Roca, Pablo

<title>RESUMEN</title> <p>A pesar de que la Práctica Basada en la Evidencia (PBE) ha estandarizado los tratamientos psicológicos, la realidad clínica sigue enfrentándose a una barrera persistente: la dificultad de traducir los resultados de ensayos clínicos grupales a la singularidad de cada paciente en contextos reales. Como respuesta, la Salud Mental de Precisión (SMP) propone una evolución del modelo actual, integrando el cuidado basado en la medición y la toma de decisiones basada en datos para ajustar la intervención a las necesidades específicas de cada persona, gracias en gran medida a desarrollos tecnológicos recientes como la Inteligencia Artificial (IA). En este contexto, el presente artículo persigue dos objetivos fundamentales. En primer lugar, sintetiza los pilares operativos de la SMP, analizando cómo la medición rutinaria y las predicciones algorítmicas pueden potenciar el juicio clínico del profesional. En segundo lugar, se introduce el proyecto NOVA (Navigating Outcomes via Analytics), un proyecto traslacional diseñado para superar las barreras actitudinales, tecnológicas, estadísticas y contextuales de la implementación de herramientas de SMP en España. Se discute sobre los pasos necesarios para cerrar la brecha entre la innovación tecnológica y la práctica asistencial en psicología, acelerando la adopción sostenible de una psicología de precisión en el mundo real.</p>

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.

Abstract Background Venous thromboembolism (VTE) in pregnancy is a major cause of maternal morbidity and death. The use of low-molecular-weight heparin (LMWH), despite being the standard of care to prevent VTE, comes with some challenges. Shared decision-making (SDM) interventions are recommended to support patients and clinicians in making preference-sensitive decisions. The quality of the SDM process has been widely assessed with the decisional conflict scale (DCS). Our aim is to report participants’ perspectives of each of the components of an SDM intervention (DASH-TOP) in relation to the different subscales of the DCS. Methods Design: A convergent, parallel, mixed-methods design. Participants: The sample consisted of 22 health care professionals, students of an Applied Clinical Research in Health Sciences (ICACS) master program. Intervention: We randomly divided the participants in three groups: Group 1 received one component (evidence -based information), Group 2 received two components (first component and value elicitation exercises), and Group 3 received all three components (the first two and a decision analysis recommendation) of the SDM intervention. Analysis: For the quantitative strand, we used a non-parametric test to analyze the differences in the DCS subscales between the three groups. For the qualitative strand, we conducted a content analysis using the decisional conflict domains to deductively categorize the responses. Results Groups that received more intervention components experienced less conflict and better decision-making quality, although the differences between groups were not statistically significant. The decision analysis recommendation improved the efficacy with the decision-making process, however there are some challenges when implementing it in clinical practice. The uncertainty subscale showed a high decisional conflict for all three groups; contributing factors included low certainty of the evidence-based information provided and a perceived small effect of the drug to reduce the risk of a VTE event. Conclusions The DASH-TOP intervention reduced decisional conflict in the decision -making process, with decision analysis being the most effective component to improve the quality of the decision. There is a need for more implementation research to improve the delivery of SDM interventions in the clinical encounter.