A Systems Approach to Analyzing and Preventing Hospital Adverse Events
Name
16-1-18 J Pt Safety Leveson & Raman CAST_Checklist_JPtSafety2016 (1).pdf
Size
1.04 MB
Format
Adobe PDF
Checksum (MD5)
b5e4b7260bd0a766477cd6553ed647f2
Author(s)
Samost, Aubrey
Dekker, Sidney
Finkelstein, Stan
Raman, Jai
Leveson, Nancy G
Date Issued
January 2016
Journal
Journal of Patient Safety
Publisher
Ovid Technologies (Wolters Kluwer) - Lippincott Williams & Wilkins
Citation
Leveson, Nancy et al. “A Systems Approach to Analyzing and Preventing Hospital Adverse Events.” Journal of Patient Safety (January 2016) © 2016 Lippincott Williams & Wilkins
Version
Author's final manuscript
Abstract
Objective: This study aimed to demonstrate the use of a systems theory-based accident analysis technique in health care applications as a more powerful alternative to the chain-of-event accident models currently underpinning root cause analysis methods.
Method: A new accident analysis technique, CAST [Causal Analysis based on Systems Theory], is described and illustrated on a set of adverse cardiovascular surgery events at a large medical center. The lessons that can be learned from the analysis are compared with those that can be derived from the typical root cause analysis techniques used today.
Results: The analysis of the 30 cardiovascular surgery adverse events using CAST revealed the reasons behind unsafe individual behavior, which were related to the design of the system involved and not negligence or incompetence on the part of individuals. With the use of the system-theoretic analysis results, recommendations can be generated to change the context in which decisions are made and thus improve decision making and reduce the risk of an accident.
Conclusions: The use of a systems-theoretic accident analysis technique can assist in identifying causal factors at all levels of the system without simply assigning blame to either the frontline clinicians or technicians involved. Identification of these causal factors in accidents will help health care systems learn from mistakes and design system-level changes to prevent them in the future.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1097/PTS.0000000000000263
