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Models based on value and probability in health improve shared decision making
Author(s) -
Ortendahl Monica
Publication year - 2008
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2007.00931.x
Subject(s) - computer science , decision analysis , optimal decision , value (mathematics) , competence (human resources) , clinical decision making , management science , artificial intelligence , machine learning , psychology , statistics , mathematics , medicine , decision tree , social psychology , family medicine , economics
Rationale, aims and objectives Diagnostic reasoning and treatment decisions are a key competence of doctors. A model based on values and probability provides a conceptual framework for clinical judgments and decisions, and also facilitates the integration of clinical and biomedical knowledge into a diagnostic decision. Method Both value and probability are usually estimated values in clinical decision making. Therefore, model assumptions and parameter estimates should be continually assessed against data, and models should be revised accordingly. Introducing parameter estimates for both value and probability, which usually pertain in clinical work, gives the model labelled subjective expected utility. Estimated values and probabilities are involved sequentially for every step in the decision‐making process. Results Introducing decision‐analytic modelling gives a more complete picture of variables that influence the decisions carried out by the doctor and the patient. Conclusion A model revised for perceived values and probabilities by both the doctor and the patient could be used as a tool for engaging in a mutual and shared decision‐making process in clinical work.