ArticleJAMA network open2020
Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes.
Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 4 of them syntheses that pooled it.
What it found
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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.
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Who cites it
122 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Pooled it
- Impact of online patient access to clinical notes on quality of care: a systematic review.BMJ quality & safety · 2026Pooled it
- Prevalence, contributing factors, and interventions to reduce medication errors in outpatient and ambulatory settings: a systematic review.International journal of clinical pharmacy · 2023Pooled it
- The economic burden of stroke: a systematic review of cost of illness studies.Journal of medicine and lifePooled it
- Providing Mobile Patient Access to Their Electronic Secondary Care Patient Record in Adults With Cystic Fibrosis: Results of a Prospective, Parallel, Randomized Open-Pilot Quantitative Study.JMIR formative research · 2025Trial
- Beyond Transparency : Ethical Note Obligations, Documentation Burden, and the Ethical Adoption of AI-Assisted Documentation.Journal of bioethical inquiry · 2026Article
- Letter to the Editor: Closing the loop in radiology-patient access to reports as an additional safety layer.Insights into imaging · 2026Article
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- The Growing Use of Copy-Paste and Its Impact on Creation and Propagation of Documentation Errors.CHEST pulmonary · 2026Article
- Please document the nature of the medical emergency.BMJ quality & safety · 2026Article
- An Evaluation of AI-Generated Clinical Notes in the OpenNotes Era: A Thematic Analysis of Clinician Discourse.Journal of evaluation in clinical practice · 2026Article
- Feasibility of Tailoring Artificial Intelligence-Assisted Ambient Scribes for Intensive Care Unit Rounds: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Patient Engagement With Open Notes Following the 21st Century Cures Act.JAMA health forum · 2026Article
- Ambient AI in primary care: an exploratory mixed methods survey of UK general practitioners.BMJ health & care informatics · 2026Article
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.medRxiv : the preprint server for health sciences · 2026Article
- Large language model-based scribing tools for ophthalmology: performance and safety evaluation using simulated clinical encounters.Eye (London, England) · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- A data and knowledge cross-level fusion-driven learning framework for detecting missing diagnosis.NPJ digital medicine · 2026Article
- Accuracy of large language model transcription of simulated physician-patient verbal interactions.BMC medical informatics and decision making · 2026Article
62 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: As health information transparency increases, patients more often seek their health data. More than 44 million patients in the US can now readily access their ambulatory visit notes online, and the practice is increasing abroad. Few studies have assessed documentation errors that patients identify in their notes and how these may inform patient engagement and safety strategies. Objective: To assess the frequency and types of errors identified by patients who read open ambulatory visit notes. Design, Setting, and Participants: In this survey study, a total of 136 815 patients at 3 US health care organizations with open notes, including 79 academic and community ambulatory care practices, received invitations to an online survey from June 5 to October 20, 2017. Patients who had at least 1 ambulatory note and had logged onto the portal at least once in the past 12 months were included. Data analysis was performed from July 3, 2018, to April 27, 2020. Exposures: Access to ambulatory care open notes through patient portals for up to 7 years (2010-2017). Main Outcomes and Measures: Proportion of patients reporting a mistake and how serious they perceived the mistake to be, factors associated with finding errors characterized by patients as serious, and categories of patient-reported errors. Results: Of 136 815 patients who received survey invitations, 29 656 (21.7%) responded and 22 889 patients (mean [SD] age, 55.16 [15.96] years; 14 447 [63.1%] female; 18 301 [80.0%] white) read 1 or more notes in the past 12 months and completed error questions. Of these patients, 4830 (21.1%) reported a perceived mistake and 2043 (42.3%) reported that the mistake was serious (somewhat serious: 1563 [32.4%]; very serious: 480 [9.9%]). In multivariable analysis, female patients (relative risk [RR], 1.79; 95% CI, 1.72-1.85), more educated patients (RR, 1.38; 95% CI, 1.29-1.48), sicker patients (RR, 1.89; 95% CI, 1.84-1.94), those aged 45 to 64 years (RR, 2.23; 95% CI, 2.06-2.42), those 65 years or older (RR, 2.00; 95% CI, 1.73-2.32), and those who read more than 1 note (2-3 notes: RR, 1.82; 95% CI, 1.34-2.47; ≥4 notes: RR, 3.09; 95% CI, 2.02-4.73) were more likely to report a mistake that they found to be serious compared with their reference groups. After categorization of patient-reported very serious mistakes, those specifically mentioning the word diagnosis or describing a specific error in current or past diagnoses were most common (98 of 356 [27.5%]), followed by inaccurate medical history (85 of 356 [23.9%]), medications or allergies (50 of 356 [14.0%]), and tests, procedures, or results (30 of 356 [8.4%]). A total of 23 (6.5%) reflected notes reportedly written on the wrong patient. Of 433 very serious errors, 255 (58.9%) included at least 1 perceived error potentially associated with the diagnostic process (eg, history, physical examination, tests, referrals, and communication). Conclusions and Relevance: In this study, patients who read ambulatory notes online perceived mistakes, a substantial proportion of which they found to be serious. Older and sicker patients were twice as likely to report a serious error compared with younger and healthier patients, indicating important safety and quality implications. Sharing notes with patients may help engage them to improve record accuracy and health care safety together with practitioners.
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