Abstract 215: Leveraging Electronic Health Record Documentation for Failure Mode and Effects Analysis Team Identification on an Inpatient Cardiology Unit

Objectives: Failure Mode and Effects Analysis (FMEA) is a frequently-used approach for prospective risk assessment and quality improvement in healthcare, particularly for high-risk care processes such as hospital discharge planning. Our goal was to evaluate whether secondary use of metadata collected by the electronic health record (EHR) during daily practice can inform assembly of a comprehensive FMEA team by showing: 1) discrepancies between expected and observed process activities and individuals involved, and 2) the presence of individuals who may be appropriate to include in an FMEA based on their variable familiarity with a process. Methods: We extracted discharge planning data for an inpatient cardiology unit from the Enterprise Data Warehouse (EDW) and compared it to a hand-drawn map (HDM) indicating clinicians’ understanding of discharge activities and providers expected to complete each activity. We assessed the presence of providers highly experienced in the process, the diversity of involved disciplines, and the accuracy of the HDM compared to observation from EDW data. Findings: Over 500 providers completed nearly 35,000 discharge-related activities across 18 activity types over 2,000 encounters. Experience was skewed such that 90% (510 of 569) of providers completed between 0 and 99 activities while the remaining 10% (59 of 569) performed up to 1,200 activities. Frequent performers completed similar activities to their peers, but did so as many as 12 times more frequently than average for their discipline. Expectation of who performed an activity closely matched observation for 11 discipline-specific activities, such as case management assessment. However, providers from up to 10 different disciplines performed the remaining 7 activities, such as scheduling a follow-up visit or ordering a therapy consult. Overall, 35% (12,183 of 34,939) of activities were performed by an unexpected provider. Conclusions: Analyzing metadata from EHRs is a novel method to inform FMEA of high-risk processes. This study provides a framework for assessing process activities and the providers involved. In the discharge planning process, there appears to be significant discrepancy between clinicians’ understanding and the actual discharge process and team, which suggests the presence of providers who could be overlooked during typical FMEA team construction. This methodology can empirically enrich the FMEA team and highlight quality improvement target areas.