Big Data en salud: evidencia literaria para extraer principios de diseño que orienten la trasformación de la atención, usando el enfoque CIMO
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Fecha
2026
Autores
Agamez Fonseca, Aramis Rafael
Álvarez Domínguez, Antonio José
Montalvo Rojas, Leidy Keren
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Ediciones Universidad Simón Bolívar
Facultad de Administración y Negocios
Facultad de Administración y Negocios
Resumen
La transformación digital del sector salud ha generado una producción masiva de datos clínicos, administrativos y poblacionales, impulsada por la expansión de historias clínicas electrónicas, sistemas interoperables y herramientas de analítica avanzada. Sin embargo, pese al crecimiento sostenido del mercado de análisis de datos en salud y al aumento exponencial de información disponible, persiste una brecha significativa entre el potencial del Big Data y su aplicación efectiva en el rediseño de modelos de atención que generen valor clínico y operativo medible. Esta brecha constituye el problema central de la investigación: la limitada conversión de evidencia dispersa en reglas prácticas transferibles para la transformación sostenible de la atención en salud. El objetivo general del estudio fue analizar la evidencia literaria sobre Big Data aplicada a modelos de atención en salud, con el fin de extraer principios de diseño verificables que orienten procesos de transformación organizacional. Para ello, se formularon preguntas orientadas a identificar qué intervenciones han sido implementadas, en qué contextos organizacionales, mediante qué mecanismos y con qué resultados, bajo una lógica explicativa que permitiera trascender la descripción de casos aislados. El marco teórico integra cuatro perspectivas complementarias: (1) la noción de sistema de salud que aprende, que concibe los datos como insumo para mejora continua; (2) la teoría sociotécnica en tecnologías de información en salud, que enfatiza la interacción entre tecnología, personas y procesos; (3) la interoperabilidad y la historia clínica electrónica como infraestructura habilitadora; y (4) la ciencia de la implementación, que explica los determinantes de adopción, sostenibilidad y escalamiento de intervenciones digitales. Como marco de síntesis se empleó el enfoque CIMO (Contexto–Intervención–Mecanismo–Resultado), utilizado para estructurar configuraciones causales y derivar principios aplicables.
The digital transformation of the healthcare sector has led to a massive production of clinical, administrative, and population-level data, driven by the widespread adoption of electronic health records, interoperable systems, and advanced analytics tools. Despite sustained market growth in health data analytics and the exponential increase in available information, a significant gap persists between the strategic potential of Big Data and its effective application in redesigning care delivery models capable of generating measurable clinical and operational value. This gap constitutes the central problem of this research: the limited translation of dispersed evidence into transferable and practical design principles that support sustainable healthcare transformation. The general objective of the study was to analyze the scientific literature on Big Data applied to healthcare delivery models in order to extract verifiable design principles that can guide organizational transformation processes. To achieve this, research questions were formulated to identify which interventions have been implemented, under what organizational contexts, through which mechanisms, and with what reported outcomes. The study adopts an explanatory logic that goes beyond descriptive case reporting and seeks to understand causal configurations that enable or constrain value generation. The theoretical framework integrates four complementary perspectives: (1) the Learning Health System approach, which conceives data as a driver of continuous improvement; (2) sociotechnical theory in health information technologies, emphasizing the interaction between technology, people, and organizational processes; (3) interoperability and electronic health records as enabling infrastructure; and (4) implementation science, which explains determinants of adoption, sustainability, and scalability of digital interventions. As a synthesis framework, the CIMO approach (Context–Intervention–Mechanism–Outcome) was employed to structure causal configurations and derive actionable design principles.
The digital transformation of the healthcare sector has led to a massive production of clinical, administrative, and population-level data, driven by the widespread adoption of electronic health records, interoperable systems, and advanced analytics tools. Despite sustained market growth in health data analytics and the exponential increase in available information, a significant gap persists between the strategic potential of Big Data and its effective application in redesigning care delivery models capable of generating measurable clinical and operational value. This gap constitutes the central problem of this research: the limited translation of dispersed evidence into transferable and practical design principles that support sustainable healthcare transformation. The general objective of the study was to analyze the scientific literature on Big Data applied to healthcare delivery models in order to extract verifiable design principles that can guide organizational transformation processes. To achieve this, research questions were formulated to identify which interventions have been implemented, under what organizational contexts, through which mechanisms, and with what reported outcomes. The study adopts an explanatory logic that goes beyond descriptive case reporting and seeks to understand causal configurations that enable or constrain value generation. The theoretical framework integrates four complementary perspectives: (1) the Learning Health System approach, which conceives data as a driver of continuous improvement; (2) sociotechnical theory in health information technologies, emphasizing the interaction between technology, people, and organizational processes; (3) interoperability and electronic health records as enabling infrastructure; and (4) implementation science, which explains determinants of adoption, sustainability, and scalability of digital interventions. As a synthesis framework, the CIMO approach (Context–Intervention–Mechanism–Outcome) was employed to structure causal configurations and derive actionable design principles.
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Palabras clave
Big Data, Transformación Sanitaria, Interoperabilidad, CIMO
