Managing near-miss reporting in hospitals: The dynamics between staff members’ willingness to report and management's handling of near-miss events

Hila Caspi, Yael Perlman, Sara Westreich

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In healthcare institutions, reporting and handling of near-miss events—events that could have resulted in catastrophe but did not—are considered crucial to patient safety. Empirical studies have explored factors that influence near-miss reporting behavior in specific healthcare settings, yet they are limited in their capacity to provide general insights regarding how best to achieve desired results. The current study formulates an analytical game-theoretic model to obtain such insights. The model describes the dynamics between hospital staff and management with regard to the reporting and handling of near-miss events. Staff members decide on the extent to which they report near-miss events or ignore them. Their decisions are driven by their own inherent willingness to report, coupled with management's handling of near-miss events. Management, in turn, decides on how thoroughly to handle near-miss events. Three versions of the model are analyzed: a game in which staff members’ inherent willingness to report is given; a Bayesian game in which willingness to report is a random variable; and an evolutionary game considering multiple interactions, in which higher-payoff strategies prevail over lower-payoff strategies. The dynamics described are demonstrated in a numerical example based on prior empirical data.

Original languageEnglish
Article number106147
JournalSafety Science
Volume164
DOIs
StatePublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Funding

This research was supported in part by “The Israel National Institute for Health Policy Research (NIHP)”, grant No. 2020/326.

FundersFunder number
Israel National Institute for Health Policy Research2020/326

    Keywords

    • Bayesian games
    • Evolutionary games
    • Mixed strategies equilibrium
    • Near-miss events
    • Patients’ safety

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