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Background: Most people experience a diagnostic error at least once in their lifetime. Patients’ experiences with their diagnosis could provide important insights when setting research priorities to reduce diagnostic error. Objective: Our objective was to engage patients in research agenda setting for improving diagnosis. Patient involvement: Patients were involved in generating, discussing, prioritizing, and ranking of research questions for diagnostic error reduction. Methods: We used the prioritization methodology based on the Child Health and Nutrition Research Initiative (CHNRI). We first solicited research questions important for diagnostic error reduction from a large group of patients. Thirty questions were initially prioritized at an in-person meeting with 8 patients who were supported by 4 researchers. The resulting list was further prioritized by patients who scored questions on five predefined criteria. We then applied previously determined weights to these prioritization criteria to adjust the final prioritization score for each question, resulting in 10 highest priority research questions. Results: Forty-one patients submitted 171 research questions. After prioritization, the highest priority topics included better care coordination across the diagnostic continuum and improving care transitions, improved identification and measurement of diagnostic errors and attention for implicit bias towards patients who are vulnerable to diagnostic errors. Discussion: We systematically identified the top-10 patient generated research priorities for diagnostic error reduction using transparent and objective methods. Patients prioritized different research questions than researchers and therefore complemented an agenda previously generated by researchers. Practical value: Research priorities identified by patients can be used by funders and researchers to conduct future research focused on reducing diagnostic errors.

To identify challenges and pragmatic strategies for improving diagnostic safety at an organizational level using concepts from learning health systems METHODS: We interviewed 32 safety leaders across the USA on how their organizations approach diagnostic safety. Participants were recruited through email and represented geographically diverse academic and non-academic settings. The interview included questions on culture of reporting and learning from diagnostic errors; data gathering and analysis activities; diagnostic training and educational activities; and engagement of clinical leadership, staff, patients, and families in diagnostic safety activities. We conducted an inductive content analysis of interview transcripts and two reviewers coded all data.Of 32 participants, 12 reported having a specific program to address diagnostic errors. Multiple barriers to implement diagnostic safety activities emerged: serious concerns about psychological safety associated with diagnostic error; lack of infrastructure for measurement, monitoring, and improvement activities related to diagnosis; lack of leadership investment, which was often diverted to competing priorities related to publicly reported measures or other incentives; and lack of dedicated teams to work on diagnostic safety. Participants provided several strategies to overcome barriers including adapting trigger tools to identify safety events, engaging patients in diagnostic safety, and appointing dedicated diagnostic safety champions.Several foundational building blocks related to learning health systems could inform organizational efforts to reduce diagnostic error. Promoting an organizational culture specific to diagnostic safety, using science and informatics to improve measurement and analysis, leadership incentives to build institutional capacity to address diagnostic errors, and patient engagement in diagnostic safety activities can enable progress.

Background The 21st Century Cures Act mandates patients’ access to their electronic health record (EHR) notes. To our knowledge, no previous work has systematically invited patients to proactively report diagnostic concerns while documenting and tracking their diagnostic experiences through EHR-based clinician note review. Objective To test if patients can identify concerns about their diagnosis through structured evaluation of their online visit notes. Methods In a large integrated health system, patients aged 18–85 years actively using the patient portal and seen between October 2019 and February 2020 were invited to respond to an online questionnaire if an EHR algorithm detected any recent unexpected return visit following an initial primary care consultation (“at-risk” visit). We developed and tested an instrument (Safer Dx Patient Instrument) to help patients identify concerns related to several dimensions of the diagnostic process based on notes review and recall of recent “at-risk” visits. Additional questions assessed patients’ trust in their providers and their general feelings about the visit. The primary outcome was a self-reported diagnostic concern. Multivariate logistic regression tested whether the primary outcome was predicted by instrument variables. Results Of 293 566 visits, the algorithm identified 1282 eligible patients, of whom 486 responded. After applying exclusion criteria, 418 patients were included in the analysis. Fifty-one patients (12.2%) identified a diagnostic concern. Patients were more likely to report a concern if they disagreed with statements “the care plan the provider developed for me addressed all my medical concerns” [odds ratio (OR), 2.65; 95% confidence interval [CI], 1.45–4.87) and “I trust the provider that I saw during my visit” (OR, 2.10; 95% CI, 1.19–3.71) and agreed with the statement “I did not have a good feeling about my visit” (OR, 1.48; 95% CI, 1.09–2.01). Conclusion Patients can identify diagnostic concerns based on a proactive online structured evaluation of visit notes. This surveillance strategy could potentially improve transparency in the diagnostic process.

BACKGROUND Patients are increasingly seeking Web-based symptom checkers to obtain diagnoses. However, little is known about the characteristics of the patients who use these resources, their rationale for use, and whether they find them accurate and useful. OBJECTIVE The study aimed to examine patients’ experiences using an artificial intelligence (AI)–assisted online symptom checker. METHODS An online survey was administered between March 2, 2018, through March 15, 2018, to US users of the Isabel Symptom Checker within 6 months of their use. User characteristics, experiences of symptom checker use, experiences discussing results with physicians, and prior personal history of experiencing a diagnostic error were collected. RESULTS A total of 329 usable responses was obtained. The mean respondent age was 48.0 (SD 16.7) years; most were women (230/304, 75.7%) and white (271/304, 89.1%). Patients most commonly used the symptom checker to better understand the causes of their symptoms (232/304, 76.3%), followed by for deciding whether to seek care (101/304, 33.2%) or where (eg, primary or urgent care: 63/304, 20.7%), obtaining medical advice without going to a doctor (48/304, 15.8%), and understanding their diagnoses better (39/304, 12.8%). Most patients reported receiving useful information for their health problems (274/304, 90.1%), with half reporting positive health effects (154/302, 51.0%). Most patients perceived it to be useful as a diagnostic tool (253/301, 84.1%), as a tool providing insights leading them closer to correct diagnoses (231/303, 76.2%), and reported they would use it again (278/304, 91.4%). Patients who discussed findings with their physicians (103/213, 48.4%) more often felt physicians were interested (42/103, 40.8%) than not interested in learning about the tool’s results (24/103, 23.3%) and more often felt physicians were open (62/103, 60.2%) than not open (21/103, 20.4%) to discussing the results. Compared with patients who had not previously experienced diagnostic errors (missed or delayed diagnoses: 123/304, 40.5%), patients who had previously experienced diagnostic errors (181/304, 59.5%) were more likely to use the symptom checker to determine where they should seek care (15/123, 12.2% vs 48/181, 26.5%; P=.002), but they less often felt that physicians were interested in discussing the tool’s results (20/34, 59% vs 22/69, 32%; P=.04). CONCLUSIONS Despite ongoing concerns about symptom checker accuracy, a large patient-user group perceived an AI-assisted symptom checker as useful for diagnosis. Formal validation studies evaluating symptom checker accuracy and effectiveness in real-world practice could provide additional useful information about their benefit.