Background: Diagnostic errors have been cited as a potential contributor to hospital readmissions, particularly early readmissions (e.g. within 7 days). A single prior study of early readmissions applied a binary (yes/no) metric to assess for diagnostic error in early readmissions, but this may be an insensitive method. Past studies of diagnostic error in primary care visits have used validated tools such as the “Safer Dx Instrument” to identify diagnostic error, but this has not been applied to 7-day readmissions.
Purpose: To develop a structured framework for identifying diagnostic error in inpatient readmissions
Description: We reviewed the charts of all patients discharged from the inpatient medical service and re-admitted to our urban academic medical center within 7 days to any service (7-day readmissions) from 1/1/18-9/30/18. A structured review of the electronic medical record was completed independently by two trained board-certified internists, supported by consensus review in discrepant cases. Diagnostic errors were identified using the “Safer Dx Instrument.” The entirety of the index admission was considered as a single episode of care when applying the “Safer Dx Instrument.” When identified, errors were then characterized using the Diagnostic Error Evaluation and Research (DEER) taxonomy tool, an error classification system. Multiple contributing factors could be identified in the same admission. For all identified diagnostic errors, the discharging physician(s) from the index admission were contacted to provide further details of the clinical decision-making and circumstances that may have contributed to the error, which was then used to refine diagnostic error characterization.
Conclusions: Structured review of 7-day readmissions for DE is a novel mechanism to identify diagnostic error in this population and may be an opportunity to provide feedback to providers and to improve diagnostic performance. Case review was feasible to perform, and reviewers had good concordance (88.9%), with improvement over time. Despite structured review, error identification was challenging due to complexity of clinical care, time intensity of reviews, incomplete or ambiguous EMR documentation, and omission of the patient perspective. Current error identification and classification systems focus attention on diagnostic reasoning more than identifying system factors contributing to error. Future structured tools adding systems factors identification could be developed.