BACKGROUND: Diagnostic errors represent a major cause of patient harm. One way to reduce diagnostic errors is to support learning health systems with standardized event review practices and data integration across event types and health systems. The extent to which this occurs and the barriers to doing so remain poorly characterized. OBJECTIVES: Characterize event review practices and analyze the presence and distribution of key patient safety learning system constructs, diagnostic error analysis capabilities, and implementation barriers across a health system. DESIGN: We developed a survey of participant safety event review practices, associated learning health system activities, diagnostic error analysis capabilities, data integration, and implementation barriers. PARTICIPANTS: The survey was electronically distributed to a purposive sample of health system employees who routinely perform safety event reviews at one US academic medical center. APPROACH: Descriptive statistics were reported for survey constructs. Chi-square analysis was used to assess for non-response bias. KEY RESULTS: One hundred and six of 249 possible respondents (42.6%) completed the survey. Safety event report review was the most frequently performed review type (85.5%) and legal claims review was the least frequent (17.0%). Only safety event report databases characterized diagnostic errors to a high degree (50.0%), while those for other event categories did so less frequently (< 30%). Few respondents reported tracking learnings across event categories (< 25% for all event types). Lack of dedicated review resources and supportive event review software were key barriers to learning and system improvement. CONCLUSIONS: There is a need for more consistent practice across all safety event review types if learnings are to reduce diagnostic error and other drivers of patient harm. Organizations may benefit from standardization of review processes, databases that categorize diagnostic error within and across event review types, and resources to support reviews and translation of learnings into system improvement.
Luty et al. (Wed,) studied this question.