JMIR MEDICAL INFORMATICS

Khairat et al

Review

Reasons For Physicians Not Adopting Clinical Decision Support
Systems: Critical Analysis
Saif Khairat1,2, PhD, MPH; David Marc3, PhD; William Crosby2, BSN, RN; Ali Al Sanousi4, MD, PhD
1Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

2

School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

3

Health Informatics Graduate Program, College of Saint Scholastica, Duluth, MN, United States

4

Hamad Medical Coperation, Doha, Qatar

Corresponding Author:
Saif Khairat, PhD, MPH
Carolina Health Informatics Program
University of North Carolina at Chapel Hill
428 Carrington Hall
Chapel Hill, NC, 27514
United States
Phone: 1 9198435413
Email: saif@unc.edu

Abstract
Background: Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies.
They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum
providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical
errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one ofthe potential reasons for this shortfall.
Objective: The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a newframework for CDSS design in order to achieve user acceptance.
Methods: A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understandingof the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input,
system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) andthe user (ie, the physician).
Results: Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions.
We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design
based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern
CDSS outputs.
Conclusions: This research demonstrates that the incorporation of the proposed models will improve user acceptance to support
the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the useof the technology but can also pose a threat to the health and well-being of patients.
(JMIR Med Inform 2018;6(2):e24) doi: 10.2196/medinform.8912
KEYWORDS
decision support systems, clinical; decision making, computer-assisted; attitude to computers

Introduction
The Agency for Healthcare Research and Quality [1] promotesa systems approach that aims “to catch human errors before
they occur or block them from causing harm.” Clinical decision
support systems (CDSSs) are at the forefront of this aim. A
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CDSS provides alerts, reminders, prescribing recommendations,
therapeutic guidelines, image interpretation, and diagnostic
assistance. Although studies have shown that CDSSs reduce
medical errors and improve outcomes, they also demonstratethat CDSSs fall short of their full potential [2-9]. Research has
attempted to narrow in on the cause of this shortfall. Coiera [10]JMIR Med Inform 2018 | vol. 6 | iss. 2 | e24 | p. 1
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identified provider’s lack of willingness and ability to use thetechnological system as one of the primary reasons.
Wendt et al [11] discussed several factors that may be related
to the acceptance of CDSSs, including the relevance of theinformation provided by the system, perceived validity of thesystem, and the work and time expended on using the system.
These factors are similar to those defined by Davis [9] in thetechnology acceptance model (TAM) and later refined by
Venkatesh et al [12] in the unified theory of acceptance and useof technology (UTAUT). These models offer a potential
explanation for how expectations of performance, effort, social
influences, and facilitating conditions are determinants of user
acceptance and technology usage [12]. Using the TAM, Van
Schaik et al [13] evaluated a gastroenterology referral CDSS.
The system assisted primary care providers by suggesting anappropriate subspecialty referral (medical vs surgical),prioritizing urgency, and offering real-time booking [13]. They
found that physicians rated acceptance based on the potential
merits of the system rather than their experience with thecomputer system [13]. This finding was concordant with
Venkatesh et al’s [12] proposal of the UTAUT model, in which
they demonstrated that performance expectancy is the strongest
predictor of user acceptance of technology.
The theory behind user acceptance and its impact on the
adoption of technology has been thoroughly described. The

Khairat et alpurpose of this paper was to conduct a review of the literaturein order to evaluate our hypothesis that meaningful engagementof physicians in the design and development of CDSSs withtransparent decision-making processes will result in higher
acceptance rates.

Methods
Critical Review
A search of MEDLINE/PubMed, CINAHL, PsycInfo, IEEE
Xplore, and Web of Science was conducted using the keywords“clinical decision support,” “decision support acceptance,” and“user acceptance.” No timeframe limits were included for any
database, and the language filters were set to English studies
only. In our initial search, we found 186 papers. After removalof duplicates, 150 studies remained. To be included in thisreview, the papers had to match the following inclusionary
criteria: investigate human interaction with a CDSS and evaluate
user acceptance using the TAM questionnaire, focus groups, orinterviews. Papers were excluded if the focus was on decision
support systems that did not include clinical care or if they did
not empirically investigate user acceptance. Title and abstract
review eliminated 111 studies. The remaining 39 studies
underwent a full-text review, resulting in a final count of 14
studies that met inclusion criteria. The search results are
summarized in Figure 1.

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Diagram.

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Study findings were categorized as either showing favorable orunfavorable responses to CDSSs. The favorable and unfavorable
categorization was based on interpretations of focus groups andinterviews conducted by the researchers of the reviewed papers.
Additionally, the type of CDSS was noted for each of thereviewed papers. If a study used the TAM questionnaire, theresults were summarized separately.

Task Analysis
To gain a greater understanding of the problems associated with
CDSSs, we conducted a task analysis. Using past research, thetask analysis helped identify and describe the goals, user input,
system output, knowledge requirements, and constraints fromtwo different perspectives. We considered the perspective of
the machine (ie, the CDSS engine). We also considered theperspective of the user (ie, the physician). The literature review
and task analysis served as the basis for designing CDSS models
that improved user acceptance.

Results
Critical Review
The results of 14 articles were evaluated. The 11 articles that
qualitatively evaluated user acceptance of CDSSs can be foundin Table 1 and the three articles that quantitatively evaluateduser acceptance of CDSSs using TAM can be found in Table
2. Favorable and unfavorable responses for the aspects of

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clinical guidelines, reminders, and diagnostic CDSSs wererecorded. Favorable responses were due to ease of system use,
perceived time savings, and perceived usefulness of the systems
in improving care delivery and overall patient health [14]. Users
with higher computer skills were reported to have greater
acceptance; however, the majority of users had an unfavorable
acceptance response [15]. These unfavorable responses were
often related to workflow interference, questionable validity ofthe systems, excessive disturbances caused by the systems, andlack of efficiency. More specifically, the workflow constraints
were related to the CDSSs causing excessive alerts, increased
time in computer handling, and decreased face-to-face time withpatients [14,16,17].
Of the studies reviewed, three used the TAM questionnaire forassessing user acceptance of CDSSs (Table 2). The CDSSs inthese studies included an evaluation of two different
computerized clinical guideline systems and one that offeredreminders and alerts for evidenced-based guidelines. The ranges
on perceived usefulness all overlap, but on the CDSSs with thehighest perceived usefulness it also has the highest perceived
ease of use. Overall, the TAM questionnaire revealed moderateuser acceptance on all scales. In terms of the relationshipbetween user acceptance of CDSSs and patient safety, none of
the reviewed papers evaluated this topic. However, Bergman
and Fors [15] found that the use of the technology was relatively
low when user acceptance was low.

Table 1. Summary of user acceptance related to clinical decision support systems (CDSSs) from previous studies (N=11).
Study

Favorable response to CDSS

Unfavorable response to CDSS

CDSS Description

Bergman & Fors (2005) [15] Can save time and provide structure

Not suitable to workflow and thereis the risk of becoming dependent

CDSS for medical diagnosis of
psychiatric diseases

Curry & Reed (2011) [16]Concept was supported

Interference with workflow andquestionable validity

Prompts for adhering to diagnostic
imaging guidelines

Gadd et al (1998) [18]Easy to use, limits the need for data entry,
accurate, and relevant

Benefits are lost because it takes so Internet-based system that interaclong to use
tively presents clinical practiceguidelines at point of care

Johnson et al (2014) [19]Longitudinal acceptance behavior, perComputer literacy, user satisfaction, Clinical reminders and alerts forceived ease of use, and perceived usefulness and general optimism
patients with asthma, diabetes, hypertension, and hyperlipidemia

Rosenbloom et al (2004)[20]

Can improve efficiency and quality of care; Senior physicians did not think itenhances education
was necessary

Rousseau et al (2003) [21]Use of “active” CDSS can bridge the gap
between own practice and best practiceClinicians found it to be difficult to CDSS for chronic disease in generaluse and unhelpful clinicallypractice

Shibl et al (2013) [22]Performance expectancy, usefulness, andeffort expectancy

Trust in CDSS and need for thesystem

No specified CDSS; responses
based on past and present experiences with multiple CDSSs

Sousa et al (2015) [23]Belief that the suggestions were good forthe patient

Low confidence in the evidence

CDSS for nursing care plan

Terraz et al (2005) [24]Ease of use and easy access to information Information that is presented is already known

Guidelines for colonoscopies

Wallace et al (1995) [25]Can improve patient outcomes

Alerts are ignored because there is
not enough time to dedicate toforming an appropriate response

CDSS to standardize administrationof supplemental oxygen

Zheng et al (2005) [17]Improves performance leading to better
care, easy to use, and efficient

Iterative advisories, lack of releClinical reminders for chronic disvance, a lot of data entry, and disrup- eases and preventive caretive

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Table 2. Results of the technology acceptance model (TAM) questionnaire from prior studies evaluating user acceptance of CDSSs.

a

Study

Buenestado et al (2013) [26]Heselmans et al (2012) [27]Peleg et al (2009) [28]

CDSS description

Computerized clinical guidelines
Reminders and alerts for evidencedand protocols for asthma in children based guidelines

Guideline-based decision support
system for diabetic patient footproblems

Participant description

8 pediatricians

39 Dutch-speaking family physicians

8 family physicians

Likert scale scorea, mean (SD)Seven-point scale

Seven-point scale

Five-point scale

Perceived usefulness

5.80 (1.24)

4.00 (1.37)

4.00 (0.71)

Perceived ease of use

6.17 (0.92)

5.02 (1.41)

4.40 (0.59)

Attitude toward using

6.21 (0.59)

4.84 (0.97)

N/A

Behavioral intention to use

5.71 (1.24)

5.91 (1.33)

4.88 (0.23)

The scores are based on a Likert scale (1=totally disagree; 5 or 7=totally agree).

Task Analysis
Task analysis is conducted to stay updated with the changing
professional practice (ie, health information technology) [29].
Task analysis applied to representative populations strengthen
health systems by systematically evaluating the skills,
knowledge, and behavior of clinicians that impact clinicalpractice [30]. The use of CDSS in health care has introduced
new dynamics to practice and requires task analysis tounderstand the perception of users to this new technology. For
that reason, conducting a task analysis will improve adoption
levels. A task analysis includes goals, input, process, and output.
The next sections discuss the purpose of each task analysis
stage.

Goals
The goal of a CDSS is to supplement the physician as the sole
information processor in clinical decision making and thereby
aid in the reduction of medical errors. Yet, there is still muchroom for improvement. In part, this shortcoming may be dueto the lack of physician acceptance of the CDSSs in
supplementing their decision making. To get a better
understanding of the challenges in creating clinical decision
processes, we first consider what information goes into thisprocess.

Input
A CDSS is based on an input-process-output (IPO) model. The
inputs for the CDSS process include patient-specific informationsuch as diagnoses, medications, symptoms, laboratory data,demographics, and other clinically relevant information. The
inputs for knowledge-based CDSSs are often determined byclinical guidelines, whereas non-knowledge-based CDSSs use
the most relevant information assessed by algorithm
performance.

Process
The CDSS process takes two different forms: knowledge based
and non-knowledge based [31]. Knowledge-based systems aregoverned by a set of rules. Non-knowledge-based systems, on
the other hand, use a computer as the central processing unit tolearn from historical information. As a result, these systems
typically utilize machine learning algorithms.
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When CDSSs offer clinical suggestions, the support, evidence,clinical guideline, or algorithm for those suggestions is notprovided. The inputs for knowledge-based CDSSs are often
determinedbyclinicalguidelines,
whereasnon-knowledge-based CDSSs use the most relevant informationassessed by algorithm performance. In both cases, the physician
is not aware of the inputs or processes the CDSS utilizes. Thus,
the CDSS is a black box to the physician.
Physicians make clinical decisions based on the same patient
information in addition to social structures (acceptable behavior
as determined by peer groups), institutions (the requirement to
act according to mandated practices), and individual morality
in decision making. One can conjecture that difficulties arise
when automating such a complex network of inputs that could
never by fully encapsulated or realized by a machine.

OutputThe output from CDSSs and physicians are a result of themethods employed for processing the inputs. The output may
be a diagnosis, procedure, prescription, etc. Ideally, in conditions
where the computer and the physician are presented with thesame information, the output from the CDSS should mirror thephysician’s decision.
The level of control the CDSS has with regards to the output isinversely related to the level of control the user has over the
output. A CDSS can be passive in situations where they only
“highlight” information for the user, but do not request
acknowledgment or action [32]. An example would bepresenting abnormal laboratory values in a red font and normal
laboratory values in a black font. Active CDSSs act
independently and provide suggestions to guide the physician’s
behavior [32]. An example would be a system that provides
diagnostic assistance. The type of output then depends on thegoal orientation of a task (eg, diagnoses, medication alerts, andclinical guidelines for preventive care).

Knowledge
Physicians are more likely to accept a CDSS if the system
matches their own decision-making processes. Forster [33]described how humans quickly act on information by using
bounded and ecological rationality. Bounded rationality is basedon the use of simple heuristics, allowing for fast, real-time
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decision making [34]. Ecological rationality is based on rational
beliefs of things in a given environmental setting whereconditions are fluid. Forster [33] argued that both bounded andecological rationality need to be present in machine learning tomimic the human decision processes.
Incorporating these two approaches into CDSSs can bechallenging. Even though the heuristics that mediate decision
processes are simple; the complexity of the cognitive
infrastructure underlying heuristic operations can be difficult
to implement. Still, Forster [33] argued that machine learningalgorithms can be improved by incorporating the principles ofbounded and ecological rationality. To carry out this task,
Forster [33] suggested that a decision-making machine shouldhave (1) a set of ad hoc rules (or biases) to act on and (2) a setof ecologically viable environmental factors to consider.
Clark [35] extended this idea of mediating decision processes
by bounded and ecological rationality through a concept hereferred to as scaffolding. Clark [35] posited that humanreasoning involves three aspects: (1) individual reasoning castby some form of fast, pattern-completing style of computation(ie, bounded rationality); (2) substantial problem-solving work
offloaded onto external structures and processes (eg, social andinstitutional structures); and (3) public language used as a meansof coordinating social structures and mediating individual
thought. Thus, decision making and cognition are largely
dependent on the capacity to dissipate reasoning throughout theenvironment to reduce individual workload.
Holland and colleagues [36] added additional elements that canbe useful to understand physician decision making. Theseelements provide a cognitive framework for problem solving,which includes two distinct schemas: pragmatic reasoning
schema and problem schema. Pragmatic reasoning schemas are
clusters of abstract inferential rules that characterize relations
over general classes of object kinds, event relationships, andproblem goals. Problem schemas are used by experts to solve
routine problems, where an expert retrieves an appropriate
problem schema and provides it with problem-specificparameters.
The system must also have two types of knowledge structures:
mental models and condition-action rules. Holland andcolleagues [36] assert that “mental models are transient, dynamic
representations of particular, unique situations. They exist only
implicitly, corresponding to the organized, multifaceted
description of the current situation and the expectations thatflow from it.” A condition-action rule can be thought of as an
IF (condition)...THEN (action) statement. Together, theseknowledge structures allow the mental schemas to operate inorder to solve problems.
To successfully implement and use CDSSs, these mental models
have to be identified. Hayek [37] stated that knowledge is not
given to anyone in its entirety. This statement legitimizes why
CDSSs are so important. In theory, CDSSs lessen the cognitive
resources a physician needs to make decisions.

Constraints
The major limitation of CDSSs is that scaffolding cannot be
fully captured by computers. The environmental, clinical, andhttp://medinform.jmir.org/2018/2/e24/

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Khairat et alsocial constraints in which physicians practice are difficult toinclude as inputs into a CDSS. In addition, reproducing aphysician’s tacit knowledge through mental models andcondition-action rules is a formidable objective. Additionally,physicians must be able to support their decision and areskeptical of recommendations or claims that lack supporting
evidence or transparency. The fact that CDSSs do not reveal
how output decisions are made may be a driving force behindthe lack of users’ acceptance.

Discussion
Means for Solving User Acceptance of Clinical Decision
Support Systems
Studies have revealed that responses to CDSSs can be
unfavorable when resulting improvements in patient outcomes
are inconsistent [6]. Also, some studies have reported incidentsof patient harm associated with CDSS implementation [38].
Despite these findings, limited research has formally evaluatedthe impact of user acceptance. Based on our comprehensive
review of the literature, we have found both favorable andunfavorable user acceptance to CDSSs.
If a user finds a product frustrating or perceives that the purposeof the product is to limit autonomy, the user may not use theproduct or do so inappropriately [39]. Vashitz et al [40] explains
the consequence of loss of autonomy as reactance. Reactanceis an unpleasant motivational state whereby people react tosituations to retain freedom and autonomy. Reactance may exist
when physicians feel threatened by clinical reminders for fearthat they are losing autonomy and freedom of choice in thepresence of such systems. Physicians may have the perceptionthat these systems are meant to replace or degrade their clinicalduties. Vashitz et al [40] describe how unsolicited advice maylead to a reactance state if the advice contradicts a person’s
original impression of choice options.
Based on the UTAUT, user expectations need to be taken into
consideration for technology to be accepted [12]. Therefore, inthe design of CDSSs, the human element cannot be ignored.
Reminders and alerts should be presented in such a way thatthe user does not find them threatening or obtrusive. User needs
and expectations of a CDSS should be evaluated early andthroughout the development lifecycle. For instance, Gadd [18]observed enhanced usability and usefulness by implementing
usability testing in the early phases of CDSS development. Theyevaluated an evolving prototype of the system and observed
user interactions over a 3-month period. In a series of sessions,they focused on evaluating user interactions with different setsof system features such as screen layout, input/output, and links
to educational materials. Finally, they considered the user
feedback on system recommendations in the design process.
Compelling suggestions for system enhancements made byusers during the earlier sessions influenced system developmentof features that were evaluated in later sessions.
Peleg et al [28] discussed the development process of their
CDSS, where clinically knowledgeable users worked alongside
the developers to design and implement the CDSS. They also
used a lifecycle model user-centered design and evaluation
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process for evaluating the users’ goals/expectations, workflow,
environmental constraints, and tasks. Finally, they conductedusability testing (ie, heuristic evaluation of user-interface,
keystroke-level modeling, and cognitive walkthroughs) prior
to implementation.
Developers of CDSSs have attempted to bottle-up thedecision-making capacity of physicians and place thatknowledge into a computer. Current methods to achieve thisfeat employ rules and machine learning algorithms. However,the lack of user acceptance has impeded CDSS use. Research
has shown that consideration of users’ needs and expectationsin the design of the CDSS may help overcome this obstacle.
We argue that this approach is only part of the solution.
We propose that CDSSs move away from the black-box process
to a more transparent method within the IPO model. Simply
put, tell the physician how the computer is making the decision.
If the computer can become part of scaffolded knowledge, thephysician may view the computer as an aid rather than a threator hindrance. Research supports the idea that the rules governing
alerts be specified to practitioners and the information bepresented based on users’ needs and expectations [41].

Proposal of Models to Gain User Acceptance
We propose two models to improve CDSSs development that
may lead to increased utilization resulting in improved patient
outcomes. First, is the user acceptance and system adaptation
design (UASAD) model that aims to involve end users early inthe design and throughout the development of CDSSs. Second,is replacing the current IPO model of CDSS development with
the input-process-output-engage (IPOE) model that serves to“engage” the physician through CDSS process transparency.
The UASAD model demands early end-user involvement in
CDSS development. User needs and expectations need to be
fully realized prior to the development of a CDSS. Anotherconsideration is to evaluate system preparedness to ensure thatusers can trust the security and privacy of the system. Prototypic
designs should undergo an iterative design process following
rigorous usability testing in a laboratory and natural setting (ie,pilot study) to ensure that the system works within the cognitiveand environmental constraints with which the user functions.
Finally, user acceptance should be evaluated to ensure that thesystem is used appropriately. If user acceptance is not achieved
above a predefined threshold, the CDSS should be reevaluatedfrom the point of view of user needs and expectations. It should
also be subjected to adaptive redesign. This process should

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iterate until user acceptance exceeds a predefined threshold. To
illustrate this process, we have developed a UASAD model(Figure 2). The purpose of the model is to include the user as
the focal point of the design process of CDSS.
The IPOE model offers users a window into the black-box IPO
process. Through “engage” physicians will see how the CDSS
is making decisions. The IPOE window will be called “engage”
because it will present users with the rules that the machine
followed to generate the output (Figure 3). Therefore, the user
can make informed decisions when determining to accept or
deny outputs. “Engage” will display the input, process, andoutput that led to the CDSS’s decision. The physician will thenbe able to evaluate the relevancy, validity, supporting evidence,and strength of a recommendation. Therefore, this system
becomes a component of the physician’s scaffolded knowledge
and enables them to act more confidently in accepting thetechnology and its role in their decision-making processes.
A limitation of the IPOE model is that in order for the model
to work successfully, the physician has to understand theprocess. Processes that utilize a machine learning algorithm,
such as neural networks, do not provide rules. Therefore, it ischallenging to make all processes transparent.

Why Do We Make Bad Decisions?
Physicians’ tendencies to incorrectly process challenging
decisions usually lead to bad clinical decisions. Most practicing
physicians tend to make decisions out of their own medical
experience, whereas others pursue medical consultations andfiltering through the jargon of relevant research. The mosteffective physician, though, is the one who has the ability toutilize his clinical judgment coupled by the computerized
decision support tools to leverage the power of CDSSs. Most
clinicians exhibit bias when it comes to medical informationthat they know and, therefore, they typically focus on things
that would agree with the specific clinical outcome that theywant to see in their patients. Therefore, the context of using a
CDSS is mandated by the efforts to decrease medical errors by
utilizing existing knowledge and technology. These systems
are a result of long-term scientific research to build efficient
tools for physicians to supplement their clinical experience.
Physicians should look at CDSSs as an added value to makethe best decisions in their day-to-day practice and to better serve
their patients. These systems seek to reduce medical errors byenabling the practicing physicians to make informed decisions
that are both accurate and precise.

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Figure 2. A user acceptance and system adaptation design (UASAD) model. CDSS: clinical decision support system; UTAUT: unified theory ofacceptance and use of technology.

Figure 3. The input-process-output-engage (IPOE) model.

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Conclusion
Implementation of CDSSs has demonstrated increased
efficiency, reduced medical errors, and improved outcomes, butthey continue to fall short of their full potential [2-9]. We believe
this key shortcoming may partly be due to the lack of physician
acceptance. In the past, CDSS designs have not incorporated
input from physicians and do not reveal their decision-making
processes. Consequently, many physicians are hesitant to accept
CDSSs leading to suboptimal implementation. Here we propose
two models for designing CDSSs with the goal of improving
efficacy and physician acceptance. One model, UASAD, focuses

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on including the physician in the design process by examining
user needs and expectations and usability of prototypic designs.
The other model, IPOE, extends the existing IPO framework
by adding an “engage” stage that displays the CDSS process tothe physician. This approach allows the physician to include
the CDSS as a component of their decisions while maintaining
professional autonomy. There is still considerable work to bedone for validating these models, yet user acceptance appears
to be pertinent for successful CDSS use. Ultimately, if aphysician does not accept the technology, it not only poses athreat to the use of the technology but can also pose a threat tothe health and well-being of patients.

Acknowledgments
The authors would like to express appreciation to Dr Marla Broadfoot, PhD, and Ms Victoria Rand for their contribution to thispaper.

Conflicts of Interest
None declared.

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Abbreviations
CDSS: clinical decision support systems
IPO: input-process-output
IPOE: input-process-output-engage
TAM: technology acceptance model
UASAD: user acceptance and system adaptation design
UTAUT: unified theory of acceptance and use of technology

Edited by G Eysenbach; submitted 06.09.17; peer-reviewed by Y Gong, R Agrawal, L Zhou, T Saheb, KC Wong, R Robinson; comments
to author 23.11.17; revised version received 02.03.18; accepted 19.03.18; published 18.04.18
Please cite as:
Khairat S, Marc D, Crosby W, Al Sanousi A
Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis
JMIR Med Inform 2018;6(2):e24
URL: http://medinform.jmir.org/2018/2/e24/
doi: 10.2196/medinform.8912
PMID: 29669706©Saif Khairat, David Marc, William Crosby, Ali Al Sanousi. Originally published in JMIR Medical Informatics(http://medinform.jmir.org), 18.04.2018. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete
bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license
information must be included.

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