Presentation
Applying Human Factors to Traditional Apparent Cause Analysis
SessionAdverse Events (HE2)
DescriptionApparent Cause Analysis (ACA) is routinely used to investigate safety events that result in limited or no harm. An amended and shortened form of the root cause analysis (RCA), ACAs are conducted in a shorter time period, with fewer resources, and focus on prevention strategies. Like RCAs, ACAs have been criticized for it's limitations and that it can promote the ideal of single root causes, ignoring the complexity of healthcare systems and the individuals involved. Further, ACAs action plans result in implementations and solutions that have low reliability. (Crandall, et. al, 2017). Last, ACAs are dependent upon those involved.
In healthcare, understanding the nuances between “work as imagined” and “work as done” is crucial to enhancing safety and efficiency. This presentation explores the application of human factors methods in the apparent cause analysis process to capture direct feedback from frontline staff. Two case studies demonstrate how different approaches reveal system vulnerabilities and generate practical solutions aligned with real-world practices.
The first case focuses on the intake process for mental health patients, specifically reexamining the search procedures for patients and their belongings. By engaging staff in the review process, the method exposed gaps between procedural assumptions and actual practices, leading to targeted improvements in patient safety and workflow efficiency.
The second case addresses the admission process for neurobehavioral patients, where human factors methods facilitated a redesign that allowed for direct admission to the appropriate unit, streamlining the process. Frontline feedback was instrumental in reshaping the workflow to better accommodate patient needs and reduce delays.
Both cases highlight the value of human factors approaches in apparent cause analysis, showcasing their role in generating actionable insights by bridging the gap between work as imagined and work as done.
1. Crandall KM, Sten MB, Almuhanna A, Fahey L, Shah RK. Improving Apparent Cause Analysis Reliability: A Quality Improvement Initiative. Pediatr Qual Saf. 2017;2(3):e025. Published 2017 May 25. doi:10.1097/pq9.0000000000000025
In healthcare, understanding the nuances between “work as imagined” and “work as done” is crucial to enhancing safety and efficiency. This presentation explores the application of human factors methods in the apparent cause analysis process to capture direct feedback from frontline staff. Two case studies demonstrate how different approaches reveal system vulnerabilities and generate practical solutions aligned with real-world practices.
The first case focuses on the intake process for mental health patients, specifically reexamining the search procedures for patients and their belongings. By engaging staff in the review process, the method exposed gaps between procedural assumptions and actual practices, leading to targeted improvements in patient safety and workflow efficiency.
The second case addresses the admission process for neurobehavioral patients, where human factors methods facilitated a redesign that allowed for direct admission to the appropriate unit, streamlining the process. Frontline feedback was instrumental in reshaping the workflow to better accommodate patient needs and reduce delays.
Both cases highlight the value of human factors approaches in apparent cause analysis, showcasing their role in generating actionable insights by bridging the gap between work as imagined and work as done.
1. Crandall KM, Sten MB, Almuhanna A, Fahey L, Shah RK. Improving Apparent Cause Analysis Reliability: A Quality Improvement Initiative. Pediatr Qual Saf. 2017;2(3):e025. Published 2017 May 25. doi:10.1097/pq9.0000000000000025
Event Type
Oral Presentations
TimeMonday, March 311:52pm - 2:15pm EDT
LocationHarbour C
Hospital Environments (HE)