ArticleJMIR medical informatics2024
Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review.
Article in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
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- Social Risk-Informed Decision Support and Blood Pressure Control in a Primary Care Cluster Randomized Controlled Trial.Annals of family medicine · 2026Trial
- Assessing digital health curriculum needs: a mixed-methods study of student and faculty perspectives in a Singapore medical school.Medical education online · 2026Article
- Barriers and Facilitators to Timely Detection and Optimal Management of Chronic Wet Cough in Aboriginal Children Across Australia From the Perspective of Health Care Providers: A Qualitative Study.The Medical journal of Australia · 2026Article
- Generative AI Use, Perceived Usefulness, Perceived Risk, and Physician Burnout and Fulfillment Among Chinese Physicians: Mixed Methods Multiregional Study.Journal of medical Internet research · 2026Article
- Usability and Workflow Integration of a Machine Learning-Derived Neonatal Risk Predictor in Kenyan Neonatal Units: Multisite User-Centered Pilot Evaluation.JMIR human factors · 2026Article
- Digital Anterior Segment Photography in Clinical Practice: Mixed Methods Study.JMIR formative research · 2026Article
- Advancing foundational models in digital health technology adoption: A systematic literature review of multidisciplinary factors.PLOS digital health · 2026Article
- Building Clinical Artificial Intelligence at the PICU Bedside: Recognizing the Clinician-Builder.Critical care explorations · 2026Article
- Global Trends, Inequalities, and Citation Dynamics in Burnout Research Among Healthcare Professionals: A Bibliometric Analysis (1987-2024).Healthcare (Basel, Switzerland) · 2026Review
- Observational
- Digital Transformation in Health Care: Are We on the Right Track?Journal of medical Internet research · 2026Article
- Technostress, Perceived Organizational Support, and Burnout Among Healthcare Professionals: A Suppression Mediation Model.Nursing reports (Pavia, Italy) · 2026Article
- Large language model-generated clinical summaries in emergency departments: A blinded comparison study.PLOS digital health · 2026Article
- Article
- Machine learning models to detect opioid misuse in emergency department patients at triage.The American journal of emergency medicine · 2026Article
- Large language models approach clinician performance in ESC cardiovascular risk stratification: a vignette-based benchmark study.European heart journal. Digital health · 2026Article
- Cognitive Load Across Interaction Formats in Digital Attention Assessment for Children: Within-Subject Neuroimaging and Behavioral Comparison Study.JMIR serious games · 2026Article
- Anaesthesia professionals' perspectives on ECG interpretation and arrhythmia situation awareness with Visual Patient Heart: a qualitative multicentre study.BMC medical informatics and decision making · 2026Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cognitive load theory suggests that completing a task relies on the interplay between sensory input, working memory, and long-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 to review 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 with the 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 factors that 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 burden in the EHR and the complexity of the user interfaces, thus adding more "work" to tasks, can lead to cognitive overload and burnout; this calls for strategies to mitigate these effects. Several factors, such as the presentation of information in the EHR, the specialty, the health care setting, and the time spent completing documentation and navigating systems, can contribute to this excess 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.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.