Partial Personalization of Medical Treatment Decisions: Adverse Effects and Possible Solutions

• Published in: Medical decision making Vol. 42; no. 1; pp. 8 - 16
• Main Authors: Weyant, Christopher, Brandeau, Margaret L.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-01-2022
• Subjects: Antipsychotic Agents - therapeutic use; Humans; Patient Preference; Quality-Adjusted Life Years; Schizophrenia - drug therapy; Medical decision making; schizophrenia; personalized medicine
• ISSN: 0272-989X; 1552-681X
• DOI: 10.1177/0272989X211013773

Background
Personalizing medical treatment decisions based on patient-specific risks and/or preferences can improve health outcomes. Decision makers frequently select treatments based on partial personalization (e.g., personalization based on risks but not preferences or vice versa) due to a lack of data about patient-specific risks and preferences. However, partially personalizing treatment decisions based on a subset of patient risks and/or preferences can result in worse population-level health outcomes than no personalization and can increase the variance of population-level health outcomes.
Methods
We develop a new method for partially personalizing treatment decisions that avoids these problems. Using a case study of antipsychotic treatment for schizophrenia, as well as 4 additional illustrative examples, we demonstrate the adverse effects and our method for avoiding them.
Results
For the schizophrenia treatment case study, using a previously proposed modeling approach for personalizing treatment decisions and using only a subset of patient preferences regarding treatment efficacy and side effects, mean population-level health outcomes decreased by 0.04 quality-adjusted life-years (QALYs; 95% credible interval [crI]: 0.02–0.06) per patient compared with no personalization. Using our new method and considering the same subset of patient preferences, mean population-level health outcomes increased by 0.01 QALYs (95% crI: 0.00–0.03) per patient as compared with no personalization, and the variance decreased.
Limitations
We assumed a linear and additive utility function.
Conclusions
Selecting personalized treatments for patients should be done in a way that does not decrease expected population-level health outcomes and does not increase their variance, thereby resulting in worse risk-adjusted, population-level health outcomes compared with treatment selection with no personalization. Our method can be used to ensure this, thereby helping patients realize the benefits of treatment personalization without the potential harms.