ArticleJournal of general internal medicine2019
Variation in Physicians' Electronic Health Record Documentation and Potential Patient Harm from That Variation.
Article in Journal of general internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- User-centered design of a patient-generated health data mobile application for primary care visits for older adults with chronic pain.JAMIA open · 2026Article
- Computable phenotypes for research using real-world data: experiences from the NIH pragmatic trials collaboratory.JAMIA open · 2026Article
- A Clinically Grounded Rule-Based Phenotyping Framework for Identifying Immune-Related Adverse Events in Structured Electronic Health Record Data: A Retrospective EHR Study.Research square · 2026Article
- Challenges in Identifying Heart Failure Diagnoses from the Electronic Health Record.Journal of cardiac failure - intersections · 2026Article
- Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study.JMIR formative research · 2026Article
- Concordance Between Patient-Reported and Clinically Documented Symptoms in Acute Myeloid Leukemia.Oncology nursing forum · 2026Article
- Current state of electronic problems lists in primary care: a rapid scoping review.Family practice · 2026Article
- AI-assisted assessment of narrative documentation quality in ART consultations using a domain-based framework.Journal of assisted reproduction and genetics · 2026Article
- Optimal approach to standardized documentation in epilepsy clinics: A scoping review.Epilepsia · 2026Article
- Quantifying improvement of psychotic symptoms in clozapine-treated schizophrenia: clinical note analysis with large language models.Scientific reports · 2026Article
- Leveraging Large Language Models with Sequential Prompting to Extract Eye Examination Findings from Free-Text Ophthalmology Notes.Ophthalmology science · 2026Article
- Variations in social care need reporting amongst GP practices in England: a retrospective cohort study in people with multimorbidity.BMC primary care · 2025Article
- The Effect of Dictation on Emergency Medicine Resident Time to Note Completion.The western journal of emergency medicine · 2025Article
- Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Comparing artificial intelligence- vs clinician-authored summaries of simulated primary care electronic health records.JAMIA open · 2025Article
- Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling.JAMIA open · 2025Article
- Artificial Intelligence for Management of Major Depression: Initial Design, Progress, and Research Plans.Alpha psychiatry · 2025Article
- Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.JCO clinical cancer informatics · 2025Article
17 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
Abstract
backgroundPhysician-to-physician variation in electronic health record (EHR) documentation not driven by patients' clinical status could be harmful.
objectiveMeasure variation in completion of common clinical documentation domains. Identify perceived causes and effects of variation and strategies to mitigate negative effects.
designSequential, explanatory, mixed methods using log data from a commercial EHR vendor and semi-structured interviews with outpatient primary care practices.
participantsQuantitative: 170,332 encounters led by 809 physicians in 237 practices. Qualitative: 40 interviewees in 10 practices. MAIN MEASURES: Interquartile range (IQR) of the proportion of encounters in which a physician completed documentation, for each documentation category. Multilevel linear regression measured the proportion of variation at the physician level. KEY
resultsFive clinical documentation categories had substantial and statistically significant (p < 0.001) variation at the physician level after accounting for state, organization, and practice levels: (1) discussing results (IQR = 50.8%, proportion of variation explained by physician level = 78.1%); (2) assessment and diagnosis (IQR = 60.4%, physician-level variation = 76.0%); (3) problem list (IQR = 73.1%, physician-level variation = 70.1%); (4) review of systems (IQR = 62.3%, physician-level variation = 67.7%); and (5) social history (IQR = 53.3%, physician-level variation = 62.2%). Drivers of variation from interviews included user preferences and EHR designs with multiple places to record similar information. Variation was perceived to create documentation inefficiencies and risk patient harm due to missed or misinterpreted information. Mitigation strategies included targeted user training during EHR implementation and practice meetings focused on documentation standardization.
conclusionsPhysician-to-physician variation in EHR documentation impedes effective and safe use of EHRs, but there are potential strategies to mitigate negative consequences.
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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 OpenQuestion graph.