Archive (2016–2006)

Challenges in Measuring the Impact of Interruption on Patient Safety and Workflow Outcomes

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Topic:

Special Topic: Medinfo 2010
Guest Editor: A. Hasman

DOI: https://doi.org/10.3414/ME11-02-0003
Issue: 2011 (Vol. 50): Issue 5 2011
Pages: 447-453

Challenges in Measuring the Impact of Interruption on Patient Safety and Workflow Outcomes

Special Topic: Medinfo 2010

F. Magrabi (1), S. Y. W. Li (1), A. G. Dunn (1), E. Coeira (1)

(1) Centre for Health Informatics, Australian Institute for Health Innovation, University of New South Wales, Sydney, Australia

Keywords

Computer Simulation, evaluation studies, Medical error, efficiency, Safety, Interruption, observation

Summary

Objective: To examine the problem of studying interruption in healthcare. Methods: Review of the interruption literature from psychology, human-computer interaction; experimental studies of electronic prescribing and error behaviour; observational studies in emergency and intensive care. Results: Primary task and interruption variables which contribute to the outcomes of an interruption include the type of task (primary and interrupting task); point of interruption; duration of interruption; similarity of interruptive task to primary task; modality of interruption; environmental cues; and interruption handling strategy. Effects of interruption on task performance can be examined by measuring errors, the time on task, interruption lag and resumption lag. Conclusions: Interruptions are a complex phenomenon where multiple variables including the characteristics of primary tasks, the interruptions themselves, and the environment may influence patient safety and workflow outcomes. Observational studies present significant challenges for recording many of the process variables that influence the effects of interruptions. Controlled experiments provide an opportunity to examine the specific effects of variables on errors and efficiency. Computational models can be used to identify the situations in which interruptions to clinical tasks could be disruptive and to investigate the aggregate effects of interruptions.

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