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Impact of Electronic Health Record Use on Cognitive Load andBurnout Among Clinicians: Narrative Review
Elham Asgari1,2, MD, MSc, PhD; Japsimar Kaur3, MBBS; Gani Nuredini4, MBBS, BMedSci; Jasmine Balloch2, BSc,
MSc; Andrew M Taylor5, MBBS, Prof Dr; Neil Sebire5, MBBS, MD, Prof Dr; Robert Robinson5, MBBS, MA, PhD;
Catherine Peters5, MBChB, MD; Shankar Sridharan5, BSc, MBBS; Dominic Pimenta2, MBBS, BSc
1

Guy's and St Thomas' NHS Trust, London, United Kingdom

2

Tortus AI, London, United Kingdom

3

Manchester University NHS Foundation Trust, Manchester, United Kingdom

4

Barts Health NHS Trust, London, United Kingdom

5Great Ormond Street Hospital, London, United Kingdom

Corresponding Author:
Elham Asgari, MD, MSc, PhD
Tortus AI
193-197 High Holborn
London, WC1V 7BD
United Kingdom
Phone: 44 7763891802
Email: asgelham@gmail.com

Abstract
The cognitive load theory suggests that completing a task relies on the interplay between sensory input, working memory, andlong-term memory. Cognitive overload occurs when the working memory’s limited capacity is exceeded due to excessive
information processing. In health care, clinicians face increasing cognitive load as the complexity of patient care has risen, leading
to potential burnout. Electronic health records (EHRs) have become a common feature in modern health care, offering improved
access to data and the ability to provide better patient care. They have been added to the electronic ecosystem alongside emails
and other resources, such as guidelines and literature searches. Concerns have arisen in recent years that despite many benefits,
the use of EHRs may lead to cognitive overload, which can impact the performance and well-being of clinicians. We aimed toreview the impact of EHR use on cognitive load and how it correlates with physician burnout. Additionally, we wanted to identify
potential strategies recommended in the literature that could be implemented to decrease the cognitive burden associated withthe use of EHRs, with the goal of reducing clinician burnout. Using a comprehensive literature review on the topic, we have
explored the link between EHR use, cognitive load, and burnout among health care professionals. We have also noted key factorsthat can help reduce EHR-related cognitive load, which may help reduce clinician burnout. The research findings suggest that
inadequate efforts to present large amounts of clinical data to users in a manner that allows the user to control the cognitive burdenin the EHR and the complexity of the user interfaces, thus adding more “work” to tasks, can lead to cognitive overload andburnout; this calls for strategies to mitigate these effects. Several factors, such as the presentation of information in the EHR, thespecialty, the health care setting, and the time spent completing documentation and navigating systems, can contribute to thisexcess cognitive load and result in burnout. Potential strategies to mitigate this might include improving user interfaces, streamlining
information, and reducing documentation burden requirements for clinicians. New technologies may facilitate these strategies.
The review highlights the importance of addressing cognitive overload as one of the unintended consequences of EHR adoption
and potential strategies for mitigation, identifying gaps in the current literature that require further exploration.
(JMIR Med Inform 2024;12:e55499) doi: 10.2196/55499
KEYWORDS
electronic health record; cognitive load; burnout; technology; clinician

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Introduction
Sweller [1] defined cognitive load as “the amount of mental
effort required to process and store information in working
memory.” The cognitive load theory argues that completing atask requires a complex interplay between sensory inputs,
working memory, and long-term memory [1]. The working
memory helps interpret the sensory input and then commits
processed information into long-term memory. This

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psychological theory stipulates that while both sensory andlong-term memories can handle large volumes of input data,
working memory has a comparatively limited capacity and cankeep 3 to 5 items in mind at a time [2]. When the amount of
information exceeds this given capacity, it leads to cognitive
overload. For any given task, there are 3 main factors that
contribute to its perceived cognitive load: intrinsic, extraneous,and germane [1]. Figure 1 provides a description of eachcognitive load with examples of how they are affected whenclinicians use electronic health records (EHRs).

Figure 1. Factors contributing to cognitive load and examples of how they are affected when clinicians use EHRs. EHR: electronic health record.

Physician burnout refers to a state of chronic physical andemotional exhaustion experienced by clinicians, often due toprolonged stress and heavy workloads. It is a growing concern
in the medical sector, both in the United Kingdom and globally,
and it affects individual health care professionals as well as thehealth care system as a whole. Key aspects include emotional
exhaustion, demoralization, and a reduced sense ofaccomplishment [3].
Contributing factors to burnout include unremitting workloads,
administrative burdens, emotional stress, work-life imbalance,
the lack of autonomy, and insufficient support systems.
Physicians who work long hours and have high patient loads
can experience physical and mental fatigue. Additionally,administrative tasks, paperwork, and EHR requirements add tothe workload, causing frustration [4].
According to a report from the World Health Organization, theaverage life expectancy globally has risen by 6 years, from 66.8
years in 2000 to 73.4 years in 2019 [5]. It is estimated that 1 in
3 adults have multiple long-term conditions [6]. Withadvancements in science and technology, there are now morediagnostic and treatment options available, which require
additional monitoring and follow-up. Consequently, physicians
face an increased cognitive load due to the larger volume ofhttps://medinform.jmir.org/2024/1/e55499

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information they need to process from complex patients todeliver high-quality care.
Simultaneously, the extraneous load experienced by clinicians
is influenced by the presentation of this information. When
information is disorganized, unnecessary, or incomplete,clinicians are exposed to a higher extraneous load, placing agreater demand on their working memory [7], which in turn has
a knock-on effect on the germane load. This effect is mostpronounced among early-career clinicians with a significant
amount of new material to learn [7].
Working memory is also attenuated in the presence ofphysiological or emotional stress [8], which in recent times has
become increasingly more common among clinicians following
the COVID-19 pandemic, leading to widespread burnout.
Although EHR systems are widely used in health care settings
worldwide, there are not enough comprehensive studies
evaluating their advantages and disadvantages or examining
how they can be improved. In a systematic review, Moy andcolleagues [9] aimed to identify the studies on physician andnursing burnout related to using EHR systems. They found that
40% of the 35 studies meeting their inclusion criteria had
mentioned clinical burnout. However, they noted a lack ofstandardized and validated measures to assess the documentation
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burden related to EHR use. There is also a lack of objective
measures to assess the cognitive load associated with using
EHRs.
In summary, the increasing cognitive load experienced byphysicians is a sum of the increasing complexity of theinformation presented, the quality and clarity of that
presentation, as well as the emotional and psychological context
in which the information is received [1]. The usability of EHRs,
including their design, interoperability, and various regulatory
requirements, impacts this cognitive burden. By working
constantly above the cognitive load threshold, clinicians may
exhibit symptoms of cognitive overload, which is considered
an immediate precursor to burnout [10]. This narrative review
examines the existing literature on cognitive overload
experienced by health care professionals, specifically in relation
to their use of EHRs and the associated burnout. The review
also explores potential solutions that could help reduce the
EHR-related cognitive load and improve the well-being ofclinicians.

Use of EHRs in Health Care
The digitization of health care has been a growing trend overthe past decades, which has seen most patient data transferredfrom paper records to EHRs. The use of EHR dates back to the
1960s but was only limited to government use [11]. Since the1970s, EHR systems have been developed with hierarchical or
relational databases for various indications, such as to help withhospital billing and scheduling systems, to help improve medicalcare, and for use in medical research. As computers havebecome more accessible, larger health care organizations havebegun using them to gather patient information [12]. This shift
has gradually expanded to encompass nonclinical tasks such asadministration, medicolegal work, research, and education [13],which have also increased in demand and complexity over time.
Poor physician acceptance and the lack of incentives, in addition
to high costs and errors associated with data entry, hindered
EHR implementation uptake in the 1990s, and thus, digitalrecords were not widespread [14]. By the 2000s, countries suchas the United States and the United Kingdom began
implementing national projects to digitize paper records [12].
The UK government attempted to create the National
Programme for Information Technology [15] in 2002 to createa universal EHR system for the entire United Kingdom. The
nationwide initiative was centered around 3 main objectives:
lifelong EHRs, 24/7 web-based access by public health care
professionals, and seamless information sharing throughout all
sectors of the National Health Service [16]. The project failed
to meet its objectives, and digitization became fragmented andslow once again [17]. However, by 2022, a total of 86% of the
UK hospital trusts had successfully transitioned from paper
notes to a digital system (although only a minority haveenterprise-wide EHR capability), with the figure expected toreach 90% by December 2023 [18]. In the United States, theHealth Information Technology for Economic and Clinical
Health Act was signed into law in February 2009 to promote
the adoption of and meaningful use of health information
technology (HIT) as part of the American Recovery andhttps://medinform.jmir.org/2024/1/e55499

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Reinvestment Act. Financial incentives that were allocated aspart of the Health Information Technology for Economic andClinical Health Act led to a significant increase in the adoptionof EHRs in the United States [14].

Factors Impacting the Usability of EHR
Systems
The usability of EHR systems continues to be a major concern,
whereby clinicians are subjected to too much or too little
information, preprogrammed workflows, and multiple alerts[19]. There have been problems with chaotic, nonintuitive visualdisplays and numerous default settings that might not be relevant
for a given task or patient [20]. Navigating through the same
information includes unnecessary steps, for example, multiple
clicks and duplicated information [21]. Users experience higher
fatigue, leading to potential room for errors and decreased
efficiency [22,23]. In addition to documentation and chart
review, managing inbox tasks has been noted as one of thesignificant burdens for clinicians [24]. Receiving excessive
notification has been shown to cause alert fatigue, leading tomissing important information and poor patient outcomes [25].
Although clinical decision support systems have been introduced
to enhance patient care, excessive use of interruptive clinicaldecision support systems in the EHRs can lead to alert fatigue
and reduced effectiveness. Chaparro and colleagues [26] havedescribed how interruptive alerts can increase cognitive burden
and lead to reduced acceptance of the alert and an increase in
the number of errors.
Several factors have been highlighted as contributing to the useof EHR and physician well-being. Nguyen and colleagues [27]studied this in a systematic review, where they found that
EHR-related physician well-being is determined by multiple
factors, including EHR usability, EHR system features, andphysician-level characteristics and beliefs.
The sheer volume of data that a physician can access during a
specific clinical encounter proves challenging [28]. As an
example, Hill and colleagues [29] found that emergency healthcare physicians see an average of 2.4 patients per hour and use
4000 mouse clicks in a 10-hour shift. This can result from a
combination of poor EHR design and information overload andadds to physician stress [30,31]. Information overload is a partof the 5 main hazards of “information chaos” alongside
information underload, information scatter, information conflict,and erroneous information [32]. Clinicians are then required tospend more effort to filter through the information, clarify
conflicting documentation, or reassess potentially erroneous
information, leading to excess workload and adverse outcomeson not only patient care and health systems but, more
importantly, clinician well-being [33].
Gal and colleagues [34] studied this in a pediatric intensive careunit where they calculated that each patient generated an averageof 1460 new data points in a 24-hour period. Pediatric intensive
care unit attending physicians cared for an average of 11 patientsduring the day and 22 patients overnight, resulting in exposure
to 16,060 (range 11,680-18,980) and 32,120 (range

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23,360-37,960) individual data points during the day and night,
respectively.
Wanderer and colleagues [35] have described how optimal datavisualization in various specialties can lead to improved
decision-making for clinicians and more efficient use of theirtime. Many EHR vendors use visual analytic systems to improvephysician workflow and reduce medical errors [36].
Blink rate, measured using eye-tracking technology, has been
associated with cognitive workload. Visual tasks that require
more focused attention and working memory load have beenshown to reduce blink rate [37]. A decreased blink rate has beenfound to occur in EHR-based tasks that require more cognitive
workload [38].
The NASA Task-Load Index (NASA-TLX) is a widely used
questionnaire to assess perceived workload (available inMultimedia Appendix 1) [39]. It consists of 6 questions, which
can be rated from 1 to 10. Nurses rated their perceived workloadfrom 0 (very low) to 10 (very high).
Using blink rate in addition to the NASA-TLX, Mazur andcolleagues [40] tested the implications of the EHR usability
interface in a study where they assigned tasks, including thereview of medical test results for 20 and 18 individuals using
baseline and enhanced EHR versions, respectively, that provided
policy-based decision support instructions for next steps.
Interestingly, they found that the baseline group had poorer
performance and higher cognitive load compared with those
who used the enhanced version, suggesting the importance ofimproving the usability of EHRs to address issues such as
clinician burnout and patient safety events.
Harry and colleagues [7] studied the direct relationship between
cognitive load with physician burnout in a national sample of
US physicians. Using the NASA-TLX, they had responses from4517 (85.6%) of the 5276 physicians included in the survey.
The median age of the physicians was 53 years; 61.8% weremale, 37.9% were female, and 0.3% were other gender; and 24
specialties were identified. They identified a dose-response
relationship between physician task load and the risk of burnout
independent of age, gender, practice setting, and hours worked
per week.
To demonstrate a more accurate association between EHR use
and stress, Yen and colleagues [41] used blood pulse wave
monitoring (previously used as a surrogate for chronic stress)
in addition to NASA-TLX on 7 nurses during 132 hours ofwork. They found that the nursing staff spent 45.54 minutes
using EHR during a 4-hour shift, which was much more than
the time spent on any other communication or hands-on
activities. In addition, the nurses’ stress when using EHR was
associated with higher perceived physical demand andfrustration.
The level of EHR-related burnout has also been shown to be inpart influenced by physician specialty. In a large study that usedassessing questionnaires among physicians in various specialties
with over 25,000 respondents, the investigators found the levelof burnout ranged from 22% to 34% by specialty [42]. The
specialties with the highest levels of burnout were family
medicine (34%) and hematology or oncology (33%). The
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specialties with the lowest levels of burnout were psychiatry(22%) and anesthesiology (24%). After adjusting forconfounding variables, physicians with 5 or fewer hours ofweekly out-of-hours charting were twice as likely to reportlower levels of burnout than those with 6 or more hours. Those
who agree that their organization has performed well with EHR
implementation, training, and support were also twice as likely
to report lower levels of burnout than those who disagreed. Thishighlights the importance of training and support following theimplementation of EHR for their optimal use.
In a scoping review, Muhiyaddin and colleagues [43] studied
the causes and consequences of physician burnout related to theuse of EHRs. Reviewing 30 eligible studies out of 500, they
identified 6 main causes that are related to physician burnout,
including EHR documentation and related tasks, poor designof EHR systems, workload leading to overtime work, inbox
alerts, and alert fatigue. Not surprisingly, physician burnout
was associated with a low quality of care, behavioral issues,
and mental health complications, as well as career dissatisfaction
and a reduction in patient safety and satisfaction.
In a survey of 640 clinicians from 3 institutions, with 282(44.1%) responses to 105 questions, Kroth and colleagues [30]identified 7 EHR design and use factors associated with highstress and burnout. These were information overload, slow
system response times, excessive data entry, inability to navigate
the system quickly, note bloat, interference with thepatient-clinician relationship, fear of missing something, andnotes geared toward billing.
Another study [44] aiming to quantify burnout due to the useof HIT used a survey sent to 4197 physicians, where 1792
responded (response rate: 42.7%). They found that HIT-related
stress was measurable, prevalent, and specialty related. About
70% of physicians with EHRs experienced HIT-related stressin their sample, and the presence of any of the 3 HIT-related
stress measures independently predicted burnout symptoms
among respondents. In particular, those with time pressures for
documentation or those doing excessive “work after work” ontheir EHR at home had approximately twice the odds of burnout
compared to physicians without these challenges. Time spent
after hours on the EHR and the volume of inbox messages havebeen found to relate to physician exhaustion [45].
Using live observational design and NASA-TLX surveys,
Khairat and colleagues [46] assessed the effect of EHRs on
emergency department attending and resident physicians’
perceived workload, satisfaction, and productivity throughcompleting 6 EHR patient scenarios. They found that EHR
frustration levels are significantly higher among more seniorattending physicians compared with more junior resident
physicians. Among the factors causing high EHR frustrationsare (1) remembering menu and button names and commands
use; (2) performing tasks that are not straightforward; (3) system
speed; and (4) system reliability.

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Advantages and Disadvantages of Using
EHRs
Overview
As highlighted in the previous section, despite their potential
benefits, there have been growing concerns that EHRs also havedetrimental effects. Here, we summarize some of the advantages
and disadvantages of using EHRs.
Information overload is a significant concern when using EHRs
[47-49]. Various studies also suggest a correlation between theusability of the EHR and cognitive load and burnout among
clinicians [34,50-52]. Clinicians feel that work-life balance,
satisfaction rates, attrition, and burnout are all affected due tothe continuous daily interaction with EHR systems [22,53-55].

Advantages
The transition from paper-based medical records to EHRs has
been perceived as a positive development in several areas [9,56].
In addition to being easily accessible, EHR systems have beenshown to improve communication between clinicians andenhance the continuity of care [57,58]. They can lead tobetter-informed decisions due to the availability of data andavoid the duplication of diagnostic testing [59]. However, areview of the impact of EHR use on enhancing medication
safety, one of the biggest risks to patient care, has shown only
modest improvements [60].
EHRs also contain a high volume of clinical data, providing uswith multiple opportunities to conduct research and audit[13,59]. A good example of this in the United Kingdom is
OpenSAFELY, a secure, transparent, and open-source software
platform for the analysis of EHR data [61]. During the
COVID-19 pandemic, scientists and statisticians could use thedata available on this platform to provide insights into
population demographics most at risk of death following
COVID-19 infection, which aided with the national policy
strategy for prioritizing care [62-65].

Disadvantages
Over the past decade, there has been a reported increase inburnout levels among clinicians, with one potential factor being
the introduction of EHR systems [23,34]. The introduction of
EHRs has resulted in changes in workflow, with frontline
clinicians taking on administrative tasks such as ordering tests,

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correcting notes, and placing referrals. This has led to increased
cognitive load, which is often overlooked [66,67].
On a day-to-day basis, clinicians face time constraints;
administrative load; and consequently, elongated workdays.
The current documentation methods used in EHRs are under
scrutiny by clinicians due to the perceived poor quality of userinterfaces, ultimately leading to burnout [52]. Factors such asincreased structured documentation requirements, physician
order entry, inbox management, and patient portals contributeto more work that is not direct face time with patients [19,68].
Inflated documentation also extends to the excessive use of
templates and copy-and-paste workflows in EHR systems that
introduce data that are neither required nor accurate [48,49,69].
EHRs allow information to be copied from almost anywhere in
the record to another section. This can save time and allow
clinicians to focus on clinical tasks rather than documentation;
however, it comes with its own challenges. Erroneous
information can be copied and pasted without editing, leading
to data integrity issues and diagnostic errors [70]. This also
creates room for false assumptions and inferred incorrect
information between different health care professions,perpetuating previous inaccuracies. It might also sanction juniorclinicians to rely solely on readily available information rather
than conducting a thorough history and examination forthemselves and constructing their own differential thoughtprocesses [71].
Although thorough documentation is key for clinical care, there
has been a rise in complex and lengthy documentation of content
that is required for billing purposes, quality improvement
measures, avoiding malpractice, and signs of compliance [72].
In countries such as the United States, the regulatory
requirements for data entry beyond what is required for direct
patient care can contribute to an increasing workload [73].
Examples of these include collecting data for claim submission,prior authorization, billing, and quality reporting. In addition,a lack of interoperability between EHR systems can result inclinicians not having access to adequate patient information andfragmented care [74]. Often the clinical needs to spend asignificant amount of time to obtain this information fromvarious medical records between different health careorganizations and sometimes even within one facility. Textbox
1 summarizes some of the advantages and disadvantages ofusing EHRs.

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Textbox 1. Advantages and disadvantages of using electronic health records (EHRs).
Advantages of using EHRs•

Improved communication between clinicians•

Remote access to clinical records enhances care delivery•

Convenient access to patient information for clinicians•

Facilitates research and audit through a high volume of clinical data storage

Disadvantages of using EHRs•

Information overload leading to cognitive overload•

Increased cognitive load due to EHRs can contribute to feelings of exhaustion and burnout•

Continuous interaction with EHRs affects work-life balance and may lead to burnout•

Complex and lengthy documentation required for billing and quality reporting can be cumbersome•

Poor quality of user interfaces in EHRs leads to clinician burnout•

Excessive use of templates and copy-and-paste workflows can lead to data integrity issues•

The lack of interoperability between EHR systems can lead to missed information and duplication of investigations

Overcoming EHR-Related Burnout
Health care organizations and policy makers worldwide are
increasingly recognizing the importance of addressing clinician
burnout [75]. Here, we have summarized some of theinterventions recommended in the literature that can reduce
various types of cognitive load and potentially clinician burnout
related to EHR use.
Dymek and colleagues [24] have made a case for producing anevidence base to reduce EHR-related clinician burden.
Describing documentation, chart review, and inbox tasks assome of the key contributing factors causing burnout, they havemade suggestions to help overcome these challenges. Some ofthese approaches include using speech recognition software andnatural language processing to help with documentation andthe generation of progress notes; the use of natural language
processing and machine learning to process, filter, and rankpatient information so that the attention can be paid to where it
is most needed; and the use of better inbox design by involving
clinicians in their development. Understanding the workflow
of the clinicians and involving them in the design of the EHR
have been shown to positively impact its usability and user
satisfaction [76].
Several studies have reviewed alert burden in EHRs anddescribed potential solutions on how to manage them effectively.
One of the very interesting and useful recommended suggestions
is developing an Interruptive Alert Stewardship by implementing
metrics to assess the alert burden and their effectiveness in
improving outcomes [26]. McGreevey and colleagues [77] havecomprehensively described reasons for alert fatigue and suggestthat organizations develop an alert governance specific to theirneeds. They propose that key stakeholders including clinicians,
informatics, information technology, and administration groups
need to participate in developing the alert governance andoversee the design and purpose of alerts. They also recommend
using a checklist to assess the purpose and justification of alerts
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efficiency. Organizations such as Geisinger Health System and
Penn Medicine have successfully improved their EHR alert tohelp with clinician well-being [77].
Clinicians have specific and feasible suggestions for reducing
EHR-related burdens, such as providing high-quality EHR
training; having an on-site EHR support team; involving support
staff or scribes in the documentation process; and, importantly,
obtaining physician input and feedback in improving EHRs
[27]. Future efforts should focus on implementing the strategies
and upgrades requested by these frontline users.
In a recent systematic review, Kang and Sarkar [78] looked atinterventions that have been used to reduce EHR-relatedburnout. The study identified 3 primary interventions, including
the use of scribes, EHR training, EHR modifications, and a
combination of training and modifications. The use of scribes
has been overall well received by clinicians and patients and,in some cases, led to increased productivity, but there were
downsides, in particular, the cost, which would be difficult toovercome in smaller centers. EHR training had varying
outcomes, with some studies showing a reduction in
documentation time, whereas others did not demonstrate thisbenefit. Nevertheless, subjective EHR proficiency increased,
which could help improve clinicians’ perception of EHR.
The study [78] has also examined several EHR modification
techniques, such as data entry automation technology, improving
EHR workflows, reducing unnecessary inbox alerts, andproviding support teams to resolve EHR issues promptly. These
interventions resulted in positive outcomes, such as a reduction
in documentation time ranging from 18.5 to 60%, improved
documentation quality and completion rate, decrease in data
errors, and subjective EHR usability and satisfaction. However,these positive effects did not lead to a significant reduction inphysician burnout. The authors suggest that this could be dueto the fact that although EHR enhancements can improve some
aspects of the clinician’s workflow, they probably do not address
the defects in the EHR usability, which contribute to burnout.
In addition, there are other factors contributing to burnout, such
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as overall workload, organizational culture, and work-lifebalance that extend beyond EHR systems.
Improving interoperability and health information exchange
through understanding the barriers, appropriate incentives, andlegislation can facilitate the clinicians’ workflow, reduce
workload, and enhance patient safety [79].
In 2020, The Office of the National Coordinator for Health
Information Technology published a report outlining strategies
to reduce regulatory and administrative burden related to theuse of HIT and EHR systems [80]. The report focuses on thechallenges of EHR and HIT-related burden, which hinder theachievement of interoperability. They recognize that theseburdens increase the time and expense clinicians must invest
in interacting with EHRs, reducing the value of information,and diverting resources from patient care. They propose aframework for trusted exchange among health information
networks to reduce clinician burden while benefiting patientsand the health care system.
The National Academy of Medicine published a potential
roadmap for EHR optimization and clinician well-being [81].
They have described several strategies currently available that
can improve the usability of EHRs, such as EHR optimization,
in-basket management techniques, documentation strategies,
team-based workflow, and EHR training, as well as the use of
artificial intelligence and add-on applications that can help withinteroperability, automation, and decision support tools in the
future. Gandhi and colleagues [82] have described how the useof artificial intelligence can reduce the cognitive workload byhelping with data gathering, documentation, and decision
support. They also suggest useful methods to assess the impactof these technologies.

Gaps in the Current Literature
Given the increasingly interconnected digital ecosystem andthe complexity of health care systems, which are influenced byphysical, emotional, and human factors, it can be challenging
to attribute specific outcomes to any particular technology.
Therefore, to maximize the benefits of new tools, it is essential
to create frameworks for scientifically assessing the impact ofany technology used.

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By using user-friendly interfaces, customization options, andcontext-sensitive information presentation, EHRs can streamline
data management [83]. Incorporating decision support tools,
data visualization techniques, and smart documentation practices
further enhances health care staff’s ability to focus on patientcare, reducing the risk of errors and burnout [36]. EHR
optimization to support clinical workflow and real-life working
is a key to uplifting the well-being of health care professionals.
Addressing physician burnout requires systemic changes,
including improving work environments with a renewed focus
on teamwork, reducing administrative burdens, providing
support, and promoting work-life balance within health careorganizations.
Individual strategies, such as self-care, stress management, andprofessional pastoral help, are crucial for clinicians to mitigate
and recover from burnout [84]. This will support the well-beingof the health care workforce and ensure ongoing high-quality
care delivery for all.
As our patients and work environments become more complex
and more health technology products become available, it is
crucial that we assess their impact through studies and engaging
with our health care staff and patients throughout all stages of
their development and use [85].

Conclusion
The use of EHR systems may provide benefit for centralizing
patient data and simplifying the process of reviewing records,
requesting laboratory and imaging tests, and reviewing results,
as well as conducting clinical audits, research, and quality
improvement projects.
However, there is a noticeable difference in the quality ofvarious EHR systems health care organizations use. Many ofthese EHR systems do not communicate with each other,
keeping data isolated in silos. Our review highlights the
cognitive load that their use places on clinical staff, which is
not always considered. Improving the design, user interface,and data visualization or retrieval of EHR systems can help toreduce cognitive load, support working memory, and potentiallyreduce physician workload while enhancing patient care.

Conflicts of Interest
None declared.

Multimedia Appendix 1
NASA Task-Load Index questionnaire.
[DOCX File , 14 KB-Multimedia Appendix 1]

References1.
2.

Sweller J. Cognitive load during problem solvingffects on learning. Cogn Sci. Apr 1988;12(2):257-285. [doi:10.1016/0364-0213(88)90023-7]
Cowan N. The magical mystery four: how is working memory capacity limited, and why? Curr Dir Psychol Sci. Feb 01,
2010;19(1):51-57. [FREE Full text] [doi: 10.1177/0963721409359277] [Medline: 20445769]

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JMIR Med Inform 2024 | vol. 12 | e55499 | p. 7
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
3.

4.
5.
6.
7.

8.
9.

10.
11.

12.
13.

14.
15.
16.
17.
18.
19.

20.
21.

22.

23.
24.

25.

26.

McKinley N, McCain RS, Convie L, Clarke M, Dempster M, Campbell WJ, et al. Resilience, burnout and coping mechanisms
in UK doctors: a cross-sectional study. BMJ Open. Jan 27, 2020;10(1):e031765. [FREE Full text] [doi:10.1136/bmjopen-2019-031765] [Medline: 31988223]
West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med. Jun
2018;283(6):516-529. [FREE Full text] [doi: 10.1111/joim.12752] [Medline: 29505159]
Department of Data and Analytics: Division of Data, Analytics and Delivery for Impact. GHE: life expectancy and healthy
life expectancy. World Health Organization. 2020. URL: https://tinyurl.com/4zc9csw4 [accessed 2024-04-05]
Hajat C, Stein E. The global burden of multiple chronic conditions: narrative review. Prev Med Rep. Dec 2018;12:284-293.
[FREE Full text] [doi: 10.1016/j.pmedr.2018.10.008] [Medline: 30406006]
Harry E, Sinsky C, Dyrbye LN, Makowski MS, Trockel M, Tutty M, et al. Physician task load and the risk of burnout
among US physicians in a national survey. Jt Comm J Qual Patient Saf. Feb 2021;47(2):76-85. [FREE Full text] [doi:10.1016/j.jcjq.2020.09.011] [Medline: 33168367]
Chajut E, Algom D. Selective attention improves under stress: implications for theories of social cognition. J Pers Soc
Psychol. Aug 2003;85(2):231-248. [FREE Full text] [doi: 10.1037/0022-3514.85.2.231] [Medline: 12916567]
Moy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, et al. Measurement of clinical documentation burden among
physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. Apr 23,
2021;28(5):998-1008. [FREE Full text] [doi: 10.1093/jamia/ocaa325] [Medline: 33434273]
Iskander M. Burnout, cognitive overload, and metacognition in medicine. Med Sci Educ. Mar 2019;29(1):325-328. [FREEFull text] [doi: 10.1007/s40670-018-00654-5] [Medline: 34457483]
Stone CP. A glimpse at EHR implementation around the world: the lessons the US can learn. Dokumen. May 2014. URL:
https://dokumen.tips/download/link/a-glimpse-at-ehr-implementation-around-the-world-the-glimpse-at-ehr-implementation.
html [accessed 2024-04-05]
Evans RS. Electronic health records: then, now, and in the future. Yearb Med Inform. May 20, 2016;Suppl 1(Suppl
1):S48-S61. [FREE Full text] [doi: 10.15265/IYS-2016-s006] [Medline: 27199197]
Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C, et al. Electronic health records: new opportunities
for clinical research. J Intern Med. Dec 18, 2013;274(6):547-560. [FREE Full text] [doi: 10.1111/joim.12119] [Medline:
23952476]
Jha AK, DesRoches CM, Kralovec PD, Joshi MS. A progress report on electronic health records in U.S. hospitals. Health
Aff (Millwood). Oct 2010;29(10):1951-1957. [FREE Full text] [doi: 10.1377/hlthaff.2010.0502] [Medline: 20798168]
Crompton P. The National Programme for Information Technology--an overview. J Vis Commun Med. Jun 2007;30(2):72-77.
[FREE Full text] [doi: 10.1080/17453050701496334] [Medline: 17671907]
The electronic health records system in the UK. Centre for Public Impact. URL: https://www.centreforpublicimpact.org/
case-study/electronic-health-records-system-uk [accessed 2024-04-05]
Justinia T. The UK's National Programme for IT: why was it dismantled? Health Serv Manage Res. Feb 2017;30(1):2-9.
[FREE Full text] [doi: 10.1177/0951484816662492] [Medline: 28166675]
A plan for digital health and social care. NHS England. URL: https://tinyurl.com/cp2p2w4m [accessed 2022-06-29]
Arndt BG, Beasley JW, Watkinson MD, Temte JL, Tuan WJ, Sinsky CA, et al. Tethered to the EHR: primary care physician
workload assessment using EHR event log data and time-motion observations. Ann Fam Med. Sep 2017;15(5):419-426.
[FREE Full text] [doi: 10.1370/afm.2121] [Medline: 28893811]
Williams MS. Misdiagnosis: burnout, moral injury, and implications for the electronic health record. J Am Med Inform
Assoc. Apr 23, 2021;28(5):1047-1050. [FREE Full text] [doi: 10.1093/jamia/ocaa244] [Medline: 33164089]
Bouamrane MM, Mair FS. A study of general practitioners' perspectives on electronic medical records systems in
NHSScotland. BMC Med Inform Decis Mak. May 21, 2013;13:58. [FREE Full text] [doi: 10.1186/1472-6947-13-58][Medline: 23688255]
Babbott S, Manwell LB, Brown R, Montague E, Williams E, Schwartz M, et al. Electronic medical records and physician
stress in primary care: results from the MEMO Study. J Am Med Inform Assoc. Feb 2014;21(e1):e100-e106. [FREE Fulltext] [doi: 10.1136/amiajnl-2013-001875] [Medline: 24005796]
Khairat S, Coleman C, Ottmar P, Bice T, Carson SS. Evaluation of physicians' electronic health records experience using
actual and perceived measures. Perspect Health Inf Manag. Jan 1, 2022;19(1):1k. [FREE Full text] [Medline: 35440931]
Dymek C, Kim B, Melton GB, Payne TH, Singh H, Hsiao CJ. Building the evidence-base to reduce electronic healthrecord-related clinician burden. J Am Med Inform Assoc. Apr 23, 2021;28(5):1057-1061. [FREE Full text] [doi:10.1093/jamia/ocaa238] [Medline: 33340326]
Powell L, Sittig DF, Chrouser K, Singh H. Assessment of health information technology-related outpatient diagnostic
delays in the US Veterans Affairs health care system: a qualitative study of aggregated root cause analysis data. JAMA
Netw Open. Jun 01, 2020;3(6):e206752. [FREE Full text] [doi: 10.1001/jamanetworkopen.2020.6752] [Medline: 32584406]
Chaparro JD, Beus JM, Dziorny AC, Hagedorn PA, Hernandez S, Kandaswamy S, et al. Clinical decision support stewardship:
best practices and techniques to monitor and improve interruptive alerts. Appl Clin Inform. May 2022;13(3):560-568.
[FREE Full text] [doi: 10.1055/s-0042-1748856] [Medline: 35613913]

https://medinform.jmir.org/2024/1/e55499

XSL• FO
RenderX

Asgari et al

JMIR Med Inform 2024 | vol. 12 | e55499 | p. 8
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
27.

28.
29.

30.

31.

32.

33.
34.
35.
36.
37.
38.

39.
40.

41.

42.

43.

44.

45.

46.

47.
48.

Nguyen OT, Jenkins NJ, Khanna N, Shah S, Gartland AJ, Turner K, et al. A systematic review of contributing factors ofand solutions to electronic health record-related impacts on physician well-being. J Am Med Inform Assoc. Apr 23,
2021;28(5):974-984. [FREE Full text] [doi: 10.1093/jamia/ocaa339] [Medline: 33517382]
Feblowitz JC, Wright A, Singh H, Samal L, Sittig DF. Summarization of clinical information: a conceptual model. J Biomed
Inform. Aug 2011;44(4):688-699. [FREE Full text] [doi: 10.1016/j.jbi.2011.03.008] [Medline: 21440086]
Hill RG, Sears LM, Melanson SW. 4000 Clicks: a productivity analysis of electronic medical records in a community
hospital ED. Am J Emerg Med. Nov 2013;31(11):1591-1594. [FREE Full text] [doi: 10.1016/j.ajem.2013.06.028] [Medline:
24060331]
Kroth PJ, Morioka-Douglas N, Veres S, Babbott S, Poplau S, Qeadan F, et al. Association of electronic health record design
and use factors with clinician stress and burnout. JAMA Netw Open. Aug 02, 2019;2(8):e199609. [FREE Full text] [doi:10.1001/jamanetworkopen.2019.9609] [Medline: 31418810]
Shoolin J, Ozeran L, Hamann C, Bria W. Association of Medical Directors of Information Systems consensus on inpatientelectronic health record documentation. Appl Clin Inform. Jun 26, 2013;4(2):293-303. [FREE Full text] [doi:10.4338/ACI-2013-02-R-0012] [Medline: 23874365]
Beasley JW, Wetterneck TB, Temte J, Lapin JA, Smith P, Rivera-Rodriguez AJ, et al. Information chaos in primary care:
implications for physician performance and patient safety. J Am Board Fam Med. 2011;24(6):745-751. [FREE Full text][doi: 10.3122/jabfm.2011.06.100255] [Medline: 22086819]
Patel RS, Bachu R, Adikey A, Malik M, Shah M. Factors related to physician burnout and its consequences: a review.
Behav Sci (Basel). Oct 25, 2018;8(11):98. [FREE Full text] [doi: 10.3390/bs8110098] [Medline: 30366419]
Gal DB, Han B, Longhurst C, Scheinker D, Shin AY. Quantifying electronic health record data: a potential risk for cognitive
overload. Hosp Pediatr. Feb 2021;11(2):175-178. [FREE Full text] [doi: 10.1542/hpeds.2020-002402] [Medline: 33500357]
Wanderer JP, Nelson SE, Ehrenfeld JM, Monahan S, Park S. Clinical data visualization: the current state and future needs.
J Med Syst. Dec 2016;40(12):275. [FREE Full text] [doi: 10.1007/s10916-016-0643-x] [Medline: 27787779]
Rostamzadeh N, Abdullah SS, Sedig K. Visual analytics for electronic health records: a review. Informatics. Feb 23,
2021;8(1):12. [FREE Full text] [doi: 10.3390/informatics8010012]
Chen S, Epps J. Using task-induced pupil diameter and blink rate to infer cognitive load. Hum Comput Interact. Apr 29,
2014;29(4):390-413. [FREE Full text] [doi: 10.1080/07370024.2014.892428]
Mosaly PR, Mazur LM, Yu F, Guo H, Derek M, Laidlaw DH, et al. Relating task demand, mental effort and task difficultywith physicians’ performance during interactions with electronic health records (EHRs). Int J Hum Comput Interact. Sep
25, 2017;34(5):467-475. [FREE Full text] [doi: 10.1080/10447318.2017.1365459]
Hart SG. NASA-Task Load Index (NASA-TLX); 20 years later. Proc Hum Factors Ergon Soc Annu Meet. Nov 05,
2016;50(9):904-908. [doi: 10.1177/154193120605000909]
Mazur LM, Mosaly PR, Moore C, Marks L. Association of the usability of electronic health records with cognitive workload
and performance levels among physicians. JAMA Netw Open. Apr 05, 2019;2(4):e191709. [FREE Full text] [doi:10.1001/jamanetworkopen.2019.1709] [Medline: 30951160]
Yen PY, Pearl N, Jethro C, Cooney E, McNeil B, Chen L, et al. Nurses' stress associated with nursing activities and electronic
health records: data triangulation from continuous stress monitoring, perceived workload, and a time motion study. AMIA
Annu Symp Proc. Mar 4, 2019;2019:952-961. [FREE Full text] [Medline: 32308892]
Eschenroeder HC, Manzione LC, Adler-Milstein J, Bice C, Cash R, Duda C, et al. Associations of physician burnout withorganizational electronic health record support and after-hours charting. J Am Med Inform Assoc. Apr 23,
2021;28(5):960-966. [FREE Full text] [doi: 10.1093/jamia/ocab053] [Medline: 33880534]
Muhiyaddin R, Elfadl A, Mohamed E, Shah Z, Alam T, Abd-Alrazaq A, et al. Electronic health records and physician
burnout: a scoping review. Stud Health Technol Inform. Jan 14, 2022;289:481-484. [doi: 10.3233/SHTI210962] [Medline:
35062195]
Gardner RL, Cooper E, Haskell J, Harris DA, Poplau S, Kroth PJ, et al. Physician stress and burnout: the impact of healthinformation technology. J Am Med Inform Assoc. Feb 01, 2019;26(2):106-114. [FREE Full text] [doi: 10.1093/jamia/ocy145][Medline: 30517663]
Adler-Milstein J, Zhao W, Willard-Grace R, Knox M, Grumbach K. Electronic health records and burnout: time spent on
the electronic health record after hours and message volume associated with exhaustion but not with cynicism among
primary care clinicians. J Am Med Inform Assoc. Apr 01, 2020;27(4):531-538. [FREE Full text] [doi: 10.1093/jamia/ocz220][Medline: 32016375]
Khairat S, Burke G, Archambault H, Schwartz T, Larson J, Ratwani RM. Perceived burden of EHRs on physicians at
different stages of their career. Appl Clin Inform. Apr 2018;9(2):336-347. [FREE Full text] [doi: 10.1055/s-0038-1648222][Medline: 29768634]
What is “cognitive load”—and how can we help clinicians manage it? Nuance. Aug 11, 2022. URL: https://whatsnext.
nuance.com/healthcare-ai/cognitive-load-and-impact-on-clinician-burnout/ [accessed 2024-04-05]
Downing N, Bates DW, Longhurst CA. Physician burnout in the electronic health record era: are we ignoring the real cause?
Ann Intern Med. Jul 03, 2018;169(1):50-51. [doi: 10.7326/M18-0139] [Medline: 29801050]

https://medinform.jmir.org/2024/1/e55499

XSL• FO
RenderX

Asgari et al

JMIR Med Inform 2024 | vol. 12 | e55499 | p. 9
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
49.

50.
51.

52.

53.

54.

55.

56.

57.
58.

59.
60.
61.
62.

63.

64.

65.

66.

67.
68.

69.
70.

Pickering BW, Gajic O, Ahmed A, Herasevich V, Keegan MT. Data utilization for medical decision making at the time ofpatient admission to ICU. Crit Care Med. Jun 2013;41(6):1502-1510. [doi: 10.1097/CCM.0b013e318287f0c0] [Medline:
23528804]
Gawande A. Why doctors hate their computers. New Yorker. Nov 5, 2018. URL: https://www.newyorker.com/magazine/
2018/11/12/why-doctors-hate-their-computers [accessed 2024-04-05]
Ash JS, Sittig DF, Dykstra RH, Guappone K, Carpenter JD, Seshadri V. Categorizing the unintended sociotechnicalconsequences of computerized provider order entry. Int J Med Inform. Jun 2007;76 Suppl 1:S21-S27. [FREE Full text][doi: 10.1016/j.ijmedinf.2006.05.017] [Medline: 16793330]
Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patientcare information system-related errors. J Am Med Inform Assoc. 2004;11(2):104-112. [FREE Full text] [doi:10.1197/jamia.M1471] [Medline: 14633936]
Friedberg MW, Chen PG, Van Busum KR, Aunon F, Pham C, Caloyeras J, et al. Factors affecting physician professional
satisfaction and their implications for patient care, health systems, and health policy. Rand Health Q. Dec 1, 2014;3(4):1.
[FREE Full text] [Medline: 28083306]
Shanafelt TD, Hasan O, Dyrbye LN, Sinsky C, Satele D, Sloan J, et al. Changes in burnout and satisfaction with work-lifebalance in physicians and the general US working population between 2011 and 2014. Mayo Clin Proc. Dec
2015;90(12):1600-2613. [FREE Full text] [doi: 10.1016/j.mayocp.2015.08.023] [Medline: 26653297]
Shanafelt TD, Boone S, Tan L, Dyrbye LN, Sotile W, Satele D, et al. Burnout and satisfaction with work-life balance among
US physicians relative to the general US population. Arch Intern Med. Oct 08, 2012;172(18):1377-1385. [FREE Full text][doi: 10.1001/archinternmed.2012.3199] [Medline: 22911330]
Aziz F, Talhelm L, Keefer J, Krawiec C. Vascular surgery residents spend one fifth of their time on electronic health records
after duty hours. J Vasc Surg. May 2019;69(5):1574-1579. [FREE Full text] [doi: 10.1016/j.jvs.2018.08.173] [Medline:
31010521]
Ball C, McBeth PB. The impact of documentation burden on patient care and surgeon satisfaction. Can J Surg. Aug 10,2021;64(4):E457-E458. [FREE Full text] [doi: 10.1503/cjs.013921] [Medline: 34388108]
Alobo IG, Soyannwo T, Ukponwan G, Akogu S, Akpa AM, Ayankola K. Implementing electronic health system in Nigeria:
perspective assessment in a specialist hospital. Afr Health Sci. Jun 2020;20(2):948-954. [FREE Full text] [doi:10.4314/ahs.v20i2.50] [Medline: 33163063]
Menachemi N, Collum TH. Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy.
2011;4:47-55. [FREE Full text] [doi: 10.2147/RMHP.S12985] [Medline: 22312227]
Ratwani RM. Modest progress on the path to electronic health record medication safety. JAMA Netw Open. May 01,
2020;3(5):e206665. [FREE Full text] [doi: 10.1001/jamanetworkopen.2020.6665] [Medline: 32469409]
Secure analytics platform for NHS electronic health records. OpenSAFELY. URL: https://www.opensafely.org/ [accessed2024-04-05]
Bhaskaran K, Bacon S, Evans SJW, Bates CJ, Rentsch CT, MacKenna B, et al. Factors associated with deaths due to
COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national deathregistrations within the OpenSAFELY platform. Lancet Reg Health Eur. Jul 2021;6:100109. [FREE Full text] [doi:10.1016/j.lanepe.2021.100109] [Medline: 33997835]
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related
death using OpenSAFELY. Nature. Aug 2020;584(7821):430-436. [FREE Full text] [doi: 10.1038/s41586-020-2521-4][Medline: 32640463]
Issitt RW, Booth J, Bryant WA, Spiridou A, Taylor AM, du Pré P, et al. Children with COVID-19 at a specialist centre:
initial experience and outcome. The Lancet Child & Adolescent Health. Aug 2020;4(8):e30-e31. [FREE Full text] [doi:10.1016/s2352-4642(20)30204-2]
Bourgeois FT, Gutiérrez-Sacristán A, Keller MS, Liu M, Hong C, Bonzel CL, et al. International analysis of electronic
health records of children and youth hospitalized with COVID-19 Infection in 6 countries. JAMA Netw Open. Jun 01,
2021;4(6):e2112596. [FREE Full text] [doi: 10.1001/jamanetworkopen.2021.12596] [Medline: 34115127]
Colligan L, Potts HWW, Finn CT, Sinkin RA. Cognitive workload changes for nurses transitioning from a legacy systemwith paper documentation to a commercial electronic health record. Int J Med Inform. Jul 2015;84(7):469-476. [FREE Fulltext] [doi: 10.1016/j.ijmedinf.2015.03.003] [Medline: 25868807]
Demerouti E, Bakker AB, Nachreiner F, Schaufeli WB. A model of burnout and life satisfaction amongst nurses. J Adv
Nurs. Aug 2000;32(2):454-464. [FREE Full text] [doi: 10.1046/j.1365-2648.2000.01496.x] [Medline: 10964195]
Baumann L, Baker J, Elshaug AG. The impact of electronic health record systems on clinical documentation times: asystematic review. Health Policy. Aug 2018;122(8):827-836. [FREE Full text] [doi: 10.1016/j.healthpol.2018.05.014][Medline: 29895467]
Tsou A, Lehmann C, Michel J, Solomon R, Possanza L, Gandhi T. Safe practices for copy and paste in the EHR. Appl Clin
Inform. Dec 20, 2017;26(01):12-34. [FREE Full text] [doi: 10.4338/aci-2016-09-r-0150]
Vogel L. Cut-and-paste clinical notes confuse care, say US internists. CMAJ. Dec 10, 2013;185(18):E826. [FREE Fulltext] [doi: 10.1503/cmaj.109-4656] [Medline: 24218539]

https://medinform.jmir.org/2024/1/e55499

XSL• FO
RenderX

Asgari et al

JMIR Med Inform 2024 | vol. 12 | e55499 | p. 10(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
71.

72.

73.

74.
75.

76.
77.

78.
79.

80.

81.

82.

83.
84.

85.

Asgari et al

Cheng CG, Wu DC, Lu JC, Yu CP, Lin HL, Wang MC, et al. Restricted use of copy and paste in electronic health records
potentially improves healthcare quality. Medicine (Baltimore). Jan 28, 2022;101(4):e28644. [FREE Full text] [doi:10.1097/MD.0000000000028644] [Medline: 35089204]
Koopman RJ, Steege LMB, Moore JL, Clarke MA, Canfield SM, Kim MS, et al. Physician information needs and electronic
health records (EHRs): time to reengineer the clinic note. J Am Board Fam Med. 2015;28(3):316-323. [FREE Full text][doi: 10.3122/jabfm.2015.03.140244] [Medline: 25957364]
Tutty M, Carlasare LE, Lloyd S, Sinsky CA. The complex case of EHRs: examining the factors impacting the EHR user
experience. J Am Med Inform Assoc. Jul 01, 2019;26(7):673-677. [FREE Full text] [doi: 10.1093/jamia/ocz021] [Medline:
30938754]
Jacob JA. On the road to interoperability, public and private organizations work to connect health care data. JAMA. Sep
2015;314(12):1213-1215. [FREE Full text] [doi: 10.1001/jama.2015.5930] [Medline: 26393833]
National Academies of Sciences, Engineering, and Medicine, National Academy of Medicine, Committee on Systems
Approaches to Improve Patient Care by Supporting Clinician Well-Being. Taking Action Against Clinician Burnout: A
Systems Approach to Professional Well-Being. Washington, DC. National Academies Press (US); 2019.
Honavar S. Electronic medical records - the good, the bad and the ugly. Indian J Ophthalmol. Mar 2020;68(3):417-418.
[FREE Full text] [doi: 10.4103/ijo.IJO_278_20] [Medline: 32056991]
McGreevey J, Mallozzi CP, Perkins RM, Shelov E, Schreiber R. Reducing alert burden in electronic health records: stateof the art recommendations from four health systems. Appl Clin Inform. Jan 2020;11(1):1-12. [FREE Full text] [doi:10.1055/s-0039-3402715] [Medline: 31893559]
Kang C, Sarkar IN. Interventions to reduce electronic health record-related burnout: a systematic review. Appl Clin Inform.
Jan 2024;15(1):10-25. [FREE Full text] [doi: 10.1055/a-2203-3787] [Medline: 37923381]
Turbow S, Hollberg JR, Ali MK. Electronic health record interoperability: how did we get here and how do we move
forward? JAMA Health Forum. Mar 01, 2021;2(3):e210253. [FREE Full text] [doi: 10.1001/jamahealthforum.2021.0253][Medline: 36218452]
U.S. Department of Health and Human Services. Strategy on reducing burden relating to the use of health IT and EHRsng
Burden Relating to the Use of Health IT and EHRs. The Office of the National Coordinator for Health Information
Technology. 2020. URL: https://tinyurl.com/4z9fv83y [accessed 2024-04-05]
Shah T, Kitts AB, Gold JA, Horvath K, Ommaya A, Frank O, et al. Electronic health record optimization and clinician
well-being: a potential roadmap toward action. NAM Perspect. Aug 3, 2020;2020:10.31478/202008a. [FREE Full text][doi: 10.31478/202008a] [Medline: 35291737]
Gandhi TK, Classen D, Sinsky CA, Rhew DC, Vande Garde N, Roberts A, et al. How can artificial intelligence decreasecognitive and work burden for front line practitioners? JAMIA Open. Oct 2023;6(3):ooad079. [FREE Full text] [doi:10.1093/jamiaopen/ooad079] [Medline: 37655124]
Guo U, Chen L, Mehta PH. Electronic health record innovations: helping physicians - one less click at a time. Health Inf
Manag. Sep 2017;46(3):140-144. [FREE Full text] [doi: 10.1177/1833358316689481] [Medline: 28671038]
Cohen C, Pignata S, Bezak E, Tie M, Childs J. Workplace interventions to improve well-being and reduce burnout for
nurses, physicians and allied healthcare professionals: a systematic review. BMJ Open. Jun 29, 2023;13(6):e071203. [FREEFull text] [doi: 10.1136/bmjopen-2022-071203] [Medline: 37385740]
Examining clinician burnout in healthcare organizations – why it’s also an IT concern. Wolters Kluwer. Apr 12, 2023.
URL: https://tinyurl.com/3xa4ynef [accessed 2024-04-05]

Abbreviations
EHR: electronic health record
HIT: health information technology
NASA-TLX: NASA Task-Load Index

Edited by C Lovis; submitted 14.12.23; peer-reviewed by R Schreiber, L Ozeran; comments to author 02.01.24; revised version
received 15.02.24; accepted 11.03.24; published 12.04.24
Please cite as:
Asgari E, Kaur J, Nuredini G, Balloch J, Taylor AM, Sebire N, Robinson R, Peters C, Sridharan S, Pimenta D
Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review
JMIR Med Inform 2024;12:e55499
URL: https://medinform.jmir.org/2024/1/e55499
doi: 10.2196/55499
PMID: 38607672

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12.04.2024. 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 https://medinform.jmir.org/, as well as this copyright and license information must be included.

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