagnostic Reasoning Style and Use of Maxims: A Comparative Study
    • Background
      • NP Diagnostic Reasoning Styles
      • Medical Doctor Diagnostic Reasoning Styles
    • Methods
      • Intuitive/Analytic Reasoning Instrument
      • Maxim Questionnaire
      • Participants
      • Ethical Considerations
      • Data Collection/Analysis
      • Validity and Reliability
    • Results
    • Participant Demographics
      • NPs
      • Residents
      • Diagnostic Reasoning Style
      • Maxims Used to Guide Diagnostic Reasoning
    • Discussion
    • Limitations
    • Conclusion
    • References

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94
http://www.biomedcentral.com/1472-6947/12/94

RESEARCH ARTICLE Open Access

Dual processing model of medical
decision-making
Benjamin Djulbegovic1,2,3,7*, Iztok Hozo4, Jason Beckstead5, Athanasios Tsalatsanis1,2 and Stephen G Pauker6

Abstract

Background: Dual processing theory of human cognition postulates that reasoning and decision-making can be
described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative
(system II) processing system. To date no formal descriptive model of medical decision-making based on dual
processing theory has been developed. Here we postulate such a model and apply it to a common clinical
situation: whether treatment should be administered to the patient who may or may not have a disease.

Methods: We developed a mathematical model in which we linked a recently proposed descriptive psychological
model of cognition with the threshold model of medical decision-making and show how this approach can be
used to better understand decision-making at the bedside and explain the widespread variation in treatments
observed in clinical practice.

Results: We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to
the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of
benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s
threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the
overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where
empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through
recent experience: the threshold will increase relative to the normative threshold value derived via system II using
expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment
that is also documented in the current medical practice.

Conclusions: We have developed the first dual processing model of medical decision-making that has potential to
enrich the current medical decision-making field, which is still to the large extent dominated by expected utility
theory. The model also provides a platform for reconciling two groups of competing dual processing theories
(parallel competitive with default-interventionalist theories).

Background
Dual processing theory is currently widely accepted as a
dominant explanation of cognitive processes that charac-
terizes human decision-making [1-9]. It assumes that
cognitive processes are governed by so called system I
(which is intuitive, automatic, fast, narrative, experiential
and affect-based) and system II (which is analytical, slow,
verbal, deliberative and logical) [1-10]. The vast majority

* Correspondence: [email protected]
1Center for Evidence-based Medicine and Health Outcomes Research,
Tampa, FL, USA
2Department of Internal Medicine, Division of Evidence-based Medicine and
Health Outcomes Research University of South Florida, Tampa, FL, USA
Full list of author information is available at the end of the article

© 2012 Djulbegovic et al.; licensee BioMed Ce
Creative Commons Attribution License (http:/
distribution, and reproduction in any medium

of existing models of decision-making including expected-
utility theory, prospect theory, and their variants assume a
single system of human thought [11]. Recently, formal
models for integrating system I with system II models
have been developed [3,11]. One such attractive
model-Dual System Model (DSM)- has been devel-
oped by Mukherjee [11]. Here, we extend Mukherjee’s
DSM model to medical field (DSM-M) by linking it
to the threshold concept of decision-making [12-15].
We also take into account decision regret, as an ex-
emplar of affect or emotion that is involved in system
I decision-making [2], and which is of particular rele-
vance to medical decision-making [16-19]. Regret was
also selected for use in our model because any

ntral Ltd. This is an Open Access article distributed under the terms of the
/creativecommons.org/licenses/by/2.0), which permits unrestricted use,
, provided the original work is properly cited.

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 2 of 13
http://www.biomedcentral.com/1472-6947/12/94

“theory of choice that completely ignores feeling such
as the pain of losses and the regret of mistakes is not
only descriptively unrealistic but also might lead to
prescriptions that do not maximize the utility of out-
comes as they are actually experienced” [1,20].
As more than 30% of medical interventions are cur-

rently not appropriately applied, mostly as over – or-
undertreatment [21-23], we illustrate how the DSM-M
model may be used to explain the practice patterns seen
in the current medical practice. Our DSM-M model is
primarily an attempt to describe how medical decisions
are made. As a descriptive model its validation will re-
quire comparing its outputs to actual choices made by
patients and clinicians and their verbalized reactions to
our model. We conclude the paper by providing some
testable empirical predictions.

Methods
A dual system model
Building on the previous empirical research, which has
convincingly showed that human cognition is deter-
mined by both system I and system II processes
[1,2,5,24,25]. Mukherjee recently developed a formal
mathematical model, which assumes parallel functioning
by both systems, while the final decision is a weighted
combination of the valuations from both systems based
on the value maximization paradigm (Figure 1) [11].
(NB. In this paper we employ terms system I and system
II as popularized by Kahneman [1,2] although some
authors prefer to talk about type 1 and 2 processing as it
is almost certain that human cognition is not organized
in distinctly separated physical systems [5,26,27]).
Mukherjee’s dual system model (DSM) assumes that

evaluation of risky choice (C) is formed by the combined
input of system I and system II into a single value and
can be formulated as follows:

E Cð Þ ¼ γVI Cð Þ þ 1� γð ÞVII Cð Þ
¼ γ

1
n

X
i

VI xið Þ þ 1� γð Þk
X
i

piVII xið Þ ð1Þ

Where C represents a decision-making situation
(“choice”), n – number of outcomes, pi – probability of
the ith outcome, xi, of the selected choice. VI represents

Decision/choice
underuncertainty

(C)

Valuation by intuitiv
affective cognitive s

Valuation by delibera
analytical/logical cog
(System II); VII (C)

(System I); VI (C)

Figure 1 Model of decision-making using dual processing cognitive p

valuation of decision under autonomous, intuitive, sys-
tem I-based mode of decision-making and VII, which
can be a utility function, represents valuation under a
deliberative, rule-based, system II mode of decision-
making. k-is a scaling constant, and γ [0 to 1] is the
weight given to system I and can be interpreted as the
relative extent of involvement of system I in the
decision-making process [11]. System II is not split into
two subsystems advocated by some [5], but is assumed
to adhere to the rationality criteria of expected utility
theory (EUT) as also advocated by modern decision sci-
ence [11,28]. γ is assumed to be influenced by a number
of processes that determine system I functioning.
Mukherjee emphasized the following factors as the im-
portant determinants of system I functioning [11]: indi-
vidual decision-making and thinking predispositions
[ranging from expected utility theory (EUT) “maximi-
zers” to system I driven “satisficing” with no regard to
probabilities but with editing or selection of outcomes of
interest] [29], affective nature of outcomes (the higher
the affective nature of outcomes, the higher is γ) and
framing and construing the decision-making task (deci-
sions for the self will likely have higher γ, as well as deci-
sion problems that are contextualized and those
requiring immediate resolution or are made under time
pressure; the last four describe circumstances character-
istic of medical decision-making). Easily available infor-
mation, our previous experience, the way in which
information is processed (verbatim vs. getting the “gist”
of it) [30] as well as memory limitations [31] are also
expected to affect γ. γ is, therefore, expected to be
higher when information about probabilities and out-
comes are ambiguous or not readily available, or when a
very severe negative prior outcome is recalled [2,32,33].
On the other hand, when such data are available their
joint evaluation by system II will reduce γ [11]. In gen-
eral, the factors that define the process of system I can
be classified under 4 major categories: a) affect, b) evolu-
tionary hard-wired processes, responsible for automatic
responses to potential danger in such a way that system
I typically gives higher weight to potentially false posi-
tives than to false negatives (i.e. humans are cognitively
more ready to wrongly accept the signal of potential
harms than one that carries the potential of benefit),

e, experiential,
ystem

tive, reflective
nitive system

Final choice
VC=VI(C)+VII(C)

rocesses (after Mukherjee [11]).

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 3 of 13
http://www.biomedcentral.com/1472-6947/12/94

(c) over-learned processes from system II that have been
relegated to system I (such as the effect of intensive
training resulting in the use of heuristics, or “rules of
thumb” or practice guidelines as one of the effort-saving
cognitive strategies. NB although guidelines may be the
products of analytic system II processes their applica-
tions tends to be a system I process.), and (d) the effects
of tacit learning [5].
Mukherjee’s DSM model draws upon empirical evidence

demonstrating that decision-makers in an affect-rich con-
text are generally sensitive only to the presence or absence
of stimuli, while in affect-poor contexts they rely on sys-
tem II to assess the magnitude of stimuli (and probabil-
ities) [24]. Hence, the salient feature of the model is that
that system I recognizes outcomes only as being possible
or, not. Every outcome that remains under consideration
gets equal weight in system I. On the other hand, system
II recognizes probabilities linearly without distortions,
according to the expected utility paradigm.
As a result, dual valuation processing often generates

instances where subjective valuations are greater at
lower stimulus magnitudes (i.e. when decision-making
relies on feeling, or evolutionary hard-wired processes
such as when the signal may present danger) while ra-
tional calculation produces greater value at high magni-
tudes [11]. DSM is capable of explaining a number of
the phenomena that characterize human decision-
making such as a) violation of nontransparent stochastic
dominance, b) fourfold pattern of risk attitude, c) ambi-
guity aversion, d) common consequences effect, e) com-
mon ratio effect, f ) isolation effect, g) and coalescing
and event-splitting effect [11].
Under the realistic assumption that outcomes are posi-

tive (i.e., utilities >0, which is particularly applicable to
medical setting) and power value functions, VI xð Þ ¼ xmI ,

System I

System II

System II

System I

Figure 2 Dual processing model of decision-making as applied to a c
(D+) or not. The patient may or may not have a disease (probability p). Re
competing treatment alternative may include Rx or NoRx). Rg- regret.

and VII xð Þ ¼ x for system I and system II, respectively,
DSM can be re-written as:

V Cð Þ ¼ γ
1
n

X
i

xmI
i þ 1� γð Þk

X
i

pixi ð2Þ

where 0 <mI ≤ 1 Note that xmI
i satisfies risk aversion for

gains and risk seeking for losses and that the term for
system II pixi is linear without risk distortions.
As noted by Mukherjee [11], the estimation of the

parameters in Equation 2) is a measurement exercise,
which needs to be evaluated in the future empirical re-
search. Consequently, the functions VII(x) and VI(x)
could be changed, depending on the decision-making
setting and decision-maker’s goals. Similarly, parameter
m may not be the same for all outcomes.

Modification of DSM for medical decision-making
We will consider a typical situation in clinical decision-
making where a doctor has to choose treatment (Rx) vs.
no treatment (NoRx) for disease (D) which is present
with the probability p. [Note than NoRx represents a
competing treatment alternative and may include a dif-
ferent treatment (Rx2)] [12,34]. Each decision results in
outcomes that have a certain value, xi. The model is
shown in the Figure 2. As noted above, the system I
recognizes outcomes only as being possible (or not), and
is thus insensitive to exact probabilities. Every outcome
with non-zero probability gets equal weight in system I.
Hence, in a two-alternative choice, each probability is
equal to 0.5 under system I. System II recognizes prob-
abilities without distortions, as would be expected
according to EUT.
We posit that among the emotions that can influence

valuation of outcomes in system I processing, regret plays

linical dilemma whether to treat (Rx) the patient with disease
gret is assumed to operate at the level of system I only. (Note that

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 4 of 13
http://www.biomedcentral.com/1472-6947/12/94

an important role [1,2], while system II processes are domi-
nated by rational, analytical deliberations according to EUT
[11]. We can define regret (Rg) as the difference (loss) in
the utilities of the outcome of the action taken and that of
the action we should have taken, in retrospect [16-19,35]
but operating at the system I level only (see Figure 2).
Hence, we have the following value functions (see

Additional file 1: Appendix for detailed derivation):

VI Rx;Dþð Þ ¼ Rg Rx;Dþ½ � ¼ 0;
VI NoRx;Dþð Þ ¼ Rg NoRx;Dþ½ � ¼ xmI

3 � xmI
1 ;

VII Rx; Dþð Þ ¼ x1 ;
VII NoRx; Dþð Þ ¼ x3;

VI Rx;D�ð Þ ¼ Rg Rx;D�½ � ¼ xmI
2 � xmI

4
VI NoRx; D�ð Þ ¼ Rg NoRx; D�½ � ¼ 0;

VII Rx; D�ð Þ ¼ x2;
VII NoRx; D�ð Þ ¼ x4;

Overall valuation of decision to treat (Rx) is equal to:

V Rxð Þ ¼ γ

2
VA Rx;Dþð Þ þ VA Rx; D�ð Þð Þ

þ 1� γð ÞkðpVDðRx;DþÞ
þ 1� pð ÞVD Rx; D�ð ÞÞ

¼ γ

2
xmA
2 � xmA

4ð Þ þ 1� γð Þk px1 þ 1� pð Þx2½ �
And

V NoRxð Þ ¼ γ

2
VA NoRx;Dþð Þ þ VA NoRx; D�ð Þð Þ

þ 1� γð ÞkðpVDðNoRx;DþÞ
þ 1� pð ÞVD NoRx; D�ð ÞÞ

¼ γ

2
xmA
3 � xmA

1ð Þ þ 1� γð Þk px3 þ 1� pð Þx4½ �

The difference in the outcomes of treating and not treat-
ing patient with disease are equal to the net benefit of
treatment (B) [13,14,36]; the difference in outcomes of not
treating and treating those patients without disease is
defined as net harms (H) [13,14,36]. Note that benefits
and harms can be expressed in the various units (such as
survival, mortality, morbidity, costs, etc.) and can be for-
mulated both as utilities and disutilities [13,14,36]. As
explained above, we further assume that valuation of net
benefits and net harms by system I differs from system II.
Hence, under system II, we replace net benefit and net
harms using EUT definitions:BII ¼ x1 � x3 and net harms
HII ¼ x4 � x2 . Under system I, we define BI ¼ xmI

1 � xmI
3 ,

and HI ¼ xmI
4 � xmI

2 . Solving for p (the probability of

disease at which we are indifferent between Rx and
NoRx), we obtain: (Equation 3)

pt ¼ p ¼ 1� γð ÞkHII � γ
2 BI �HI½ �

1� γð Þk BII þ HII½ �
¼ 1

1þ BII
HII

� γ

2k 1� γð Þ
BI � HI

BII þ HII

¼ 1

1þ BII
HII

!
1þ γ

2 1� γð Þ
HI

HII

� �
1� BI

HI

� �� �

¼ pt EUTð Þð Þ 1þ γ

2 1� γð Þ
HI

HII

� �
1� BI

HI

� �� �
ð3Þ

This means that if the probability of disease is above pt
the decision-maker favors treatment; otherwise, a compet-
ing management alternative (such as “No Treatment”)
represents the optimal treatment strategy. Note that k can
be typically set at 1, as we do it here. Also note that the
first part of equation is equivalent to the threshold expres-
sion described in EUT framework [13,14,36]; the second
expression modifies system II’s EUT-based decision-mak-
ing process in such a way that if benefits are experienced
higher than harms, the threshold probability is always
lower than EUT threshold. However, if a decision-maker
experiences HI>BI, the threshold probability is always
higher than the EUT threshold (see below for discussion
in the context of medical example). Note that γ and the
ratio HI

HII
only contribute to the extent of magnitude the

dual threshold is above or below the classic EUT thresh-
old. That is, γ and the ratio HI

HII
do not change the quality

of relationship between dual threshold and EUT thresh-
old: whether dual threshold will be above or below the
EUT threshold depends only on a BI

HI
ratio.

It should be noted that the identical derivations can be
obtained by applying the concept of expected regret
(instead of EUT) [16-19,35]. Although it can be argued that
regret is a powerful emotion influencing all cognitive pro-
cesses (as so called, “cognitive emotion”) [37,38], and so it
may function at level of both system I and system II [39],
most authors recognize the affect value of regret [2,10].
Hence, we assumed that regret functions at system I level
[2]. Therefore, in our model we restrict the influence of re-
gret to system I. Incidentally, our Equation 3) can also be
derived from the general Mukherjee’s DSM model even if
regret is not specifically invoked [11].
Although Equation 3) implies exact calculations, it

should not be understood as one that provides precise
mathematical account of human decision-making. Rather,
it should be considered more as a semi-quantitative or
qualitative description of the way physicians may make
their decisions. First, this is because system I does not per-
form exact calculations, but rather relies on “gist” [30,31]

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 5 of 13
http://www.biomedcentral.com/1472-6947/12/94

for assessment of benefits and harms in more qualitative
manner. The mechanism depends on associations, emo-
tions (so called, “risk as feelings” estimates [10]), as well as
memory, and experience [2,5,8,31]. In this sense, the sec-
ond part of Equation 3) that relies on system I can be
understood as the qualitative modifier (“weight”), which,
depending on the system I’s estimates of benefits and
harms increases or decreases the first part of equation
(which is dependent on system’s II precise usage of evi-
dence for benefits and harms). Second, the threshold
probability itself should be considered as an “action
threshold”- at some point, a physician decides whether to
administer treatment or not. Typically, she contrasts the
estimated probability of disease against the threshold and
acts: if the probability of disease is above the “action
threshold”, the physician administers the treatment; if it is
below, she decides not to give treatment. So, one way to
interpret Equation 3) is to consider physician’s estimate of
“gist” of the action threshold: if in her estimation, overall
benefits of treatment outweigh harms, and she considers
that it is “likely” that the probability of disease is above the
threshold probability, then she would act and administer
treatment. If the physician assesses that it is “unlikely” that
the probability disease is above the “action threshold”,
then she would not prescribe the treatment.

The behavior of DSM-M model
The exact cognitive mechanisms that underlie dual system
processes are not fully elucidated. As discussed through-
out this paper, many factors affect dual processes reason-
ing leading to suggestions that these processes should be
grouped according to the prevailing mechanisms [27]. Fo-
cusing on each of these processes may lead to specific the-
oretical proposals. Our goal in this paper is to provide
overarching cognitive architecture encompassing general
features of the majority existing theoretical concepts, while
at the same time concentrating on specifics of medical de-
cision-making. In general, dual processing theories [27]
fall into two main groups [27,40] parallel competitive the-
ories and default-interventionalist theories. The parallel-
competitive theories assume that system I and II processes
proceed in parallel, each competing for control of the re-
sponse [27]. If there is a conflict, it is not clear which
mechanism is invoked to resolve the conflict [27]. On the
other hand, default-interventionist theories postulate that
system I generates a rapid and intuitive default response,
which may or may not be intervened upon by subsequent
slow and deliberative processed of system II [2,5,27]. This
can be further operationalized via several general mechan-
isms that have been proposed in the literature:

1) Mukherjee’s additive model as described above [11].
It can be categorized as a variant of parallel-
competitive theory as it assumes that system I and II

processes proceed in parallel, but does include
parameter γ, which can trigger greater or smaller
activation of system I. Mukherjee’s model, however,
does not explicitly model the choices in terms of
categorical decisions (i.e. accept vs. do not accept a
given hypothesis), which is a fundamental feature of
dual-processing models [27].

2) System I and system II operate on a continuum [41],
but in such a way that system I never sleeps [2]. A
final decision depends on the activation of both
systems I and II [2]. It has been estimated that about
40-50% of decisions are determined by habits (i.e. by
system I) [42]. This is also a variation of parallel-
competitive theory; it should be noted that latest
literature is moving away from this model [5,27].

3) The final decision appears to depend both on the
system I and system II in such a way that system I is
the first to suggest an answer and system II endorses
it [2]. In doing so, system II can exert the full control
over system I (such as when it relies on the EUT
modeling) or completely fail to oversee functioning
of system I (e.g., because of its ignorance or laziness)
[2]. Therefore, according to this model, decisions are
either made by system I (default) or system II (which
may or may not intervene). This is a default-
interventionalist model.

4) The variation of the model #3 is the so called “toggle
model”, which proposes that decision-maker
constantly uses cognitive processes that oscillate
between the two systems (toggle) [6,7,9]. This is a
variant of default-interventionalist model.

Note that γ is continuous in our model, but it can be
made categorical [0,1] if the “toggle” theory is considered
to be the correct one. In this case, a logical switch can be
introduced in the decision tree to allow toggling between
the two systems. Most importantly, by linking Mukherjee’s
additive model with the threshold model, we provide the
architecture for reconciling parallel competitive theories
with default-interventionalist theories. We do it by making
explicit that decisions are categorical (via threshold) at
certain degree of cognitive effort (modeled via γ) param-
eter [27]. That is, the key question is what processes deter-
mine acceptance or rejection of a particular (diagnostic)
hypothesis. Our model shows that this can occur if we
maintain parallel-competing architecture of Mukherjee’s
additive model but assume a switch, yes or no answer,
whether to accept or reject a given hypothesis (at the
threshold). It is evaluation of the (diagnostic) event with
respect to the threshold that serves as the final output of
our decision-making and reasoning processes. As our
model shows, this depends on assumption of parallel
working of both system I and system II, and the switch in
control of one system over another according to default-

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 6 of 13
http://www.biomedcentral.com/1472-6947/12/94

interventionalist hypothesis. Note that depending on acti-
vation of γ parameter and assessment of benefits (gains)
and harms (losses) the control can be exerted by either
system: sometimes it will be the intuitive system that it
will exert the control and our action will take the form
“feeling of rightness” [43]; sometimes, it will be system II
that it will prevail and drive our decisions. Thus, we
succeed in uniting parallel competitive with default-
interventionalist models by linking Mukherjee’s additive
model with the threshold model for decision-making.
As discussed above, many factors can activate the

switch such as the presence or absence of empirical,
quantitative data, the context of decision making (e.g.
affect poor or rich), the decision maker’s expertise and
experience, etc. In addition, extensive psychological re-
search has demonstrated that people often use a simple
heuristic, which is based on the prominent numbers as
powers of 10 (e.g., 1,2,5,10,20,50,100,200 etc.) [44]. That
is, although system I does not perform the exact calcula-
tions, it still does assess “gist” of relative benefits and
harms, and likely does so according to “1/10 aspiration
level” [44] (rounded to the closest number) in such a
way that the estimates of benefits/harms ratio change by
1,2,5, 10, etc. orders of magnitude. Therefore, in this sec-
tion we consider several prototypical situations: 1) when
γ = 0, 0.5, or 1; 2) when BII> >HII, BII =HII and BII < <
HII; and 3) when regret of omission (BI) < < regret of
commission (HI), BI =HI, or BI> >HI

First, note that γ=0, when the numerator of the left frac-
tion in the Equation 6 (Additional file 1: Appendix) is
zero, i.e., when pBII � 1� pð ÞHII ¼ 0, or solving for p, we
obtain p ¼ 1

1þBII
HII

, which is exactly the value of the EUT

threshold for the probability at which the expected utilities
of the two options are the same. This will correspond to
model #3 above, in which system II exerts full control over
decision-making. Therefore, when γ = 0, we have the clas-
sic EUT and therapeutic threshold model. In this case, re-
gret does not affect the EUT benefits and harms, and

pt ¼ HII
HIIþBII

¼ 1
1þBII

HII

. If BII> >HII, pt approaches zero and a

decision-maker will recommend treatment to virtually
everyone. On the other hand, if BII =HII, pt equals 0.5 and
she might recommend treatment if the disease is as likely
as not. Finally, if BII < < HII, pt approaches 1.0, and the
decision-maker is expected to recommend treatment only
if she is absolutely certain in diagnosis.
At the other extreme, if γ = 1, we have the pure sys-

tem I model (corresponding to model #3 above, which
solely relies on system I processes). Note the value of
γ=1, when the denominator of the second fraction in
Equation 6 (Additional file 1: Appendix) equals one, or
when the expression HI � BI ¼ 0 , i.e., when BI=HI.
Under these conditions, it is fairly obvious that the

system I assessments become irrelevant if the perceived
net benefit of the treatment is equal to the perceived net
harm. When γ=1, regret avoidance becomes the key mo-
tivator, not EUT’s benefits and harms. Note that in sys-
tem I p is not related to γ in terms of the valuation
(Equation 1). Under these circumstances only decision-
making under system I operate and the analytical pro-
cesses of system II are suppressed (Equation 1) as seen
in those decision-makers who tend to follow intuition
only, or are extremely affected by their past experiences
without considering new facts on the ground. That is,
differences in probability do not play any role in such
decisions, because a person who only uses system I
doesn’t consider probability as a factor.
Finally, if γ = 0.5, the decision maker is motivated by

EUT and by regret avoidance (model #2 listed above). In
this case, the benefits (BII), harms (HII), regrets of omission
(BI) and commission (HI) are all active players. These three
cases are presented in Table 1 (see Additional file 2) which
shows threshold probabilities for γ=0.5 and objective data
indicating a high benefit/harms ratio (BII=HII ¼ 10). Also
shown is how the threshold probability depends on indi-
vidual risk perception. If HI> >HI, it magnifies effect of
BI/HI (see Equation 3), which results in extreme behavior
in sense of increasing likelihood that such a person will ei-
ther always accept (as pt<0) or reject treatment (as pt>1).
For HI <<HII, the impact on the way system I processes
benefits and harms is not that pronounced and influences
the EUT threshold to much smaller extent.

Results
Illustrative medical examples
Clinical examples abound to illustrate applicability of
our model. To illustrate the salient points of our model,
we chose two prototypical examples where there is close
trade-offs between treatments’ benefits and harms.

Example #1: treatment of pulmonary embolism
Pulmonary embolism (PE) (blood clot in the lungs) is an
important clinical problem that can lead to significant
morbidity and death [45]. Even though many diagnostic
imaging tests exist to aid in the accurate diagnosis of PE,
the tests are often inconclusive, and physicians are left to
face the decision whether to treat patient for presumptive
PE, or attribute the patient’s clinical presentation (such as
shortness of breath and/or chest pain) to other possible
etiologies. There exists an effective treatment for a PE,
which consists of the administration of 2 anticoagulants
(blood thinners): heparin followed by oral anticoagulants
such as warfarin [46,47]. Heparin (unfractionated or low-
molecular weight heparins) are highly effective treatments
associated with relative risk reduction of death from PE by
70-90% in comparison to no treatment [46,47]. This con-
verts into the absolute death reduction as: net benefits,

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 7 of 13
http://www.biomedcentral.com/1472-6947/12/94

BII=17.5% to 22.5% (calculated as 25% morality without
heparin minus 7.5% to 2.5% with heparin) [17,18,46,47].
However, these drugs are also associated with a significant
risk of life-threatening bleeding; net harms range from
HII=0.037% (a typical scenario) to 5% (a worst-case sce-
nario) depending on the patients’ other comorbid condi-
tions [17,18,47,48]. Thus, net benefits/net harms range
from 60.8 (22.5/0.037) (best case) to 3.5 (17.5/5)(worst
case scenario). If we apply a classic EUT threshold
[13,14,36], which relies solely on system II processes, we
observe that the probability of pulmonary embolism above
which the physician should administer anticoagulants
ranges from 1.6% [¼ 1= 1þ 60:8ð Þ ] (best case) to 22.2%
[1= 1þ 3:5ð Þ ](worst case scenario). However, ample clin-
ical experience has demonstrated that few clinicians would
consider prescribing anticoagulants at such low probability
of PE [18]. In fact, most experts in the field recommend
giving anticoagulants when probability of PE exceeds 95%
[49-51]. We have previously suggested that this is because
regret associated with administering unnecessary and po-
tentially harmful treatments under these circumstances
likely outweighs regret associated with failing to adminis-
ter potentially beneficial anticoagulants [17-19]. We now
show how this argument can be made in the context of
dual processing theory. Indeed, some physicians may feel
that the risk of bleeding may be much higher, particularly
in case of a patient who recently experienced major
hemorrhage. The physician may not have data readily
available to adjust her EUT, system II-based calculations.
Rather, she employs the system I-based reasoning, globally
assessing the benefits and harms of treatments under her
disposal. Importantly, these are personal, intuitive, affect-
based, subjective judgments of the values of outcomes that
are influenced by memory limitations and recent experi-
ences and that may not be objectively based on the exter-
nal evidence [2,30-33]. In addition, it is well documented
that the physicians’ recent experience leads to a type of
bias, known as primacy effect, that is governed by system I
[2,33]. If the last patient with PE whom the physician took
care of had severe bleeding, system I may be primed in
such a way that it will likely conclude that harms outweigh
benefits. In our case of PE, if her reasoning is dominated
by system I (operating, say, at γ level of 0.77 according to
model #2 listed above, see Section “The behavior of DSM-
M model”) in a such way that the physician concludes that
if harms is larger than benefits by 10%, then the threshold
probability above which she will treat her patient sus-
pected of PE exceeds 95% [as easily demonstrated after
plugging in the benefits/harms values in Equation 3)
pt dualð Þ ¼ :222� :77ð Þ= 2�:23ð Þ� �:10=:225ð Þ ¼ 0:966 ¼
96:6% for k ¼ 1]. Note that this calculation describes cir-
cumstances under which the physician would adhere to
the contemporary practice guidelines i.e. to prescribe
anticoagulants when PE exceeds 95% [49-51]. It should be

further noted that if γ value is only slightly higher (≥0.78),
the physician will require the absolute certainty to act (i.e.
the threshold ≥1).
DSM offers an account of the opposite behavior as well

i.e. the threshold based on global evaluation using both
system I and system II can also be lower than the EUT
threshold (if BI>HI additive, model #1, Equation 3). For
example, the physician may trivialize the risks of treatment
and believe that the benefits are much higher than the
treatment harms. As a result, the threshold above which
she commits to treatment drops below EUT threshold (as
predicted by Equation 3). Figure 3 shows how the decision
threshold (pt) is affected by the relative involvement of
systems I and II in dual process model of medical
decision-making in the “best” ( BII=HII ¼ 60:8 ) and
“worst case” scenario (BII=HII ¼ 3:5) for treatment of PE
and when system I valuation of benefits is greater than
harms or when harms are perceived to outweigh benefits.
It can be seen that when objective data indicate that bene-
fits considerably outweigh harms (BII >>> HII) (as when
BII=HII ¼ 60:8), then as long as system I values benefits as
being greater than harms, the threshold dramatically drops
to zero indicating that the extent of system I involvement
(i.e. γ value) in decision-making is of little consequence.
However, if system I clashes with objective data, then the
probability of PE above which the decision-maker is pre-
pared to treat, dramatically increases (Figure 3a). Similarly,
in all other circumstances (when BII >HII, BII ~HII, BII <
HII), the threshold probability is significantly affected by
involvement of system I (Figures 3b–3d).

Example #2: treatment of acute leukemia
Acute myeloid leukemia (AML) is a life-threatening dis-
ease, which, depending on the aggressiveness of disease
can be cured in the substantial minority of patients. To
achieve a cure, patients are typically given induction
chemotherapy to bring the disease into remission, after
which another form of intensive therapy – so called, con-
solidation treatment – is given. To achieve a cure in
patients with more aggressive course of disease such as
those classified as intermediate- and poor-risk AML
based on cytogenetic features of disease, allogeneic stem
cell transplant (alloSCT) is recommended [52]. However,
the cure is not without price- many patients given
alloSCT as a consolidation therapy die due to treatment.
A decision dilemma faced by a physician is whether to
recommend alloSCT, or alternative treatment, such as
chemotherapy or autologous SCT, which has lower
cure rate but less treatment-related mortality. In
intermediate-risk AML, for example, credible evidence
shows that, compared with chemotherapy allogeneic
alloSCT result in better leukemia-free survival (LFS) by
at least 12% at 4 years (LFS with alloSCT =53% vs 41%
with chemotherapy/auto SCT) [53]. Treatment-related

a) BII >>> HII b) BII > HII

d) BII > HIIc) BII= HII

Figure 3 Dual decision threshold (pt) as the function of relative involvement of systems I and II in dual process model of medical
decision making: a) objective data show very high benefit/harms ratio (BII> >HII), b) moderately high benefit/harms ratio (BII > HII), c)
BII = HII, d) BII < HII. The intercept at y axis threshold probability. The graph shows how the threshold is affected by the extent of system I
involvement (γ) and whether system I perceives that benefits is greater than harms [by 5% in this example](red lines, circles) or that harms
outweigh benefits[by 5%] (blue line, squares). Decision-maker accepts treatment if the probability of disease exceeds the threshold; otherwise,
treatment would not be acceptable (see text for details).

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 8 of 13
http://www.biomedcentral.com/1472-6947/12/94

mortality is much higher with alloSCT by 16%, on
average (19% with alloSCT vs. 3% with chemotherapy/
autoSCT) [53]. This means that based on objective
data, and using rational EUT model, we should recom-
mend alloSCT for any probability of AML relapse
≥57.1% threshold ¼ 1= 1þ 0_12=0:16ð Þ ¼ 0:571ð Þ. There-
fore, treatment benefits and harms are, on average, very
close. Because of this, the driving force to recommend
alloSCT is the physician’s estimates of the patient’s toler-
ability of alloSCT: if she assess that the patient will not
be able to tolerate alloSCT, the physician will not recom-
mend transplant. Conversely, if she thinks that the pa-
tient will be able to tolerate allo SCT, the physician will
recommend it. Although there are objective criteria to
evaluate a patient’s eligibility for transplant, the assess-
ment to the large extent depends on physicians’ judg-
ment and experience [54]. That is, the assessment of
patient’s eligibility for transplant depends both on the

objective data on benefits and harms (system II ingredi-
ents) and intuitive, gist type of judgment (characteristics
of system I). As discussed above, system I does not con-
duct the precise calculations. Rather, it relies on “gist” or
on simple heuristics such as those that are based on
powers of 10 (e.g., 1,2,5,10,20, etc.) [42]. The physician,
therefore, adjusts the threshold above or below based on
her intuitive calculations. For instance, it is often the
case that the physician whose patient recently died dur-
ing the transplant is more reluctant to recommend the
procedure even to those patients who, otherwise, seems
fit for it. In doing so, the physician in fact modifies her/
his dual system threshold upwards. In our example, let’s
assume that the physician judges that the harms of
alloSCT for a given patient is twice as large as reported
in the studies where patients were carefully selected for
transplant [52]. That, in our case, would mean that mor-
tality due to alloSCT is 32% (instead of 16%). We can

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 9 of 13
http://www.biomedcentral.com/1472-6947/12/94

now plug these numbers in Equation 3) (BII = 0.12, HII =
0.16, BI = 0.12, HI = 0.32).
Note that the physician can make this judgment at vari-

ous level of activation of system I. If the decision is pre-
dominantly driven by system I judgment then our
physician’s threshold according to Equation 3) is greater
than 100% for all circumstances in which γ value exceeds
55%. That means that under these circumstances of sys-
tem I activation, the physician will never recommend
transplant. The opposite can occur for those physicians
whose experience is not affected by poor patients’ out-
comes. Under such circumstances, the physician may
judge the patient to be in such a good condition that she
may re-adjust the reported treatment-related transplant
risk to be as half of those observed risks in the published
clinical studies (i.e. 8%). The new numbers required to de-
termine the threshold according to Equation 3 are: BII =
0.12, HII = 0.16, BI = 0.12, HI = 0.08. If the physician relies
excessively on system I, as often seen in busy clinics where
decisions are routinely made on “automatic pilot”, the dual
threshold drops to zero (for all γ >89%). That means, that
the physician will recommend alloSCT to all her/his
patients under these circumstances.
As discussed above, we provide the precise calculations

only to illustrate the logic of decision-making. The process
should be understood more along semi-quantitative or
qualitative description of clinical decision-making. Although
currently the Equation 3) allows entry of almost any value
for benefit and harms, it is probably the case that benefit
and harms as perceived by system I are based on “1/10
aspirational level” [44], so that only values of 1,2,5,10, 20 etc.
should be allowed. This is, however, empirical question that
should be answered in further experimental testing; there-
fore, at this time, we decided not to provide the exact
boundaries of the values for benefit and harms that can be
entered in Equation 3 (see Discussion). Note also that these
calculations are decision-maker specific, and although we il-
lustrate them from the perspective of the physician, the
same approach applies to the patient, who ultimately has to
agree –based on her own dual cognitive processing- on the
suggested course of treatment actions.

Discussion
Models of medical decision-making belong to two gen-
eral classes-descriptive and prescriptive. The former,
which the DSM-M exemplifies, attempt to explain why
decision makers take or might take certain actions when
presented with challenging decision problems abundant
in contemporary medicine. The latter, exemplified by the
normative therapeutic threshold models [13,14] pre-
scribe the choice options that a rational decision maker
should take. We have defined the first formal dual-
process theory of medical decision-making by taking
into consideration the deliberative and the experiential

aspects that encompass many of the critical decisions
physicians face in practice. Mathematically, our model
represents an extension of Mukharjee’s additive Dual
System Model [11] to the clinical situation where a
physician faces frequent dilemmas: whether to treat the
patient who may or may not have the disease, or choose
one treatment over another for prevention of disease
that is yet to occur. Our model is unique in that incor-
porates an exemplar of strong emotion, decision regret,
as one of the important components of system I func-
tioning. We focused on regret because previous research
has shown that people often violate EUT prescribed
choice options in an effort to minimize anticipated re-
gret [1,2,20]. Although we use the more common psy-
chological term “regret,” the concept is analogous to
Feinstein’s term “chagrin” [55]. In fact, explicit consider-
ation of post-choice regret in decision making has been
considered an essential element in any serious theory of
choice and certainly dominates many clinical decisions
[1,2,20]. We also reformulated the original model using
the threshold concept- a fundamental approach in med-
ical decision-making [13,14,36]. The threshold concept
represents a linchpin between evidence (which presents
on the continuum of credibility) and decision-making,
which is a categorical exercise (as choice options are ei-
ther selected or not) [13,14,36]. Using an example such
as pulmonary embolism, we have shown how the
extended model can explain deviations from outcomes
predicted by EUT, and account for the variation in man-
agement of pulmonary embolism [45]. In general, it is
possible that the huge practice variation well documen-
ted in contemporary medicine [56-61], can be, in part,
due to individual differences in subjective judgments of
disease prevalence and “thresholds” at which physicians
act. [17,18,62]. This may be because quantitative inter-
pretations of qualitative descriptors such as rarely, un-
likely, possible, or likely [63] differ markedly among
individuals and hence “gist” representations of a given
clinical situation can vary widely among different physi-
cians [30]. We are, of course, aware that many other fac-
tors contribute to variation in patient care including the
structure of local care organizations, the availability of
medical technologies, financial incentives etc [60]. Our
intent in this article is to highlight, yet another import-
ant factor- individual differences in risk assessment as
shaped by different mechanisms operating within a dual
process model of human cognitive functioning [5].
There are many theories of decision-making [64].

Most assume a single system of human reasoning [11].
Nevertheless, all major theories of choice agree that ra-
tional decision-making requires integrations of benefits
(gains) and harms (losses). EUT vs. non-EUT theories of
decision-making differ in how benefits and harms should
be integrated in a given decision task. To date, dual

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 10 of 13
http://www.biomedcentral.com/1472-6947/12/94

processing theory provides the most compelling explan-
ation how both intuitive and rational cognitive processes
integrate information on benefits and harms and provide
not only descriptive assessments of decision-making, but
possibly may lead to insights that improve the way deci-
sions are made. Figures 3 & 4 illustrate how dual decision
threshold (shown on the Y axis) for deciding between two
possible courses of action can be influenced by the degree
of system I involvement. As discussed above and mathem-
atically captured in Equation 3, the clinical action such as
treat versus no treat is best explained by relating benefit
and harms of proposed therapeutic interventions to the
threshold probability: if the estimated probability of dis-
ease is greater than the threshold probability, then the
decision-maker is inclined to give treatment; if the prob-
ability of disease is below the threshold, then the treat-
ment is withheld. Figure 4 shows a dramatic drop in the
decision threshold as a function of the ratio between bene-
fit and harms, which is derived from empirically obtained
evidence. When these data are solely relied on by system
II, the rational course of action consists of administering
treatment as long as the probability of disease is above the
threshold regardless how low the threshold probability
drops [13,14,36] (which in case of the treatment of a pa-
tient with pulmonary embolism can be as low as 1.6%!).
Paradoxically, if we were to adopt this – presumably most
rational-approach to the practice of medicine, we would
likely see a further explosion of inappropriate and wasteful
use of health care resources [18,21]. This is because in
today’s practice, benefits of approved treatments vastly
outweigh their harms, and as a result threshold probability
values is predictably very low for the majority of health
care interventions employed in the contemporary clinical
practice [18]. System I, however, does offer a means of
mitigation. The correction of the thresholds – our action

Figure 4 Dual Decision Threshold Model. Classic, expected utility thresh
system II, EUT (expected utility threshold) (solid line). The treatment should
should be withheld. Note that if system I perceives that harms are higher t
higher than classic EUT (dotted line). However, if BI > HI, the threshold prob
for details).

whether we are comfortable treating at higher or lower
probability than the thresholds obtained via usage of sys-
tem II – depends on the extent of involvement of system I
in decision-making. If system I perceives that harms are
higher than system I benefits, the threshold probability is
always higher than classic EUT threshold. However, if
BI>HI, the threshold probability is always lower than the
EUT threshold (Figure 4). This is particularly evident in
clinical practice when physicians attempt to tailor evi-
dence based on the results of the research study, which
generates the “group averages”, to individual patients who
often differ in important ways from patients enrolled in
the research studies (e.g., these patients may be older, have
comorbid conditions, might be using multiple medica-
tions, etc.) [65]. It is under these circumstances that sys-
tem I affects our judgments and can give rise to different
decisions from those based solely on system II. Note, how-
ever, that although system I does assess benefits and
harms, it likely does so via”gist” representation and not ne-
cessarily by employing the exact numerical values as sys-
tem II does [30]. System I is also affected by emotions, as
illustrated in the case where experts panels of the govern-
ments of many countries recommended H1N1 influenza
vaccination, but where inoculation was refused by the ma-
jority of patients [66,67].
It is interesting to examine circumstances under which

we always treat (pt≤ 0) or never treats (pt≥ 1). Equation 1
(Additional file 2: Table S1) shows that when objective
evidence indicates that benefits outweigh harms, and
when this is further augmented by the decision-maker’s
risk attitude in such a way that it magnifies system I’s
valuation of benefits and harms, then we can expect to
continue to witness further overtreatment in clinical
practice (as pt drops to zero) [65]. However, when the
decision-maker perceives the benefits smaller than

old probability as a function of benefit/harms ratio as derived by
be given if the probability of disease is above the threshold, otherwise
han system I benefits (BI < HI), the threshold probability is always
ability is always lower than the EUT threshold (dashed line) (see text

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 11 of 13
http://www.biomedcentral.com/1472-6947/12/94

harms, then the threshold increases; consequently, the
decision-maker will require higher diagnostic certainty
before acting (Figure 3 & Figure 4). This may occur dur-
ing extrapolation of research results from the group
averages to individual patients, when empirical evidence
about BII and HII is considered to be unreliable, when
the decision-maker is risk averse, or when his or her
cognitive processes are biased through the distorting
effects of recent experience, memory limitations or other
forms of biases well described in the literature [2,31,33].
This discussion illustrates how the “rationality of action”
may require a re-definition, one encompassing both the
formal principles of probability theory and human intui-
tions about good decisions [5,68]. Our goal here is not
to demonstrate that one approach is conclusively super-
ior to the other- we are merely outlining the differences
in the current physicians’ behavior from the perspective
of dual processing theory.
Despite the growing recognition of the importance of

dual processing for decision-making [2,5], a few formal
models have been developed to try to capture the es-
sence of the way we make decisions. Because different
authors focus on different aspects of a multitude of
decision-making processes, Evans has recently pointed
out that there are many dual processing theories [27]
which fall into two main groups [27,40] parallel competi-
tive theories and default-interventionalist theories. While
the exact accounts of cognitive processes between these
two groups of theories differ [27], as discussed above
(Section The behavior of DSM-M Model), we, for the
first, time provide a platform, albeit the theoretical one,
for reconciling parallel competitive theories with default-
interventionalist theories.
Nevertheless, our main goal is to define a theoretical

model for medical decision-making; such a model may
enable creation of new theoretical frameworks for future
empirical research. Future research, obviously, involves
extension of the model described herein to more com-
plex clinical situations beyond relatively simple two-
alternative situation, even if the latter is frequently
encountered in practice. Particularly interesting will be
the extension of our dual processing model to include
the use of diagnostic tests as the number of new diag-
nostic technologies continues to explode. Finally, and
most importantly, the model presented here needs em-
pirical verification. This limitation is not unique to our
model, however, and this criticism can be leveled against
most current medical decision-making models, which
are rarely, if ever, subjected to empirical verification.
Our model heavily relies on Mukherjee’s model [11],

and is accurate to the extent his additive dual processing
model is correct (Figure 1, Equations 1 & 2). Also, note
that we have extended Mukherjee’s DSM model by omit-
ting his scaling constant k and using general utility

expressions, rather than a single parameter monotonic
power function. As discussed above, many factors can ac-
tivate the switch of system II. In fact, Kahneman warns [2]
that “because you have little direct knowledge what goes
on in your mind, you will never know that you might have
made a different judgment or reached a different decision
under very slightly different circumstances”. This implies
that the multiple factors affecting the gamma parameter
cannot be directly modeled. A possible solution –and area
for future research building on the psychological “fuzzy
trace theory” [30]-would be to employ a fuzzy logic model
to assess the values of γ (and threshold) as a function of
multiple fuzzy inputs [69].
The complexity described here notwithstanding, we

believe that the empirical verification of our current dual
processing model is feasible. Even without direct model-
ing of all factors affecting γ parameter, our model gener-
ates empirically falsifiable qualitative predictions as it
clearly identifies circumstances under which the decision
threshold is increased or decreased as a function of acti-
vation of system I (γ parameter). Using simulation to
imitate the various real-life decision-making scenarios
[70] offers most logical avenue toward the first empirical
testing of our model.
Our model also holds promise in medical education.

As highlighted in Introduction, modern knowledge of
cognition has taught us that most people, including phy-
sicians process information using both system I (fast, in-
tuitive) and system II (slow, deliberative) reasoning at
different times but few investigators have examined how
to teach physicians to integrate both modes of reasoning
in arriving at therapeutic strategies. On the diagnostic
side, many investigators [6,71] have examined clinical
reasoning and proposed how experienced physicians
move between system I and system II, although most
early papers used different terminology. The integration
of system I and system II in therapeutic decision making
in medicine has been less well examined. A number of
investigators have proposed approaches to using and
teaching system II reasoning, including the use of deci-
sion models [71]. Although this is taught in some
schools it has not yet taken medical education by storm
[71]. In the field of economic analysis Mukerjee has
proposed a theoretical means of combining system I and
system II reasoning. In this paper, we build on Mukurjee’s
work and show how the integration of system I and sys-
tem II therapeutic reasoning can form a basis for teaching
students and experienced physicians to recognize and
integrate system I and system II reasoning. Our model
uniquely captures most salient features of (medical)
decision-making, which can be effectively employed for di-
dactic purposes. It is believed that by recognizing separate
roles of system II and the influence of system I mechan-
isms on the way we make decisions, we can be in a better

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 12 of 13
http://www.biomedcentral.com/1472-6947/12/94

position to harness both types of processes toward better
practice of making clinical decisions [2,9].

Conclusion
We hope that our model will stimulate new lines of em-
pirical and theoretical work in medical decision-making.
In summary, we have described the first dual processing
model of medical decision-making, which has potential
to enrich the current medical decision-making field
dominated by expected utility theory.

Additional files

Additional file 1: Appendix: Derivation of DSM-M equation.

Additional file 2: Table S1. Evaluation of Behavior of Dual Processing
Model for Medical Decision-Making (DSM -M). Threshold probability as a
function of individual risk perception.

Competing interests
The authors declare that they have no competing interests.

Authors’ contributions
BD had an idea for the study. BD & IH jointly developed the model. IH
solved the model. BD and IH performed the analyses. JB and AT performed
additional analyses. SGP analyzed the performance of dual processing model
and provided an additional intellectual input. BD wrote the first draft. All
authors read and approved the final manuscript.

Acknowledgments
We want to thank to Dr Shira Elqayam of De Montfort University, Leicester,
UK for the most helpful comments, in particular to introducing us to a
notion of the parallel competitive vs. default-interventionalist dual processing
theories and pointing the way how our model can help reconcile these two
competing theoretical frameworks.
Presented as a poster at: 14th Biennial European Conference of the Society for
Medical Decision Making (SMDM Europe 2012) Oslo, Norway, June 10–12, 2012.
Supported by the US DoA grant #W81 XWH 09-2-0175 (PI Djulbegovic).

Author details
1Center for Evidence-based Medicine and Health Outcomes Research,
Tampa, FL, USA. 2Department of Internal Medicine, Division of Evidence-
based Medicine and Health Outcomes Research University of South Florida,
Tampa, FL, USA. 3Departments of Hematology and Health Outcomes and
Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
4Department of Mathematics, Indiana University Northwest, Gary, IN, USA.
5College of Nursing, University of South Florida, Tampa, FL, USA. 6Division of
Clinical Decision Making, Department of Medicine, Tufts Medical Center,
Boston, USA. 7USF Health, 12901 Bruce B. Downs Boulevard, MDC27, Tampa,
FL 33612, USA.

Received: 18 June 2012 Accepted: 21 August 2012
Published: 3 September 2012

References
1. Kahneman D: Maps of bounded rationality: psychology for behavioral

economics. Am Econ Rev 2003, 93:1449–1475.
2. Kahnemen D: Thinking fast and slow. New York: Farrar, Straus and Giroux; 2011.
3. Evans JSTBT: Hypothethical thinking. Dual processes in reasoning and

judgement. New York: Psychology Press: Taylor and Francis Group; 2007.
4. Stanovich KE, West RF: Individual differences in reasoning:implications for

the rationality debate? Behavioral and Brain Sciences 2000, 23:645–726.
5. Stanovich KE: Rationality and the Reflective Mind. Oxford: Oxford University

Press; 2011.
6. Croskerry P: Clinical cognition and diagnostic error: applications of a dual

process model of reasoning. Adv Health Sci Educ Theory Pract 2009,
14(Suppl 1):27–35.

7. Croskerry P: A universal model of diagnostic reasoning. Acad Med 2009,
84(8):1022–1028.

8. Croskerry P, Abbass A, Wu AW: Emotional influences in patient safety.
J Patient Saf 2010, 6(4):199–205.

9. Croskerry P, Nimmo GR: Better clinical decision making and reducing
diagnostic error. J R Coll Physicians Edinb 2011, 41(2):155–162.

10. Slovic P, Finucane ML, Peters E, MacGregor DG: Risk as analysis and risk as
feelings: some thoughts about affect, reason, risk, and rationality. Risk
Anal 2004, 24(2):311–322.

11. Mukherjee K: A dual system model of preferences under risk. Psychol Rev
2010, 177(1):243–255.

12. Djulbegovic B, Hozo I, Lyman GH: Linking evidence-based medicine
therapeutic summary measures to clinical decision analysis.
MedGenMed 2000, 2(1):E6.

13. Pauker S, Kassirer J: Therapeutic decision making: a cost benefit analysis.
N Engl J Med 1975, 293:229–234.

14. Pauker SG, Kassirer J: The threshold approach to clinical decision making.
N Engl J Med 1980, 302:1109–1117.

15. Djulbegovic B, Desoky AH: Equation and nomogram for calculation of
testing and treatment thresholds. Med Decis Making 1996, 16(2):198–199.

16. Djulbegovic B, Hozo I, Schwartz A, McMasters K: Acceptable regret in
medical decision making. Med Hypotheses 1999, 53:253–259.

17. Hozo I, Djulbegovic B:When is diagnostic testing inappropriate or irrational?
Acceptable regret approach. Med Decis Making 2008, 28(4):540–553.

18. Hozo I, Djulbegovic B: Will insistence on practicing medicine according to
expected utility theory lead to an increase in diagnostic testing?
Medical Decision Making 2009, 29:320–322.

19. Hozo I, Djulbegovic B: Clarification and corrections of acceptable regret
model. Medical Decision Making 2009, 29:323–324.

20. Kahneman D, Wakker PP, Sarin RK: Back to Bentham? Explorations of
experienced utility. Q J Econ 1997, 112:375–405.

21. Berwick DM, Hackbarth AD: Eliminating waste in US health care. JAMA: The
Journal of the American Medical Association 2012, 307(14):1513–1516.

22. Manchikanti L, Falco FJ, Boswell MV, Hirsch JA: Facts, fallacies, and politics
of comparative effectiveness research: part 2 – implications for
interventional pain management. Pain Physician 2010, 13(1):E55–E79.

23. Manchikanti L, Falco FJ, Boswell MV, Hirsch JA: Facts, fallacies, and politics
of comparative effectiveness research: part I. Basic considerations. Pain
Physician 2010, 13(1):E23–E54.

24. Hsee CK, Rottenstreich Y: Music, pandas and muggers: on the affective
psychology of value. J Exp Psychol 2004, 133:23–30.

25. Rottenstreich Y, Hsee CK: Money, kisses, and electric shock: On the
affective psychology of risk. Psychol Sci 2001, 12:185–190.

26. Evans JSTBT: Thinking Twice. Two Minds in One Brain. Oxford: Oxford
University Press; 2010.

27. Evans JSTBT: Dual-process theories of reasoning: contemporary issues
and developmental applications. Dev Rev 2011, 31:86–102.

28. Edwards W, Miles R Jr, vonWinterfeld D: Advances in decision analysis. From
foundations to applications. New York: Cambridge University Press; 2007.

29. Simon HA: Information processsing models of cognition. Ann Review
Psychol 1979, 30:263–296.

30. Reyna VF, Brainerd CJ: Dual processes in decision making and developmental
neuroscience: a fuzzy-trace model. Dev Rev 2011, 31(2–3):180–206.

31. Reyna VF, Hamilton AJ: The importance of memory in informed consent
for surgical risk. Med Decis Making 2001, 21(2):152–155.

32. Kahneman D, Tversky A: The psychology of preferences. Sci American 1982,
246:160–173.

33. Tversky A, Kahneman D: Judgements under uncertainty: heuristics and
biases. Science 1974, 185:1124–1131.

34. Djulbegovic B, Hozo II, Fields KK, Sullivan D: High-dose chemotherapy in
the adjuvant treatment of breast cancer: benefit/risk analysis. Cancer
Control 1998, 5(5):394–405.

35. Djulbegovic B, Hozo I: When should potentially false research findings be
considered acceptable? PLoS Medicine 2007, 4(2):e26.

36. Djulbegovic B, Hozo I: Linking evidence-based medicine to clinical
decision analysis. Med Decision Making 1998, 18:464. abstract.

37. Zeelenberg M, Pieters R: A theory of regret regulation 1.0. J Consumer
Psychol 2007, 17:3–18.

38. Zeelenberg M, Pieters R: A theory of regret regulation 1.1. J Consumer
Psychol 2007, 17:29–35.

Djulbegovic et al. BMC Medical Informatics and Decision Making 2012, 12:94 Page 13 of 13
http://www.biomedcentral.com/1472-6947/12/94

39. Tsalatsanis A, Hozo I, Vickers A, Djulbegovic B: A regret theory approach to
decision curve analysis: a novel method for eliciting decision makers’
preferences and decision-making. BMC Medical Informatics and Decision
Making 2010, 10(1):51.

40. Evans JSTBT: On the resolution of conflict in dual process theories of
reasoning. Think Reasoning 2007, 13(4):321–339.

41. Hammond KR: Human judgment and social policy. irreducible uncertainty,
inevitable error, unavoidable injustice. Oxford: Oxford University Press; 1996.

42. Duhigg C: The power of habit: why we do what we do in life and business.
New York: Random House; 2012.

43. Thompson VA, Prowse Turner JA, Pennycook G: Intuition, reason, and
metacognition. Cogn Psychol 2011, 63:107–140.

44. Brandstatter E, Gigerenzer G: The priority heuristic: making choices
without trade-offs. Psychol Rev 2006, 113:409–432.

45. Sox HC: Better care for patients with suspected pulmonary embolism.
Ann Intern Med 2006, 144(3):210–212.

46. Barritt DW, Jordan SC: Anticoagulant drugs in the treatment of
pulmonary embolism. A controlled trial. Lancet 1960, 1:1309–1312.

47. Segal JB, Eng J, Jenckes MW, Tamariz LJ, Bolger DT, Krishnan JA, Streiff MB,
Harris KA, Feuerstein CJ, Bass EB: Diagnosis and treatment of deep venous
thrombosis and pulmonary embolism. In AHRQ Publication No
03-E016. Washington, DC: Agency for Healthcare Research and Quality, U.S.
Department of Health and Human Services; 2003.

48. Linkins L-A, Choi PT, Douketis JD: Clinical impact of bleeding in patients
taking oral anticoagulant therapy for venous thromboembolism:
a meta-analysis. Ann Intern Med 2003, 139(11):893–900.

49. Roy PM, Durieux P, Gillaizeau F, Legall C, Armand-Perroux A, Martino L,
Hachelaf M, Dubart AE, Schmidt J, Cristiano M, et al: A computerized
handheld decision-support system to improve pulmonary embolism
diagnosis: a randomized trial. Ann Intern Med 2009, 151(10):677–686.

50. Roy P-M, Colombet I, Durieux P, Chatellier G, Sors H, Meyer G: Systematic
review and meta-analysis of strategies for the diagnosis of suspected
pulmonary embolism. BMJ 2005, 331(7511):259.

51. Hull RD: Diagnosing pulmonary embolism with improved certainty and
simplicity. JAMA 2006, 295(2):213–215.

52. Koreth J, Schlenk R, Kopecky KJ, Honda S, Sierra J, Djulbegovic BJ, Wadleigh
M, DeAngelo DJ, Stone RM, Sakamaki H, et al: Allogeneic stem cell
transplantation for acute myeloid leukemia in first complete remission:
systematic review and meta-analysis of prospective clinical trials.
Jama 2009, 301(22):2349–2361.

53. Cornelissen JJ, Van Putten WL, Verdonck LF, Theobald M, Jacky E, Daenen
SM, van Marwijk Kooy M, Wijermans P, Schouten H, Huijgens PC, et al:
Results of a HOVON/SAKK donor versus no-donor analysis of
myeloablative HLA-identical sibling stem cell transplantation in first
remission acute myeloid leukemia in young and middle-aged adults:
benefits for whom? Blood 2007, 109(9):3658–3666.

54. Djulbegovic B: Principles of reasoning and decision-making. In Decision
Making in Oncology Evidence-based management. Edited by Djulbegovic B,
Sullivan DS. New York: Churchill Livingstone, Inc; 1997:1–14.

55. Feinstein AR: The ‘chagrin factor’ and qualitative decision analysis.
Arch Intern Med 1985, 145:1257–1259.

56. Detsky AS: Regional variation in medical care. N Engl J Med 1995,
333:5890590.

57. Dilts DM: Practice variation: the Achilles’ Heel in quality cancer care.
J Clin Oncol 2005, 23(25):5881–5882.

58. Eddy DM: Variations in physician practice: the role of uncertainty.
Health Aff 1984, 3(2):74–89.

59. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL: The
implications of regional variations in medicare spending. Part 2: health
outcomes and satisfaction with care. Ann Intern Med 2003, 138(4):288–298.

60. Sirovich BE, Gottlieb DJ, Welch HG, Fisher ES: Regional variations in health
care intensity and physician perceptions of quality of care. Ann Intern
Med 2006, 144(9):641–649.

61. Zhang Y, Baicker K, Newhouse JP: Geographic variation in medicare drug
spending. N Engl J Med 2010, 363(5):405–409.

62. Hozo I, Djulbegovic B: Explaining variation in practice: acceptable regret
approoach. Boston: 28th Annual Meeting of the Society for Medical
Decision Making; 2006.

63. Shaw NJ, Dear PR: How do parents of babies interpret qualitative
expressions of probability? Arch Dis Child 1990, 65(5):520–523.

64. Reyna VF: Theories of medical decision making and health: an evidence-
based approach. Med Decis Making 2008, 28(6):829–833.

65. Djulbegovic B, Paul A: From efficacy to effectiveness in the face of
uncertainty: indication creep and prevention creep. JAMA 2011, 305
(19):2005–2006.

66. Ofri D: The emotional epidemiology of H1N1 influenza vaccination.
N Engl J Med 2009, 361(27):2594–2595.

67. Poland GA: The 2009–2010 influenza pandemic: effects on pandemic and
seasonal vaccine uptake and lessons learned for seasonal vaccination
campaigns. Vaccine 2010, 28(Supplement 4(0)):D3–D13.

68. Krantz DH, Kunreuther HC: Goals and plans in decision making.
Judgement and Decision Making 2007, 2(3):137–168.

69. Zimmerman HJ: Fuzzy set theory anf its applications. 3rd edition. Boston:
Kluwer; 1996.

70. Society for simulation in healthcare. http://ssih.org/about-simulation
(Last accessed: August 27,2012).

71. Kassirer JP, Kopelman RI: Learning clinical reasoning. Baltimore: Williams &
Wilkins; 1991.

doi:10.1186/1472-6947-12-94
Cite this article as: Djulbegovic et al.: Dual processing model of medical
decision-making. BMC Medical Informatics and Decision Making 2012 12:94.

Submit your next manuscript to BioMed Central
and take full advantage of:

• Convenient online submission

• Thorough peer review

• No space constraints or color figure charges

• Immediate publication on acceptance

• Inclusion in PubMed, CAS, Scopus and Google Scholar

• Research which is freely available for redistribution

Submit your manuscript at
www.biomedcentral.com/submit

  • Abstract
    • Background
    • Methods
    • Results
    • Conclusions
  • Background
  • Methods
    • A dual system model
    • Modification of DSM for medical decision-making
    • The behavior of &b_k;DSM-&e_k;&b_k;M&e_k; model
  • Results
    • Illustrative medical examples
    • Example #1: treatment of pulmonary embolism
    • Example #2: treatment of acute leukemia
  • Discussion
  • Conclusion
  • Additional files
  • Competing interests
  • Authors’ contributions
  • Acknowledgments
  • Author details
  • References

<<
/ASCII85EncodePages false
/AllowTransparency false
/AutoPositionEPSFiles true
/AutoRotatePages /PageByPage
/Binding /Left
/CalGrayProfile (Dot Gain 20%)
/CalRGBProfile (sRGB IEC61966-2.1)
/CalCMYKProfile (U.S. Web Coated 50SWOP51 v2)
/sRGBProfile (sRGB IEC61966-2.1)
/CannotEmbedFontPolicy /Error
/CompatibilityLevel 1.4
/CompressObjects /Tags
/CompressPages true
/ConvertImagesToIndexed true
/PassThroughJPEGImages true
/CreateJobTicket false
/DefaultRenderingIntent /Default
/DetectBlends true
/DetectCurves 0.0000
/ColorConversionStrategy /LeaveColorUnchanged
/DoThumbnails true
/EmbedAllFonts true
/EmbedOpenType false
/ParseICCProfilesInComments true
/EmbedJobOptions true
/DSCReportingLevel 0
/EmitDSCWarnings false
/EndPage -1
/ImageMemory 1048576
/LockDistillerParams true
/MaxSubsetPct 100
/Optimize true
/OPM 1
/ParseDSCComments true
/ParseDSCCommentsForDocInfo true
/PreserveCopyPage true
/PreserveDICMYKValues true
/PreserveEPSInfo true
/PreserveFlatness true
/PreserveHalftoneInfo false
/PreserveOPIComments false
/PreserveOverprintSettings true
/StartPage 1
/SubsetFonts true
/TransferFunctionInfo /Apply
/UCRandBGInfo /Preserve
/UsePrologue false
/ColorSettingsFile ()
/AlwaysEmbed [ true
]
/NeverEmbed [ true
]
/AntiAliasColorImages false
/CropColorImages true
/ColorImageMinResolution 300
/ColorImageMinResolutionPolicy /OK
/DownsampleColorImages true
/ColorImageDownsampleType /Bicubic
/ColorImageResolution 300
/ColorImageDepth -1
/ColorImageMinDownsampleDepth 1
/ColorImageDownsampleThreshold 1.50000
/EncodeColorImages true
/ColorImageFilter /DCTEncode
/AutoFilterColorImages true
/ColorImageAutoFilterStrategy /JPEG
/ColorACSImageDict <<
/QFactor 0.15
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/ColorImageDict <<
/QFactor 0.15
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/JPEG2000ColorACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/JPEG2000ColorImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/AntiAliasGrayImages false
/CropGrayImages true
/GrayImageMinResolution 300
/GrayImageMinResolutionPolicy /OK
/DownsampleGrayImages true
/GrayImageDownsampleType /Bicubic
/GrayImageResolution 300
/GrayImageDepth -1
/GrayImageMinDownsampleDepth 2
/GrayImageDownsampleThreshold 1.50000
/EncodeGrayImages true
/GrayImageFilter /DCTEncode
/AutoFilterGrayImages true
/GrayImageAutoFilterStrategy /JPEG
/GrayACSImageDict <<
/QFactor 0.15
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/GrayImageDict <<
/QFactor 0.15
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/JPEG2000GrayACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/JPEG2000GrayImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/AntiAliasMonoImages false
/CropMonoImages true
/MonoImageMinResolution 1200
/MonoImageMinResolutionPolicy /OK
/DownsampleMonoImages true
/MonoImageDownsampleType /Bicubic
/MonoImageResolution 1200
/MonoImageDepth -1
/MonoImageDownsampleThreshold 1.50000
/EncodeMonoImages true
/MonoImageFilter /CCITTFaxEncode
/MonoImageDict <<
/K -1
>>
/AllowPSXObjects false
/CheckCompliance [
/None
]
/PDFX1aCheck false
/PDFX3Check false
/PDFXCompliantPDFOnly false
/PDFXNoTrimBoxError true
/PDFXTrimBoxToMediaBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXSetBleedBoxToMediaBox true
/PDFXBleedBoxToTrimBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXOutputIntentProfile (None)
/PDFXOutputConditionIdentifier ()
/PDFXOutputCondition ()
/PDFXRegistryName ()
/PDFXTrapped /False

/CreateJDFFile false
/Description <<
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
/BGR <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>
/CHS <FEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000500044004600206587686353ef901a8fc7684c976262535370673a548c002000700072006f006f00660065007200208fdb884c9ad88d2891cf62535370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002>
/CHT <FEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef653ef5728684c9762537088686a5f548c002000700072006f006f00660065007200204e0a73725f979ad854c18cea7684521753706548679c300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002>
/CZE <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>
/DAN <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>
/DEU <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>
/ESP <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>
/ETI <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>
/FRA <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>
/GRE <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>
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
/HRV <FEFF005a00610020007300740076006100720061006e006a0065002000410064006f00620065002000500044004600200064006f006b0075006d0065006e0061007400610020007a00610020006b00760061006c00690074006500740061006e0020006900730070006900730020006e006100200070006900730061010d0069006d006100200069006c0069002000700072006f006f006600650072002000750072006501110061006a0069006d0061002e00200020005300740076006f00720065006e0069002000500044004600200064006f006b0075006d0065006e007400690020006d006f006700750020007300650020006f00740076006f00720069007400690020004100630072006f00620061007400200069002000410064006f00620065002000520065006100640065007200200035002e0030002000690020006b00610073006e0069006a0069006d0020007600650072007a0069006a0061006d0061002e>
/HUN <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>
/ITA <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>
/JPN <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>
/KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020b370c2a4d06cd0d10020d504b9b0d1300020bc0f0020ad50c815ae30c5d0c11c0020ace0d488c9c8b85c0020c778c1c4d560002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e>
/LTH <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>
/LVI <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>
/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken voor kwaliteitsafdrukken op desktopprinters en proofers. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
/NOR <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>
/POL <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>
/PTB <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>
/RUM <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>
/RUS <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>
/SKY <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>
/SLV <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>
/SUO <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>
/SVE <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>
/TUR <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>
/UKR <FEFF04120438043a043e0440043804410442043e043204430439044204350020044604560020043f043004400430043c043504420440043800200434043b044f0020044104420432043e04400435043d043d044f00200434043e043a0443043c0435043d044204560432002000410064006f006200650020005000440046002c0020044f043a04560020043d04300439043a04400430044904350020043f045604340445043e0434044f0442044c00200434043b044f0020043204380441043e043a043e044f043a04560441043d043e0433043e0020043404400443043a04430020043d04300020043d0430044104420456043b044c043d043804450020043f04400438043d044204350440043004450020044204300020043f04400438044104420440043e044f044500200434043b044f0020043e044204400438043c0430043d043d044f0020043f0440043e0431043d0438044500200437043e04310440043004360435043d044c002e00200020042104420432043e04400435043d045600200434043e043a0443043c0435043d0442043800200050004400460020043c043e0436043d04300020043204560434043a0440043804420438002004430020004100630072006f006200610074002004420430002000410064006f00620065002000520065006100640065007200200035002e0030002004300431043e0020043f04560437043d04560448043e04570020043204350440044104560457002e>
/ENU (Use these settings to create Adobe PDF documents for quality printing on desktop printers and proofers. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /NoConversion
/DestinationProfileName ()
/DestinationProfileSelector /NA
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure true
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles true
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /NA
/PreserveEditing true
/UntaggedCMYKHandling /LeaveUntagged
/UntaggedRGBHandling /LeaveUntagged
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [595.440 793.440]
>> setpagedevice

RESEARCH ARTICLE

Dual Processing Model for Medical Decision-
Making: An Extension to Diagnostic Testing
Athanasios Tsalatsanis1,2, Iztok Hozo3, Ambuj Kumar1,2, Benjamin Djulbegovic1,2,4*

1 Comparative Effectiveness Research, University of South Florida, Tampa, FL, United States of America,
2 Department of Internal Medicine, University of South Florida, Tampa, FL, United States of America,
3 Department of Mathematics, Indiana University of Northwest, Gary, IN, United States of America,
4 Departments of Hematology and Health Outcomes and Behavior, H. Lee Moffitt Cancer Center &
Research Institute, Tampa, FL, United States of America

* [email protected]

Abstract
Dual Processing Theories (DPT) assume that human cognition is governed by two distinct

types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based

on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeu-

tic medical decision-making. The DPMmodel indicates that doctors decide to treat when

treatment benefits outweigh its harms, which occurs when the probability of the disease is

greater than the so called “threshold probability” at which treatment benefits are equal to

treatment harms. Here we extend our work to include a wider class of decision problems

that involve diagnostic testing. We illustrate applicability of the proposed model in a typical

clinical scenario considering the management of a patient with prostate cancer. To that end,

we calculate and compare two types of decision-thresholds: one that adheres to expected

utility theory (EUT) and the second according to DPM. Our results showed that the deci-

sions to administer a diagnostic test could be better explained using the DPM threshold.

This is because such decisions depend on objective evidence of test/treatment benefits

and harms as well as type 1 cognition of benefits and harms, which are not considered

under EUT. Given that type 1 processes are unique to each decision-maker, this means

that the DPM threshold will vary among different individuals. We also showed that when

type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect

a decision; the decision is based on the assessment of benefits and harms of treatment.

These findings could explain variations in the treatment and diagnostic patterns docu-

mented in today’s clinical practice.

Introduction
A paradigmatic decision-making dilemma faced by clinicians is whether to observe the patient
without ordering a diagnostic test, order a diagnostic test and act according to the results of the
test, or administer treatment without ordering a test. Typically, this decision relies on the prob-
ability of disease and the relationship between the treatment’s harms and benefits. As described

PLOSONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 1 / 16

OPEN ACCESS

Citation: Tsalatsanis A, Hozo I, Kumar A,
Djulbegovic B (2015) Dual Processing Model for
Medical Decision-Making: An Extension to Diagnostic
Testing. PLoS ONE 10(8): e0134800. doi:10.1371/
journal.pone.0134800

Editor: Guy Brock, University of Louisville, UNITED
STATES

Received: December 1, 2014

Accepted: July 14, 2015

Published: August 5, 2015

Copyright: © 2015 Tsalatsanis et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.

Data Availability Statement: Data are presented in
the manuscript.

Funding: This work is supported by the Department
of Army grant #W81 XWH 09-2-0175. (PI: BD).

Competing Interests: The authors have declared
that no competing interests exist.

later in this paper, the assessment of the likelihood of disease and the evaluation of treatment’s
benefits and harms is often done intuitively, but this decision-making process can be formal-
ized under the “threshold model”.

According to the threshold model [1,2], when faced with a choice of observing the patient,
ordering a diagnostic test, or administering treatment, there is a probability of disease, also
known as threshold probability, at which a decision maker is indifferent between any two
choices (e.g. treating vs. ordering a test, or ordering a test vs. withholding treatment) [3–6].
Furthermore, decisions involving diagnostic testing rely on two probabilities of disease known
as testing and treatment thresholds. Testing threshold relates to the decision about ordering a
test vs. observing a patient and treatment threshold relates to the decision about administering
treatment vs. ordering the diagnostic test. According to the threshold model [1,2], if the proba-
bility of disease is smaller than the testing threshold, the test should be withheld. If the proba-
bility of disease is above the treatment threshold, then treatment should be ordered without
ordering a diagnostic test. The test should only be ordered if the estimated probability of the
disease is between the testing and treatment thresholds (Fig 1).

The threshold model relies on expected utility theory (EUT) and it was formulated almost 4
decades ago[1,2]. EUT suggests that when choosing between different strategies, the decision
maker should always select the strategy that leads to the outcome with the highest expected
utility. It has been well documented, however, that EUT is routinely violated by decision-mak-
ers [7–9]. These violations are typically attributed to the decision maker’s emotional, experien-
tial or intuitive responses to decision choices that are different from the EUT derived expected
utilities. Consequently, the main drawback of threshold model is its reliance on EUT as demon-
strated in our recent empirical study [10].

The importance of non-EUT based cognitive processes has recently been highlighted by
dual processing theories (DPT) of human reasoning and decision-making [11], which are
increasingly accepted as the dominant explanation of how people make decisions [5,7,12–19].
DPT posits that human cognition is governed by two types of processes [11,19]: type 1 pro-
cesses, which are intuitive, automatic, fast, narrative, experiential and affect-based, and type 2
processes, which are analytical, slow, verbal, deliberative and allow for abstract and hypotheti-
cal thinking. Therefore, the EUT model cannot be seen as an adequate model of medical deci-
sion-making.

Fig 1. Relation between the probability of disease and the threshold probabilities for testing and treatment (adopted from [3]).

doi:10.1371/journal.pone.0134800.g001

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 2 / 16

To overcome the drawbacks of the EUT-based threshold model we recently developed a
Dual Processing Model (DPM) [12], which is based on DPT. The DPM [12] incorporates
regret to model type 1 processes and EUT to model type 2 processes. This is because regret is
one of the key emotions that play a major role in medical-decision making [20–22]. Two main
assumptions of DPM are that the extent of activation of type 1 processes is regulated by a
parameter γ, and that when faced with a decision problem our initial responses tend to rely
mostly on type 1 processes [23].

In our previous work [12] we demonstrated the applicability of the DPM-based threshold in
a situation when no diagnostic test is available but a clinician has to make a decision whether
to administer treatment or not. Here, we extend our work to include a wider class of medical
decision-making problems that involve diagnostic testing.

Methods

Threshold models
EUT threshold model. Most decision theories agree that decision-making depends on

evaluation of harms (losses) and benefits (gains) associated with a given decision strategy. The
threshold model takes this into consideration by relating the threshold probability to benefit/
harms ratio. For example, the EUT threshold is calculated as:

pt;EUT ¼ 1

1þ BII
HII

; forHII > 0

Where pt, EUT 2 [0,1] is the threshold probability i.e. the probability of disease at which we are
indifferent between treatment vs. no treatment. BII � 0 is the net benefits of treatment defined
as the difference in outcomes of treating and not treating a patient with disease, as realized by
type 2 (denoted also as II in the equations) processes [12,24–29]. HII > 0 is the net harms due
to treatment, defined as the difference in outcomes of not treating and treating the patients
without disease as realized by type 2 processes[12,24–29]. Typically, the values of harms and
benefits are obtained from the best available evidence found in the literature [12,24–29]. Note
that for the validity of the pt,EUT equation, HII must take values greater than zero. This require-
ment is clinically justifiable because in reality every treatment is associated with some harms.

Regret-based model. When treating a patient, a decision maker may face two types of
regret: regret associated with failure to provide necessary treatment (regret of omission) and
regret associated with administering harmful treatment (regret of commission)
[12,21,22,30,31]. These two regrets are used to compute the regret based threshold probability
as:

pt;RG ¼ 1

1þ BI
HI

; forHI > 0

where pt,RG 2 [0,1] is the threshold probability at which a decision maker is indifferent between
treating or not a patient. BI � 0 is the net benefits of treatment as realized by type 1 processes
and computed here as regret of omission.HI > 0 is the net harms of treatment as realized by
type 1 (denoted also as I in the equations) processes and computed here as regret of commis-
sion. [12,21,22]. Both BI and HI values may be elicited using the Dual Analogue Scale described
elsewhere [21,22]. As with HII,HI must take values greater than zero so that pt,RG is defined.

Dual Processing Model. DPM [12] assumes that the valuation of a risky choice is formed
as the combination of type 1 and type 2 processes. To demonstrate, consider a clinical scenario
(Fig 2) in which a decision maker is faced with a choice of treating (Rx) or not (NoRx) of a

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 3 / 16

patient who has a disease with probability p. Each decision results in a specific outcome xi. For
example, outcome x1 corresponds to the decision of treating a patient who had a disease and
outcome x2 corresponds to the decision of treating a patient who did not have a disease. The
parameters xmI

i � 0 and xmII
i � 0 correspond to valuations of the outcome xi when the decision

maker employs type 1 and 2 processes respectively. Each outcome is also associated with type
1, UI,i � 0, and type 2, UII,i � 0, utilities.

Solving the decision tree in Fig 2, we derive the DPM threshold probability, pt 2 [0,1], or the
probability at which we are indifferent between providing and withholding treatment, as [12]:

pt ¼ min ðpt;EUTÞ 1þ g
2ð1� gÞ

HI

HII

� �
1� BI

HI

� �� �
; 1

� �
; for g 2 ½0; 1� ð1Þ

The interaction between type 1 and type 2 processes is represented by the parameter γ 2
[0,1]. γ exemplifies the extent of activation of type 1 processes in the decision in such a way
that when it is zero the decision-making processes is based on type 2 processes according to the
EUT paradigm. As the value of γ increases, so does the involvement of type 1 processes in the
decision. However, Eq 1 is not valid for values of γ = 1. In that case, based on the decision tree
depicted in Fig 2 for γ = 1, the decision to treat or not depends solely on the explicit evaluation
of harms and benefits based on type 1 processes (see also the Special Case in S1 Appendix). As
a consequence, treatment should be administered only if benefits of treatment as assessed by
type 1 processes outweigh harms of treatment.

Fig 2. Decision tree describing a typical scenario in which a physician is considering administering
(Rx) / withholding treatment (NoRx) to/from his patient. xi represents an outcome; γ is the involvement of
type 1 in the decision process; p is the probability of disease;UI,i is the utility of the outcome xi under type 1
process;UII,i is the utility of outcome xi under type 2 processes; The valuation of an outcome xi under type 1 is
estimated as the regret associated with the outcome xi; the valuation of an outcome xi under type 2 is
estimated as the utility of the outcome xi ([12] for details).

doi:10.1371/journal.pone.0134800.g002

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 4 / 16

γ can be best visualized as the relative distance between the analytically derived threshold pt,
EUT and the regret derived threshold pt,RG, or [32]:

g ¼ min
jpt;EUT � pt;RGj

pt;EUT
; 1

( )

However, γ can be affected by many different mechanisms that characterize type 1 pro-
cesses. Even though our model assumes a dominant role of regret, it does also incorporate
other mechanisms of type 1 cognitive processes.

If a patient’s probability of disease is greater than the threshold probability then the decision
maker favors treatment and withholds treatment otherwise. Eq 1 shows the impact of the
extent of treatment harms and benefits on decisions and how they relate to the DPM and EUT
thresholds. When the type 1 benefits of treatment as perceived by the decision maker are higher
than its harms, the DPM threshold is always lower than the EUT threshold. Conversely, the
DPM threshold is always greater than the EUT threshold if the type 1 harms of treatment are
perceived to be higher than benefits. These changes in the threshold often lead to different
choices than those predicted by the EUT and therefore may explain the violations of EUT in
decision-making described extensively in literature [7,8,21,22,33,34].

DPMwith a diagnostic test. In many cases the use of a diagnostic test may assist the treat-
ing physician in decreasing diagnostic uncertainty. However, obtaining diagnostic information
may expose the patient to unnecessary risks [4] and therefore, a test should be ordered only
when benefits of testing outweigh its risks [3].

Typically, deciding when to perform a diagnostic test relates to the assessment of the prior
probability that a patient has a suspected disease [3]. If the probability of disease is very low or
very high, then performing a diagnostic test may be unnecessary. As explained above, accord-
ing to the threshold framework, there exists: 1. a probability at which we are indifferent
between performing a diagnostic test and withholding treatment; and 2. a probability at which
we are indifferent between performing a diagnostic test and administering treatment. These
probabilities are formally known as the threshold probabilities for testing and they are decom-
posed into 1. testing threshold (ptt) and 2. treatment threshold respectively (prx) [3]. Here we
derive and present both threshold probabilities in terms of DPM [12].

We consider a generic scenario in clinical decision-making in which a decision maker is
considering one of three strategies for the management of a patient’s condition (Fig 3). These
strategies are: 1. do nothing (NoRx), 2. perform a diagnostic test (T), and 3. administer treat-
ment (Rx). The patient may have a disease (D) with probability p. Each strategy results in an
outcome xi, which is associated with a certain valuation, xmI

i � 0 when type 1 processes are
involved and xmII

i � 0 when type 2 processes are employed. Each outcome has a utility UI,i� 0
for type 1 processes and UII,i � 0 for type 2 processes. As described earlier, valuation of out-
comes under type 1 processes is performed using regret elicited using the Dual Visual Analogue
Scale (DVAS) while valuation of outcomes under type 2 processes is based on EUT and the lat-
est available evidence [12].

Solving the decision tree in Fig 3, we derive the following expected valuations for each strat-
egy (detailed derivation is presented in the S1 Appendix):

VðRxÞ ¼ g
2
ðUI;2 � UI;4Þ þ ð1� gÞ½pUII;1 þ ð1� pÞUII;2�

VðNoRxÞ ¼ g
2
ðUI;3 � UI;1Þ þ ð1� gÞ½pUII;3 þ ð1� pÞUII;4�

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 5 / 16

and

VðTÞ ¼ g
4
ðUI;2 � UI;4 þ UI;3 � UI;1Þ þ ð1� gÞ½pSUII;1 þ ð1� pÞð1� SpÞUII;2 þ pð1� SÞUII;3

þ ð1� pÞSpUII;4� � ðgHI;T þ ð1� gÞHII;TÞ

The notation for the expected valuations is as follows: UI,i� 0 and UII,i� 0 corresponds to
the utilities of the xi outcome under type 1 and 2 processes respectively; p is the probability of
disease; γ 2 [0,1] is the weight given to type 1 processes; S 2 [0,1] is the sensitivity of the diag-
nostic test; Sp 2 [0,1] is the specificity of the diagnostic test; HI,T� 0 and HII,T� 0 denote the
harms associated with the diagnostic test as perceived by type 1 and 2 processes respectively.

Threshold probabilities for testing
Testing threshold. The testing threshold is the probability at which we are indifferent

between withholding treatment and ordering a diagnostic test [3]. Thus, working with the
expected valuations for NoRx and T, V(NoRx) = V(T), and solving for the threshold probability
we derive:

ptt ¼ min ptt;EUT 1þ g

4ð1� gÞð1� SpÞ 1þ 1
1�Sp

HII;T

HII

� HI

HII

1� BI

HI

� �
þ 4

HI;T

HII

� �2
4

3
5; 1

8<
:

9=
;; for g 2 ½0; 1� ð2Þ

Eq 2 is invalid for γ = 1. In that case, decision makers should always choose not to treat
instead of testing i.e. ordering a diagnostic test does not contribute to the decision (see S1

Fig 3. Decision tree describing a typical scenario in which a physician is considering one the
following three strategies: administering treatment (Rx); withholding treatment (NoRx); and
performing a diagnostic test before deciding on treatment (Test). xi represents an outcome; γ is the
involvement of type 1 in the decision process; p is the probability of disease;UI,i is the utility of the outcome xi
under type 1 andUII,i is the utility of outcome xi under type 2 cognitive processes; HI,T denotes the harms of
test as realized by type 1 andHII,T denotes the harms of test as realized by type 2 processes; P1 = pS+(1-p)
(1-Sp); P11 = pS/P1; P12 = (1-p)(1-Sp)/P1; P2 = (1-p)Sp+p(1-s); P21 = p(1-s)/P2; P22 = (1-p)Sp/P2; S is the test’s
sensitivity and Sp the test’s specificity. The valuation of an outcome xi under type 1 is estimated as the regret
associated with the outcome xi; the valuation of an outcome xi under type 2 is estimated as the utility of the
outcome xi.

doi:10.1371/journal.pone.0134800.g003

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 6 / 16

Appendix Special Case section for details). This result is a function of type 1 processes, which
do not have a role in calibration of probabilities, but treat each choice as “yes/no” outcome (see
Fig 3). Indeed, the key role of a diagnostic test is to decrease uncertainty by increasing/decreas-
ing probability of disease. When a decision-maker does not take this probability into account,
then there is no sense in considering a diagnostic test.

If the probability of disease is greater than or equal to ptt the decision maker favors the diag-
nostic test; otherwise, he/she prefers withholding treatment. The DPM testing threshold (Eq 2)
is always higher than the analytically derived EUT testing threshold if the relationship between
type 1 benefits and harms of treatment and harms of test is HI+4HI,T> BI. This relationship
shows that if the harms of test and treatment are perceived greater than the benefits of treat-
ment, the decision maker requires more certainty before testing. Conversely, the DPM testing
threshold is always lower than the EUT testing threshold ifHI+4HI,T> BI, which demonstrates
that the decision maker requires less certainty before testing. Note that the accuracy of the diag-
nostic test (expressed in terms of sensitivity and specificity) does not affect this finding. Both
ptt,EUT and ptt are undefined for the special case of Sp = 100%. However, as Sp!100%, ptt,
EUT!1 and ptt!1. This finding demonstrates a decision maker’s aversion in providing diag-
nostic testing, which is perceived to lead to more harms than benefits. Note that the values of γ,

Sp, S and
HI
HII


affect the degree (“depth”) by which the DPM testing threshold ptt is greater/

lower than the classic EUT threshold ptt,EUT; however, they do not change the quality of the
relationship.

Treatment threshold. The treatment threshold is the probability at which we are indiffer-
ent between testing and administering treatment [3]. Working with the expected valuations of
Rx and T, V(Rx) = V(T), and solving for the threshold probability we derive:

prx ¼ min prx;EUT 1þ g

4ð1� gÞSp 1� 1
Sp

HII;T

HII

� HI

HII

1� BI

HI

� �
� 4

HI;T

HII

� �2
4

3
5; 1

8<
:

9=
;; for g 2 ½0; 1� ð3Þ

Eq 3 is invalid for γ = 1. In that case, decision makers should always choose treating instead
of testing (see S1 Appendix Special Case section for details). As outlined above, this result is a
consequence of how type 1 processes work: by treating each choice as “yes/no” outcome there
is no sense in taking diagnostic test probabilities into account (see Fig 3).

If the probability of disease is greater than or equal to prx the decision maker will choose to
administer treatment; otherwise, he/she will prefer to perform a diagnostic test. The DPM
treatment threshold (Eq 3) is always higher than the analytically derived EUT treatment
threshold if the relationship between type 1 benefits and harms of treatment and harms of test
is as follows:HI > BI+4HI,T. This relationship shows that if the decision maker assumes that
the harms of treatment are higher than its benefits added to the harms of testing, then he
requires more certainty before proceeding with treatment. Conversely, the DPM treatment
threshold is always lower than the EUT treatment threshold ifHI < BI+4HI,T, which demon-
strates that the decision maker requires less certainty before proceeding with treatment. As
above, the test sensitivity and specificity does not affect this relationship. Both rules assume
that the diagnostic test is objectively assessed (via type 2 functioning) to be less harmful than

the treatment, HII,T < HII, which is almost always the case. The values of γ, Sp, S, and
HI
HII


affect the extent (“depth”) by which the dual threshold prx is greater/lower than the classic EUT
threshold prx,EUT but does not change the quality of the relationship.

To summarize, for γ 2 [0,1) a decision maker will choose to perform a diagnostic test if the
patient’s probability of disease is ptt � p< prx. The probabilities ptt and prx are functions of a

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 7 / 16

decision maker’s attitudes towards treatment benefits and harms as well as harms of testing
and they are derived using both type 1 and type 2 cognitive mechanisms. The probability of
disease, p can be estimated by statistical evidence, and by the physician’s intuition and experi-
ence. When γ = 1, the management choices are limited to treatment vs no treatment as testing
results in the overall lower valuation in comparison with the other two alternatives. Therefore,
the optimal decision is a function of the decision maker’s attitudes towards treatment benefits
and harms as assessed by type 1 processes (see S1 Appendix Special Case section for details).

Case Study
We will now demonstrate the applicability of the proposed method as it relates to decisions
regarding performing prostate biopsy in a patient suspected of having prostate cancer. Con-
cerns about prostate cancer may be raised by elevated values of the Prostate-specific Antigen
(PSA) biomarker and/or by abnormalities found through Digital Rectal Examination (DRE).
Further verification is obtained through a biopsy, which is currently the gold standard for diag-
nosis of prostate cancer. During the prostate biopsy, several needles are inserted through the
rectum wall into the areas of the prostate, where the abnormality is detected, to remove small
amounts of tissue, which are later analyzed in the lab. The patient may experience discomfort,
pain, bleeding, hematuria, infections, sepsis and vasovagal episodes as a result of the biopsy
[35–40]. Table 1 summarizes the risks and benefits associated with prostate biopsy as reported
in literature.

Contingent on the results of the biopsy, the treating urologist may choose to perform a radi-
cal prostatectomy and surgically remove a part or all of the prostate gland. The goal of the pro-
cedure is to cure or control the cancer. The procedure is performed either through an open
surgery, where the surgeon makes a cut in the abdomen or between the testicles and the back
passage, or laparoscopy, where the surgeon makes several small incisions in the pelvis. In both
cases, the patient may experience major or minor complications after or during the surgery
including heart problems, blood clots, blood loss, allergic reactions to anesthesia, infections,
erectile dysfunction, urinary incontinence, damage to the urethra or the rectum [41–43]. How-
ever, radical prostatectomy has statistically beneficial effect on patient’s survival compared to
observation[44–46]. Table 2 summarizes the risks and benefits of radical prostatectomy as
reported in literature.

For example consider the management strategies for a 66-year-old patient with elevated
PSA and abnormal DRE: 1. do nothing (e.g. observe or wait for 6 months to repeat PSA and
DRE), 2. perform a prostate biopsy and act accordingly, and 3. proceed directly with radical
prostatectomy. Based on the evidence provided on Tables 1 and 2 we define the type 2 benefits
and harms regarding radical prostatectomy as BII = 2.5% to 10% andHII = 0.4%. The harms
associated with prostate biopsy are HII,T = 0.09%.

Table 1. Harms associated with cancer biopsy as reported in literature*.

Cancer biopsy

Harm (H) Size

Death (H) 0.09% [52]

Infections (H) 2–3% [37–39]

Hematuria (H) 50%-60% [35,36,38]

Discomfort, pain, bleeding, sepsis, vasovagal episodes (H) <10% [35–40]

* The data are related to transrectal ultrasound guided prostate biopsy.

doi:10.1371/journal.pone.0134800.t001

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 8 / 16

For demonstration and simplification purposes, we will first describe how the decision
dilemma described above would be solved relying solely on type 2 processes focusing on the
most important harms and benefits i.e. those related to survival. An elaborate modification of
the EUTmodel to include all harms and benefits reported in literature can also be implemented
as in [24]. We assume that the values of sensitivity and specificity of the biopsy are equal to
86% and 94% respectively [47] (the highest reported values for biopsy guided by transrectal
ultrasound (TRUS)). The EUT-based threshold probabilities, are derived by Eqs 2 and 3
(assuming γ = 0) and they are equal to ptt_EUT = 0% and prx_EUT = 21% (considering maximum
benefit of treatment BII = 10%). The results show that a decision maker will accept biopsy and
surgery at very low probabilities of prostate cancer: 0% for biopsy and 21% for surgery. That is,
according to the EUT model we should perform biopsy at the slightest suspicion of prostate
cancer (i.e., as long as it is greater than 0%!), and can recommend surgery at the estimated
probability of prostate cancer> 21%! No physician (or, a patient) would agree with such rec-
ommendations. The finding based on the EUT model also contradicts the influential, National
Cancer Network (NCCN) expert guidelines for prostate cancer [48] which indicates that a
prostate biopsy should be performed if the probability of prostate cancer exceeds 48% (indi-
rectly computed by a Gleason score of 8 or higher for symptomatic patients which translates
into 48% according to [49]).

Our finding of low threshold for the action may be attributed to the oversimplified assump-
tion focusing only on mortality, which is rather small: harms (death) attributed to biopsy
(0.09%) and prostatectomy (0.4%). If instead of death due to prostatectomy, we focus on erec-
tile dysfunction (37%), which reflects the main concern of patients with 20 or more years of life
expectancy, the threshold values increase considerably: ptt_EUT = 21% and prx_EUT = 49% (con-
sidering maximum benefit of treatment BII = 10%). In this case a decision maker opts out of
biopsy for probabilities less than 21% and requires more certainty for prostatectomy (49%).
The problem, however, is how to integrate multiple outcomes in the EUT model, particularly
since it is believed that the values people attach to different outcomes depend on the type 1
mechanisms, which processes the information on all benefits and harms in holistic fashion [4].

It is, therefore, necessary to arrive at decisions using cognitive mechanisms that employ
both type 2 and type 1 processes. In our model, this is easily accomplished by increasing the
value of γ, which reflects the extent of type 1 processes in the decision process. As a result both
testing thresholds change. To compute the threshold values from Eqs 2 and 3 we need to elicit
the decision maker’s preferences towards biopsy (HI,T) and towards prostatectomy (BI,HI). In
contrast to the valuation of outcomes through EUT that entail inquiries for every harm and

Table 2. Benefits and harms of radical prostatectomy as reported in literature*.

Radical prostatectomy

Benefit (B) or Harm (H) Size

Survival (B) Absolute risk reduction: 2.5%-
10% [44–46]

Death (H) 0.4% [46]

Erectile dysfunction (H) 37% [46]

Urinary incontinence (H) 10.8% [46]

Heart problems (H), blood clots (H), blood loss (H), allergic
reactions to anesthesia (H), infections (H), damage to the urethra or
the rectum (H)

<10% [41–43]

* The data are related to radical prostatectomy performed as an open surgery or laparoscopic.

doi:10.1371/journal.pone.0134800.t002

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 9 / 16

benefit individually, the type 1 processes valuate outcomes in a holistic manner by eliciting
regrets of omission and commission using the DVAs [21,22]. Because type 1 processes are
unique to each decision-maker, we expect that the DPM-based thresholds will vary among dif-
ferent individuals.

Figs 4 and 5 graph the values of the EUT and DPM thresholds for testing as functions of the
type 1 treatment benefit/harm ratio (BI/HI) for different values of type 1 harms of biopsy (HI,

T). Both figures are generated for maximum benefit of treatment (BII = 10%) however, Fig 4
assumes harms of treatment relate to survival (HII = 0.4%) and Fig 5 assumes harms of treat-
ment relate to erectile dysfunction (HII = 37%).

Fig 4 demonstrates that as the harms of biopsy (HI,T) increase (Fig 4b, 4c and 4d), the decision
maker will always choose a minimal risk and high benefit prostatectomy over the biopsy. This
result is true for any type 1 treatment benefit/harm ratio (BI/HI). In addition, it is shown that the
treatment threshold decreases dramatically as the type 1 benefits of prostatectomy outweigh its
harms (BI> HI) and that there exists a benefits/harms ratio at which a decision maker would opt
for prostatectomy at practically 0% probability of prostate cancer. These results appear to be dif-
ferent than those based on the EUTmodel, which recommends biopsy to practically all patients
and prostatectomy to the patients with probability of cancer greater than 21% regardless of the
decision maker’s preferences towards biopsy and prostatectomy. Note the important result in Fig

Fig 4. EUT and DPM testing thresholds as functions of type 1 benefits/harms of prostatectomy ratio. The chart progression (Fig 4a–4d) shows the
effect of increasing type 1 harms of biopsy on the values of testing thresholds. Unlike the EUT threshold, as harms of biopsy (HI,T) increase (Fig 4b, 4c and
4d), the DPM testing threshold increases to the maximum indicating that a decision maker will never choose a biopsy. When benefits of prostatectomy are
higher than its harms (BI>HI), the decision maker opts for prostatectomy at practically 0% of disease. Note that the DPMmodel allows for the treatment
threshold to be lower than the testing threshold. This is rationally not possible within the EUT framework, but has been observed in clinical practice. As an
illustration consider the case where BI<HI. The DPM testing threshold (ptt) is always higher than the EUT testing threshold (ptt,EUT). This is because the DPM
testing threshold considers the decision maker’s attitudes towards treatment according to which the benefits of treatment are higher than its harms (e.g.
BII>HII). The same holds for the case of BII<HII, but only whenHI,T>0 (i.e. the diagnostic test is harmful) (Fig 4b, 4c and 4d). If HI,T = 0 and BII>HII (Fig 4a), the
decision maker may choose test or treatment at the same probability of disease. Also, for most BI/HI, the DPM treatment threshold (prx) is lower than the EUT
treatment threshold (prx,EUT). Again, this is because the decision maker values treatment benefits higher that its harms.

doi:10.1371/journal.pone.0134800.g004

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 10 / 16

4: The treatment threshold is lower than the testing threshold, which is rationally not possible
within the EUT framework, but has been observed in clinical practice [20].

The results shown in Fig 5 are far more complicated and fascinating since in this case we
consider that the prostatectomy may cause erectile dysfunction in 37 out of 100 people (BII <
HII). It would be logical to assume that a decision maker would always prefer to undergo a diag-
nostic test with minimal harms before a harmful intervention. This behavior is demonstrated
in Fig 5a, where it is assumed thatHI,T = 0. However, the prostate cancer probability levels at
which a decision maker would opt in for either strategy changes based on the way the decision
maker experiences type 1 benefits and harms of prostatectomy. For example, in Fig 5a, the
decision maker will accept biopsy at a probability of prostate cancer greater than 40% (closer to
NCCN guidelines than the EUT threshold) and prostatectomy for probability greater than 90%
if he perceives type 1 harms of prostatectomy as greater than its benefits (e.g. HI = 8BI). If the
decision maker perceives type 1 benefits of prostatectomy higher than its harms (e.g. BI = 8HI),
he may tolerate biopsy for any probability of prostate cancer and accepts prostatectomy at a
probability greater than 60%.

Furthermore, as the harms of biopsy (HI,T) increase (Fig 5b, 5c and 5d), there is a range of
benefit/harms ratio above which the decision maker will prefer prostatectomy over biopsy. For
example, when type 1 harms of biopsy are believed to be 10% (Fig 5b), a prostatectomy is pre-
ferred to a biopsy (if type 1 benefits of prostatectomy are at least 2 times higher that its harms).
Similarly, when HI,T = 20% (Fig 5c), a prostatectomy is preferred to a biopsy if its benefits are
slightly higher than its harms. Once again, this is rationally not possible within the EUT frame-
work[20], however, it is observed in current urological practice.

Fig 5. EUT and DPM testing thresholds as functions of type 1 benefits/harms of prostatectomy ratio. The chart progression (Fig 5a–5d) shows the
effect of increasing type 1 harms of biopsy to the values of testing thresholds. The value of treatment threshold decreases as the ratio benefit/harms of
prostatectomy increases (Fig 5a, 5b, 5c and 5d). The value of testing threshold also decreases as the ratio benefit/harms of prostatectomy increases but only
when the harms of biopsy are zero (Fig 5a). If the decision maker perceives biopsy as harmful (Fig 5b, 5c and 5d) the testing threshold increases to the point
that he will never choose biopsy. A prostatectomy becomes the preferred choice when BI > 2HI in Fig 5b; BI > HI in Fig 5c; BI > 0.8HI in Fig 5d.

doi:10.1371/journal.pone.0134800.g005

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 11 / 16

Our case study clearly illustrates that the estimation of the exact values for each parameter,
particularly those valuated under type 1 functioning, is not a simple exercise and reflect the
complexity of real time decision making. It also demonstrates a variation in decisions made by
different decision makers, a finding that may explain why people violate EUT.

Discussion
In this article we described the derivation of a DPM for medical decision making to accommo-
date decisions that involve diagnostic testing. Our model is based on Dual Processing Theories
and assumes that human cognition relies on type 1 processes, which are intuitive and affect-
based, as well as type 2 processes, which are analytical and deliberate processes.

Most medical decision making models rely on EUT and have failed to explain variations
between the predicted versus the actual decisions people make. We hypothesize that this is
because most existing models depend only on the analytical process of human cognition and
ignore other experiential aspects of the decisions humans face[12,50].

Our DPMmodifies the EUT threshold model for decision making to incorporate influences
by both modes of human cognition: intuitive, affect-based (type 1) and analytical processes
(type 2). The derived expressions (Eqs 2 and 3) show that the DPM thresholds modify the EUT
thresholds based on the way decision makers evaluate trade-offs of treatment. For example, the
DPM-based testing threshold is always higher than the EUT-based threshold when harms of
treatment and of test are assessed by type 1 processes to be higher than benefits of treatment.
On the other hand, the DPM-based treatment threshold is always lower than the EUT-based
threshold when harms of treatment are perceived by the type 1 processes to be lower than treat-
ment benefits and biopsy harms. As described in the Methods section, the test sensitivity and
specificity does not affect this relationship. The importance of our findings is best seen in the
context of the current attempts to curb waste associated with over-testing. The American
Board of Internal Medicine’s (ABIM) nine specialty societies representing 374,000 physicians
developed a list of each specialty’s ‘Top Five’ inappropriately prescribed diagnostic tests in
order to improve care by eliminating unnecessary tests and procedures [51]. For example, one
typical recommendation reads, “Don’t order annual electrocardiograms (EKGs) or any other
cardiac screening for low-risk patients without symptoms. False-positive tests are likely to lead
to harm through unnecessary invasive procedures, over-treatment and misdiagnosis.” The
problem with this guideline is how to determine how low is “low” (in terms of “low-risk”) and
how likely is “likely” (in terms of false positives)? That is, at which threshold probability the
test should actually be ordered? As illustrated in this paper, because the test characteristics do
not affect the thresholds, we do not need to worry about false-positives (or, false-negatives for
that matter). What matters is 1) objective data on treatment benefits and harms, and 2) how
the decision-maker perceives these data (via type 1 processes). Therefore, a solution to the cur-
rent health care waste could be to continue emphasizing the need for reliable, evidence-based
resources and to highlight the importance of cognitive mechanisms in the way we process the
information we access through the literature and collect during a clinical encounter. It has
been argued that mindful awareness of type 1 and type 2 processes [15] may help us improve
our decision-making processes. Our model provides the salient outline of these processes and
how these processes can be effectively approached in clinical practice and education.

We demonstrated the applicability of our approach in a hypothetical case study in which a
decision maker is considering radical prostatectomy for a patient who has elevated PSA and
abnormal DRE. As the decision maker is uncertain whether a radical prostatectomy is the
appropriate action for the particular patient, he also considers a prostate biopsy. Our results
demonstrated the inability of EUT to model the preferences and attitudes of individual decision

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 12 / 16

makers towards treatment and diagnostic testing. On the other hand, the DPM threshold pro-
vides a convincing explanation as to why treatment decisions vary between decision makers.
We posit that this variation is contingent on the extent of activation of type 1 processes. Our
main point here is that type 2 processes (as adhered to the EUT model) will always produce the
same results, while it is type 1 processes that are unique to each decision-maker. In turn, it is
the extent of activation of type 1 processes that can explain excessive ordering of diagnostic
tests as well as overall variations in the treatment and diagnostic patterns documented in
today’s clinical practice.

Our model has limitations. Even though it is a simple mathematical expression, its applica-
tion is challenging since many of the model parameters are not easily elicited. Our suggestion,
which was implemented in this paper, is to use data from published literature to valuate out-
comes under type 2 processes and the Dual Visual Analogue scales developed in [21,22] to val-
uate outcomes under type 1 processes. In fact, we have shown that it is possible to elicit these
values [32] although we have not yet done it in the context of the diagnostic setting. Our next
step is to perform decision-making experiments initially in simulated environments, through
hypothetical scenarios, and later in real clinical environments. In both cases, we will compare
the decisions predicted by the DPMmodel to the actual decisions physicians make.

Throughout this article we avoided assigning a clear role to the decision maker. We believe
that our methodology can be used by physicians and/or by patients. It is our position, however,
that medical decision-making is shared between physicians and their patients. In such setting
physicians recommend alternative treatment strategies with their associated harms and bene-
fits and the patients eventually agree with the recommendation. We envision our methodology
as a part of a computerized decision support system operated by the physician to elicit the
patient’s preferences towards alternative forms of treatment.

Conclusions
We have extended the recently derived DPM for medical decision making to include a diagnos-
tic testing. Our model has the potential to explain the discrepancies found between optimal
and actual actions. Because it captures the salient elements of medical decision-making via few
parameters, our model has offered an important didactic value for medical education. Future
research involves testing our model in a simulated environment with a wide variety of health-
care professionals.

Supporting Information
S1 Appendix. Detailed derivations of Dual Processing thresholds.
(DOCX)

Acknowledgments
This work is supported by the Department of Army grant #W81 XWH 09-2-0175. (PI: BD)

Author Contributions
Conceived and designed the experiments: AT IH BD. Performed the experiments: AT IH BD.
Analyzed the data: AT IH BD. Contributed reagents/materials/analysis tools: AT IH AK BD.
Wrote the paper: AT IH BD.

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 13 / 16

References
1. Pauker S, Kassirer J (1975) Therapeutic decision making: a cost benefit analysis. N Engl J Med 293:

229–234. PMID: 1143303

2. Pauker SG, Kassirer J (1980) The threshold approach to clinical decision making. N Engl J Med 302:
1109–1117. PMID: 7366635

3. Sox HC, Higgins MC (1988) Medical decision making: Amer College of Physicians.

4. Hunink MGM, Glasziou PP, Siegel JE, Weeks JC, Pliskin JS, Elstein AS, et al. (2001) Decision making
in health and medicine: integrating evidence and values: Cambridge University Press.

5. Kahneman D (2003) Maps of bounded rationality: psychology for behavioral economics. American Eco-
nomic Review 93: 1449–1475.

6. Kahnemen D (2011) Thinking fast and slow. New York: Farrar, Straus and Giroux.

7. Baron J (2000) Thinking and deciding: Cambridge University Press.

8. Bell DE, Raiffa H, Tversky A (1988) Decision making: Descriptive, normative, and prescriptive interac-
tions: Cambridge University Press.

9. Hastie R, Dawes RM (2009) Rational choice in an uncertain world: The psychology of judgment and
decision making: SAGE Publications, Incorporated.

10. Djulbegovic B, Elqayam S, Reljic T, Hozo I, Miladinovic B, Tsalatsanis A, et al. (2014) How do physi-
cians decide to treat: an empirical evaluation of the threshold model. BMCMedical Informatics and
Decision Making 14: 47. doi: 10.1186/1472-6947-14-47 PMID: 24903517

11. Evans JSBT (2003) In two minds: dual-process accounts of reasoning. Trends in cognitive sciences 7:
454–459. PMID: 14550493

12. Djulbegovic B, Hozo I, Beckstead J, Tsalatsanis A, Pauker S (2012) Dual processing model of medical
decision-making. BMCMedical Informatics and Decision Making 12: 94. doi: 10.1186/1472-6947-12-
94 PMID: 22943520

13. Kahneman D, Slovic P, Tversky A (2005) Judgement under uncertainty: heuristics and biases. New
York: Cambridge University Press.

14. Croskerry P (2009) A universal model of diagnostic reasoning. Acad Med 84: 1022–1028. doi: 10.
1097/ACM.0b013e3181ace703 PMID: 19638766

15. Croskerry P (2013) FromMindless to Mindful Practice—Cognitive Bias and Clinical Decision Making.
The New England journal of medicine 368: 2445–2448. doi: 10.1056/NEJMp1303712 PMID:
23802513

16. Croskerry P, Nimmo GR (2011) Better clinical decision making and reducing diagnostic error. J R Coll
Physicians Edinb 41: 155–162. doi: 10.4997/JRCPE.2011.208 PMID: 21677922

17. Evans JSTBT (2007) Hypothethical thinking. Dual processes in reasoning and judgement. New York:
Psychology Press: Taylor and Francis Group.

18. Slovic P, Finucane ML, Peters E, MacGregor DG (2004) Risk as analysis and risk as feelings: some
thoughts about affect, reason, risk, and rationality. Risk Anal 24: 311–322. PMID: 15078302

19. Stanovich KE (1999) Who is rational?: Studies of individual differences in reasoning: Lawrence
Erlbaum.

20. Hozo I, Djulbegovic B (2008) When is diagnostic testing inappropriate or irrational? Acceptable regret
approach. Medical Decision Making 28: 540–553. doi: 10.1177/0272989X08315249 PMID: 18480041

21. Tsalatsanis A, Barnes LE, Hozo I, Djulbegovic B (2011) Extensions to Regret-based Decision Curve
Analysis: An application to hospice referral for terminal patients. BMCMedical Informatics and Decision
Making 11: 77. doi: 10.1186/1472-6947-11-77 PMID: 22196308

22. Tsalatsanis A, Hozo I, Vickers A, Djulbegovic B (2010) A regret theory approach to decision curve anal-
ysis: A novel method for eliciting decision makers’ preferences and decision-making. BMCMedical
Informatics and Decision Making 10: 51. doi: 10.1186/1472-6947-10-51 PMID: 20846413

23. Thompson VA, ProwseTurner JA, Pennycook G (2011) Intuition, reason, and metacognition. Cognitive
Psychology 63: 107–140. doi: 10.1016/j.cogpsych.2011.06.001 PMID: 21798215

24. Djulbegovic B, Hozo I, Lyman GH (2000) Linking evidence-based medicine therapeutic summary mea-
sures to clinical decision analysis. MedGenMed 2: E6.

25. Cornelissen JJ, van PuttenWL, Verdonck LF, Theobald M, Jacky E, Daenen SM, et al. (2007) Results
of a HOVON/SAKK donor versus no-donor analysis of myeloablative HLA-identical sibling stem cell
transplantation in first remission acute myeloid leukemia in young and middle-aged adults: benefits for
whom? Blood 109: 3658–3666. PMID: 17213292

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 14 / 16

26. Kearon C, Akl EA, Comerota AJ, Prandoni P, Bounameaux H, Goldhaber SZ, et al. (2012) Antithrombo-
tic therapy for VTE disease: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American
College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest 141: e419S–494S.
doi: 10.1378/chest.11-2301 PMID: 22315268

27. Koreth J, Schlenk R, Kopecky KJ, Honda S, Sierra J, Djulbegovic BJ, et al. (2009) Allogeneic stem cell
transplantation for acute myeloid leukemia in first complete remission: systematic review and meta-
analysis of prospective clinical trials. JAMA 301: 2349–2361. doi: 10.1001/jama.2009.813 PMID:
19509382

28. Linkins L-A, Choi PT, Douketis JD (2003) Clinical Impact of Bleeding in Patients Taking Oral Anticoagu-
lant Therapy for Venous Thromboembolism: A Meta-Analysis. Ann Intern Med 139: 893–900. PMID:
14644891

29. Segal JB, Eng J, Jenckes MW, Tamariz LJ, Bolger DT, Krishnan JA, et al. (2003) Diagnosis and Treat-
ment of Deep Venous Thrombosis and Pulmonary Embolism. Washington, DC: Agency for Healthcare
Research and Quality, U.S. Department of Health and Human Services.

30. Djulbegovic B, Hozo I, Schwartz A, McMasters K (1999) Acceptable regret in medical decision making.
Med Hypotheses 53: 253–259. PMID: 10580533

31. Djulbegovic B, Ende J, HammRM, Mayrhofer T, Hozo I, Pauker SG (2015) When is rational to order a
diagnostic test, or prescribe treatment: the threshold model as an explanation of practice variation.
European journal of clinical investigation 45: 485–493. doi: 10.1111/eci.12421 PMID: 25675907

32. Djulbegovic B, Elqayam S, Reljic T, Hozo I, Tsalatsanis A, Kumar A, et al. (2013) Empirical evaluation
of the threshold model. BMCMed Inform Decis Mak under review.

33. Dawes RM, Kagan J (1988) Rational choice in an uncertain world: Harcourt Brace Jovanovich New
York.

34. Hozo I, Djulbegovic B (2009) Will Insistence on Practicing Medicine According to Expected Utility The-
ory Lead to an Increase in Diagnostic Testing? Reply to DeKay’s Commentary: Physicians’ Anticipated
Regret and Diagnostic Testing. Medical Decision Making 29: 320–324.

35. De Jesus C, Corrêa LA, Padovani CR (2006) Complications and risk factors in transrectal ultrasound-
guided prostate biopsies. Sao Paulo Med J 124: 198–202. PMID: 17086300

36. Djavan B, Waldert M, Zlotta A, Dobronski P, Seitz C, Remzi M, et al. (2001) Safety and morbidity of first
and repeat transrectal ultrasound guided prostate needle biopsies: results of a prospective European
prostate cancer detection study. The Journal of urology 166: 856–860. PMID: 11490233

37. Lee G, Attar K, Laniado M, Karim O (2006) Safety and detailed patterns of morbidity of transrectal ultra-
sound guided needle biopsy of prostate in a urologist-led unit. International urology and nephrology 38:
281–285. PMID: 16868698

38. Rodriguez LV, Terris MK (1998) Risks and complications of transrectal ultrasound guided prostate nee-
dle biopsy: a prospective study and review of the literature. The Journal of urology 160: 2115–2120.
PMID: 9817335

39. Wagenlehner FME, Van Oostrum E, Tenke P, Tandogdu Z, Çek M, Grabe M, et al. (2012) Infective
complications after prostate biopsy: outcome of the Global Prevalence Study of Infections in Urology
(GPIU) 2010 and 2011, a prospective multinational multicentre prostate biopsy study. European
urology.

40. Sieber PR, Rommel F, Theodoran CG, Hong RD, Del Terzo MA (2007) Contemporary prostate biopsy
complication rates in community-based urology practice. Urology 70: 498–500. PMID: 17905105

41. Guillonneau B, Rozet F, Cathelineau X, Lay F, Barret E, Doublet JD, et al. (2002) Perioperative compli-
cations of laparoscopic radical prostatectomy: the Montsouris 3-year experience. The Journal of urol-
ogy 167: 51. PMID: 11743274

42. Fowler FJ, Barry MJ, Lu-Yao G, Roman A, Wasson J, Wennberg JE (1993) Patient-re ported complica-
tions and follow-up treatment after radical prostatectomy: The national medicare experience: 1988–
1990 (updated June 1993). Urology 42: 622–628. PMID: 8256394

43. Hu JC, Nelson RA, Wilson TG, Kawachi MH, Ramin SA, Lau C, et al. (2006) Perioperative complica-
tions of laparoscopic and robotic assisted laparoscopic radical prostatectomy. The Journal of urology
175: 541–546. PMID: 16406991

44. Abdollah F, Sun M, Schmitges J, Tian Z, Jeldres C, Briganti A, et al. (2011) Cancer-specific and other-
cause mortality after radical prostatectomy versus observation in patients with prostate cancer: compet-
ing-risks analysis of a large North American population-based cohort. European urology 60: 920–930.
doi: 10.1016/j.eururo.2011.06.039 PMID: 21741762

45. Bill-Axelson A, Holmberg L, Ruutu M, Häggman M, Andersson SO, Bratell S, et al. (2005) Radical pros-
tatectomy versus watchful waiting in early prostate cancer. New England Journal of Medicine 352:
1977–1984. PMID: 15888698

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 15 / 16

46. Wilt TJ, Brawer MK, Jones KM, Barry MJ, AronsonWJ, Fox S, et al. (2012) Radical prostatectomy ver-
sus observation for localized prostate cancer. New England Journal of Medicine 367: 203–213. doi:
10.1056/NEJMoa1113162 PMID: 22808955

47. Purohit RS, Shinohara K, Meng MV, Carroll PR (2003) Imaging clinically localized prostate cancer. The
Urologic clinics of North America 30: 279. PMID: 12735504

48. National Comprehensive Cancer Network (2013) NCCNClinical Practice Guidelines in Oncology,
Prostate Cancer, version 1.2013.

49. Andrén O, Fall K, Franzén L, Andersson SO, Johansson JE, Rubin MA (2006) How well does the Glea-
son score predict prostate cancer death? A 20-year followup of a population based cohort in Sweden.
The Journal of urology 175: 1337–1340. PMID: 16515993

50. Mukherjee K (2010) A dual systemmodel of preferences under risk. Psychological Review 177: 243–
255.

51. American Board of Internal Medicine.

52. Nam RK, Saskin R, Lee Y, Liu Y, Law C, Klotz LH, et al. (2010) Increasing hospital admission rates for
urological complications after transrectal ultrasound guided prostate biopsy. The Journal of urology
183: 963–969. doi: 10.1016/j.juro.2009.11.043 PMID: 20089283

DPM for Medical Decision-Making

PLOS ONE | DOI:10.1371/journal.pone.0134800 August 5, 2015 16 / 16

Writerbay.net

Looking for top-notch essay writing services? We've got you covered! Connect with our writing experts today. Placing your order is easy, taking less than 5 minutes. Click below to get started.


Order a Similar Paper Order a Different Paper