Respecting patient autonomy and delivering goal-concordant care require an understanding of individual preferences. However, the preferences of incapacitated patients are often unknown and substituted judgment is fraught with high levels of inaccuracy. Ethicists have suggested a patient preference predictor (PPP) trained with machine learning (ML) to increase the accuracy of substituted judgment, but one has not yet been developed. Here, we present the first proof of concept of an ML-based PPP.

Using population-representative data from 1811 Swiss participants of the Survey of Health, Ageing and Retirement in Europe, we evaluated several ML techniques to create a PPP and we employed a commonly used, explainable ML method to identify the best-performing model. Reflecting different use scenarios, we trained three models: a simple model based on demographic data; a clinical model trained on data that are likely to be available in electronic health records; and a personalized model incorporating richer information on individual preferences.

All three models outperformed the partners of index persons in our sample in accurately predicting whether the person would prefer cardiopulmonary resuscitation in the event of cardiorespiratory arrest. With a mean fivefold cross-validated accuracy of up to 70.6% (standard deviation ±1.3%), our models also performed on par with or better than typical estimates of surrogate predictive accuracy in the literature.

This study demonstrates that an ML-based PPP can improve surrogate decision-making. While highlighting technical and conceptual limitations, we hold this to be a major contribution to the efforts to improve care and fully respect patient autonomy.