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# Over-, Under-reaction in Device Recall Decisions: Study
- URL: https://www.fdaweb.com/over-under-reaction-in-device-recall-decisions-study/
- Published: 2017-02-22T12:00:00.000Z
- Updated: 2026-09-14T22:08:11.000Z
- Author: David McFarland
- Tags: Devices, #legacy-id-D5138035

Decision makers in medical device firms over- or under-react to market feedback through user-generated reports on adverse events related to their products and making product recall decisions, according to a detailed “big data” analysis [reported](http://cdn.arentzlaw.com/wp-content/uploads/2017/02/Flaws-in-FDA-Medical-Devices-Monitoring-System-Paper.pdf?x25093&ref=fdaweb.com) in *Production and Operations Management*. The study authors say that the characteristics of the signal in adverse event reports and the situated context of the decision makers are significantly associated with judgment bias (over-, or under-reacting).

Thus, high noise-to-signal ratio in user feedback on adverse events is associated with under-reaction likelihood, and user feedback on adverse events characterized by high severity is associated with over-reaction likelihood. The situated context of managers, defined as firm size and product portfolio index, is positively associated with under-reaction likelihood.

The authors say it is important that regulators, such as FDA, recognize the sources of judgment bias in decisions related to product recalls, such as medical device recalls that are associated with injuries, hospitalizations, and deaths. They note that the Government Accountability Office (GAO) said in 2011 that FDA should use data on device usage available to them for better analysis and proactive management of device recalls to minimize the public health risks associated with product recalls. In a later study, they report, GAO said that user feedback on adverse events related to medical devices, the “big data” that were mined in the study, can provide early signals of potential medical device recalls.

“Towards that end,” they conclude, “the findings of this study will inform firms and government institutions (e.g., FDA and GAO) about the sources of judgment bias and improve the post-launch market surveillance of products (e.g., medical devices) by making it more evidence-based and predictive.”