RESUMEN Objetivo. Identificar los determinantes sociales de la adherencia terapéutica de los usuarios del Programa de Salud Cardiovascular (PSCV) de la atención primaria de salud. Método. Estudio observacional-descriptivo transversal en dos centros de salud familiar (CESFAM) de Valparaíso (Chile) entre marzo y junio del 2024. Se encuestaron 364 usuarios mayores de 18 años del PSCV, seleccionados mediante un muestreo estratificado proporcional. Para identificar los factores que inciden en la adherencia terapéutica, se diseñó una escala ad hoc que integra la prueba Morisky-Green-Levine-MMAS-4, las dimensiones del modelo de creencias en salud de Rosenstock, Janz y Becker, y del modelo de determinantes sociales de Dahlgren y Whitehead. Se utilizaron estadística descriptiva y pruebas de chi cuadrado para identificar asociaciones significativas entre adherencia y factores individuales, sociales y estructurales. Resultados. El 65,1% de las personas encuestadas presenta baja adherencia terapéutica y el 58,2%, baja adherencia farmacológica. La adherencia se asocia significativamente con factores individuales (edad), de estilo de vida (conocimiento de la enfermedad, motivación, adherencia a controles y alimentación, bajo estrés), redes de apoyo (apoyo familiar) y condiciones de vida (acceso y calidad de la atención en los CESFAM, satisfacción del usuario, barreras físicas y económicas). Conclusiones. Los hallazgos evidencian la influencia multidimensional de factores sociales y estructurales en la adherencia, subrayando la centralidad del nivel de atención primaria en el manejo de las enfermedades cardiovasculares. Desde un enfoque centrado en la persona y territorial, la implementación de estrategias que integren la educación en salud, el acompañamiento psicosocial y la eliminación de barreras de acceso podrían mejorar la adherencia terapéutica.

Background: Low comprehension and adherence to medical treatment among the elderly directly and negatively affect their health. Many elderly patients forget medical instructions immediately after their appointments, misunderstand them, or fail to recall them altogether. Some identified causes include the short time slots allocated for appointments in the public health system in Chile, the complex terminology used by healthcare professionals, and the stress experienced by patients during appointments. One approach to improving patients’ adherence to medical treatment is to combine written and oral instructions with graphical elements such as pictograms. However, several challenges arise due to the ambiguity of natural language and the need for pictograms to accurately represent various medication combinations, doses, and frequencies. Objective: This study introduces SIMAP (System for Integrating Medical Instructions with Pictograms), a technological framework aimed at enhancing adherence among asthma patients through the delivery of pictograms via a computational system. SIMAP utilizes a collaborative and user-centered methodology, involving health professionals and patients in the construction and validation of its components. Methods: The technological framework presented in this study is composed of three parts. The first two are medical indications and pictograms related to the treatment of the disease. Both components were developed through a comprehensive and iterative methodology that incorporates both qualitative and quantitative approaches. This methodology includes the utilization of focus groups, interviews, paper and online surveys, as well as expert validation, ensuring a robust and thorough development. The core of SIMAP is the technological component that leveraged artificial intelligence methods for natural language processing to analyze, tokenize, and associate words and their context to a set of one or more pictograms, addressing issues such as the ambiguity in the text, the cultural factor that involves many ways of expressing the same indication, and typographical errors in the indications. Results: Firstly, we successfully validated 18 clinical indications along with their respective pictograms. Some of the pictograms were redesigned based on the validation results. However, in the final validation, the comprehension percentages of the pictograms exceeded 70%. Furthermore, we developed a software called SIMAP, which translates medical indications into previously validated pictograms. Our proposed software, SIMAP, achieves a correct mapping rate of 96.69%. Conclusions: SIMAP demonstrates great potential as a technological component for supplementing medical instructions with pictograms when tested in a laboratory setting. The use of artificial intelligence for natural language processing can successfully map medical instructions, both structured and unstructured, into pictograms. This integration of textual instructions and pictograms holds promise for enhancing the comprehension and adherence of elderly patients to their medical indications, thereby improving their long-term health.