The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes
Open accessAbstract
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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“As health information transparency spreads and more patients have access to their EHRs, 24 patients and families will likely identify documentation errors and organizations will need systems to support and receive patient feedback.19,25”
From Speaking up about patient-perceived serious visit note errors: Patient and family experiences and recommendations (2021) · cites this paper as [19]
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“With educational support, patient self-triage of perceived errors may help organizations to respond meaningfully.19”
From Speaking up about patient-perceived serious visit note errors: Patient and family experiences and recommendations (2021) · cites this paper as [19]
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Walker, J., Leveille, S., Bell, S., Chimowitz, H., Dong, Z., Elmore, J. G., Fernandez, L., Fossa, A., Gerard, M., Fitzgerald, P., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/13876 · Cited by 309
Self-citationBell, S. K., Mejilla, R., Anselmo, M., Darer, J. D., Elmore, J. G., Leveille, S., Ngo, L., Ralston, J. D., Delbanco, T., & Walker, J. (2017) · BMJ Quality & Safety · doi:10.1136/bmjqs-2015-004697 · Cited by 213
Self-citationSingh, H., Thomas, E. J., Mani, S., Sittig, D., Arora, H., Espadas, D., Khan, M. M., & Petersen, L. A. (2009) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.263 · Cited by 212
The missing evidence: a systematic review of patients' experiences of adverse events in health care
Harrison, R., Walton, M., Manias, E., Smith–Merry, J., Kelly, P., Iedema, R., & Robinson, L. (2015) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzv075 · Cited by 134
EHR Safety: The Way Forward to Safe and Effective Systems
Walker, J. M., Carayon, P., Leveson, N., Paulus, R. A., Tooker, J., Chin, H., Bothe, A., & Stewart, W. F. (2008) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2618 · Cited by 113
Self-citationWeiner, S. J., Wang, S., Kelly, B., Sharma, G., & Schwartz, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa027 · Cited by 100
Cited by
Wearable sensor data and self-reported symptoms for COVID-19 detection
Quer, G., Radin, J. M., Gadaleta, M., Baca-Motes, K., Ariniello, L., Ramos, E., Kheterpal, V., Topol, E. J., & Steinhubl, S. R. (2021) · Nature Medicine · doi:10.1038/s41591-020-1123-x · Cited by 447
Kernberg, A., Gold, J. A., & Mohan, V. (2024) · Journal of Medical Internet Research · doi:10.2196/54419 · Cited by 120
“In recent years, it has become increasingly evident that inaccuracies in medical notes lead to miscommunication, diagnostic discrepancies, and patients’ perceptions of subpar medical care [1].”
Artificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou, E. K., & Khera, R. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae415 · Cited by 34
Lam, B. D., Bourgeois, F., Dong, Z. J., & Bell, S. K. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa293 · Cited by 29
Self-citationHussain, S.-A., Sezgin, E., Krivchenia, K., Luna, J., Rust, S., & Huang, Y. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab084 · Cited by 20
Metadata
- Authors
- Bell, S. K., Delbanco, T., Elmore, J. G., Fitzgerald, P. S., Fossa, A., Harcourt, K., Leveille, S. G., Payne, T. H., Stametz, R. A., Walker, J., & DesRoches, C. M.
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2020-06-09
- Volume
- 3
- Issue
- 6
- Pages
- e205867
- ISSN
- 2574-3805 (ISSN-L)
- OpenAlex
- W3034019787
- Cited by
- 238
- References
- 6
- FWCI
- 22.55
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bell, S. K., Delbanco, T., Elmore, J. G., Fitzgerald, P. S., Fossa, A., Harcourt, K., Leveille, S. G., Payne, T. H., Stametz, R. A., Walker, J., & DesRoches, C. M. (2020). Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes. JAMA Network Open. https://doi.org/10.1001/jamanetworkopen.2020.5867
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