From Formal Theory to Personalized Treatment: A Precision Mental Health Roadmap

Evidence-based psychotherapies benefit many patients, but individual outcomes vary and guidance for personalizing treatment remains limited. The verbal theories underlying these treatments describe processes thought to produce improvement but often leave their strength, direction, functional form, and timescales unspecified. This limits theory-informed predictions about how a particular patient would respond to alternative treatments. Researchers have begun formalizing theories of psychopathology in mathematical or computational form to address these limitations. Formal theories make theoretical assumptions explicit, allowing precise and testable predictions to be deduced analytically or through computational models. When these models represent treatment effects and relevant individual differences, researchers can use patient measurements to inform model states and parameters, simulate alternative treatment configurations, and compare predicted outcomes. Empirical evaluation must distinguish the accuracy of outcome predictions, support for the proposed mechanisms, and whether model-guided selection or adaptation improves outcomes relative to other treatment selection strategies. Translating this potential into practice requires aligning the theory with patient assessment, treatment delivery, and evaluation. We propose a roadmap for this process and describe conditions supporting the generation, delivery, and testing of formal theory-driven treatment recommendations. A worked example using a formal theory of panic disorder and a modular digital therapy illustrates the roadmap’s early phases, including provisional model extensions that generate different treatment predictions for two simulated patients. We conclude by discussing challenges in theory development, patient matching, measurement, and empirical evaluation that must be addressed before these predictions can guide clinical care.