The use of data for healthcare decision-making has numerous benefits, including increasing knowledge of user demographics and needs, enabling adequate planning of healthcare resources and services, and providing a roadmap of decisions made to ensure stakeholder accountability. Despite these clear benefits, frameworks and theories guiding decision making in healthcare remain under-utilised. This paper presents three decision-making theories that focus on data. Classical Decision Theory and its modern iterations emphasize the decision-making process and the use of data in this process. The Ottawa Decision Support Framework is employed when the decision relates to new diagnoses or treatments or when extensive deliberation is needed in uncertain circumstances. Lastly, Bayesian Decision Theory considers existing knowledge and cost functions in decision-making. The context in which these theories were developed and applied is discussed, and their future applications in healthcare decision-making are explored.

© 2023 The Author(s), licensee HBKU Press.

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/content/journals/10.5339/avi.2023.8

2024-02-12

2024-04-26

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