Using Patient Experience Surveys to Identify Potential Diagnostic Safety Breakdowns: A Mixed Methods Study
Bibliographic record
Abstract
OBJECTIVES: One in 20 outpatients in the United States experiences a diagnostic error each year, but there are no validated methods for collecting feedback from patients on diagnostic safety. We examined patient experience surveys to determine whether patients' free text comments indicated diagnostic breakdowns. Our objective was to evaluate associations between patient-perceived diagnostic breakdowns reported in free text comments and patients' responses to structured survey questions. METHODS: We conducted an exploratory mixed methods study using data from patient experience surveys collected from adult ambulatory care patients March 2020 to June 2020 in a large U.S. health system. Data analysis included content analysis of qualitative data and statistical analysis of quantitative data. RESULTS: In 2525 surveys with negative comments, 619 patients (24.5%) identified diagnostic breakdowns, including issues with accuracy (n = 282, 46%), timeliness (n = 243, 39%), or communication (n = 290, 47%); some patients (n = 181) reported breakdowns in multiple categories. Patients who gave a low average score (50 or less on a 100-point scale) on provider questions were almost seven times more likely to perceive a diagnostic breakdown than patients who scored their provider higher. Similarly, patients who gave a low average score on practice-related questions were twice as likely to perceive a diagnostic breakdown. CONCLUSIONS: Patient feedback in routinely collected patient experience surveys is a valuable and actionable information source on diagnostic breakdowns in the ambulatory setting. The more easily monitored structured survey data provide a screening method to identify encounters that may have included a patient-perceived diagnostic breakdown and therefore require further examination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".