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This study introduces P4-DT, a Personalized Patient Preference Predictor that utilizes Dilemma Training to enhance the prediction of patient treatment choices by engaging users in varied medical dilemmas. By employing bi-directional training, P4-DT achieved an accuracy of 81.7%, significantly surpassing both unassisted surrogates and those assisted by the model. The findings highlight the importance of contextual and situation-dependent reasoning in improving decision-making accuracy in healthcare settings.
P4-DT outperformed human surrogates in predicting patient preferences, achieving an impressive 81.7% accuracy by leveraging contextual dilemmas.
In serious illness, human surrogates often struggle to accurately predict patient preferences (68% accuracy), causing decision conflict. Personalized Patient Preference Predictor (P4) agents offer a potential solution, but prior prototypes treat values as static ratings, ignoring the contextual, situation-dependent nature of medical choices. Grounded in the'logic of care', we present P4-DT (Dilemma Training), a P4 agent that constructs a patient decision policy by engaging users with varied medical dilemmas, eliciting individual preference reasoning through bi-directional training. In a study with 12 patient-surrogate dyads, P4-DT predicted patient treatment choices with 81.7% accuracy, significantly exceeding chance (OR = 5.61 [2.03, 15.51], p<.001) and outperforming both unassisted surrogates (55.0%; OR = 3.67 [1.59, 8.47], p = .002) and surrogates assisted by P4-DT (61.7%). Comparative prompt analyses showed that incorporating contextual scenario decisions and open-ended text improved accuracy by 15.0 percentage points over initial values ratings alone. We discuss implications for further testing and designing of context-aware AI agents that embody richer human experience to partner in complex decision-making.
