Evidence-Based Practice (EBP) has increased the availability of psychological treatments, yet many people do not benefit from therapy, some report deteriorations in symptoms, and dropout rates remain high. Precision Mental Health (PMH) is proposed as an extension of EBP by combining systematic measurement with predictive analytics to support the right intervention, at the right time, for the right person. Recent advances in Artificial Intelligence (AI) make PMH increasingly feasible in routine psychotherapy; however, the implementation of these approaches in routine care is still incipient. In this context, the present article has two main aims. First, we summarize key advances in PMH, particularly measurement-based care and data-informed decision making. Second, we introduce the NOVA project (Navigating Outcomes via Analytics), a multi-phase translational program designed to implement PMH in real-world psychological services. Guided by the Implementing Precision Methods framework, NOVA integrates (i) stakeholder-informed work on clinician acceptability and intention to use, (ii) pragmatic evaluation of decision support tools in routine care, (iii) development of robust and interpretable predictive models, and (iv) training and dissemination activities aligned with responsible innovation and professional competencies for AI-supported precision care. By detailing NOVA’s implementation pathway, we aim to provide a concrete roadmap for bridging AI innovation and psychological practice, accelerating the sustainable adoption of PMH in real-world settings.
Roca et al. (Fri,) studied this question.