Dealing with uncertainty when using a surveillance system. (August 2017)

• Record Type: Journal Article
• Authors: Texier, Gaëtan; Pellegrin, Liliane; Vignal, Claire; Meynard, Jean-Baptiste; Deparis, Xavier; Chaudet, Hervé
• Is Part Of: International journal of medical informatics. Volume 104(2017)
• Page Start: 65
• Page End: 73
• Publication Date: 2017-08
• Subjects: Disease surveillance system -- Outbreak -- Uncertainty -- Decision support system -- Expert decision making; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2017.05.006
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: This work investigates how experts develop strategies to address uncertainty during the management of an outbreak with the help of an early warning system. We confirm the level of uncertainty and quantify mechanisms involved in outbreak management. We detail tools and systems used to support experts in their coping strategies. We propose that surveillance systems include different features to provide relevant information that can help users reduce uncertainty. Abstract: Introduction: Epidemiologists manage outbreak identification and confirmation by means of a "situation diagnosis", which involves validating (or invalidating) an alarm (signal identified as abnormal) as an alert (a real, characterized outbreak) and proposing the first countermeasures. This work investigates how uncertainty is materialized during this stage, and how experts develop strategies to address this uncertainty with the help of an early warning system. Methods: We built an experiment using a simulation platform with a scenario involving both a natural and an intentional outbreak. Observations of expert activities were recorded and formalised using a specific task analysis method. These formatted data were then categorized by applying RAWFS ( Reduction- Assumption – Weighing − Forestalling- Suppression ) heuristics. Results: We quantified uncertainty and the mechanisms involved. During the situation diagnosis, two sorts of uncertainty were characterized: practice-imposed uncertainty and situation-imposed uncertainty. We did not find either weighing pros and cons or suppression strategies in this area of expertise, but highlight the predominance of coping strategies that relied on reduction (66, 4%) and assumption-based reasoning. We observed a predominance of the phone (89%) to cope with uncertainty and among electronic tools, the surveillance system plays a major role (69% of cases) and is mainly used in reduction strategies. We detail tools and systems used to support experts in their coping strategy. Conclusion: We confirmed that a surveillance system must include different features that provide relevant information to help users reduce uncertainty and thus support their decision making. In that perspective, the flow diagram and proposal presented in this study can help prioritize the necessary changes to surveillance system design.