Improving Apparent Cause Analysis Reliability: A Quality Improvement Initiative

INTRODUCTION: Apparent cause analysis (ACA) is a process in quality improvement used to examine events. A baseline assessment of completed ACAs at a tertiary care free-standing pediatric academic hospital revealed they were ineffective due to low-quality analysis, unreliable action plans, and poor spread, leading to error recurrence. The goal of this project was to increase ACA action plan reliability scores while maintaining or decreasing turnaround time. METHODS: The Model for Improvement served as the framework for this quality improvement initiative. We developed a key driver diagram, established measures, tested interventions using plan- do-study-act cycles, and implemented the effective interventions. To measure reliability, we created a high reliability toolkit that links each action item/intervention to a level of reliability and scored each ACA action plan to determine overall reliability score. Action plans scored as low level of reliability required revision before implementation. RESULTS: Average ACA action plan reliability scores increased from 86.4% to 96.1%. ACA turnaround time decreased from a baseline of 13 days to 8.6 days. Stakeholders reported a subjective increase in satisfaction with the revamped ACA process. CONCLUSIONS: Incorporating high reliability principles into ACA action plan development increased the effectiveness of ACA while decreasing turnaround time. The high reliability toolkit was instrumental in providing an organizational resource for approaching this subset of cause analyses. The toolkit provides a way for safety/quality leaders to connect with stakeholders to design highly reliable solutions that improve safety for patients, families, and staff.