Bretos Azcona, Pablo Evaristo
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Bretos Azcona
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Pablo Evaristo
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EconomĆa
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Publication Open Access Tailoring integrated care services for high-risk patients with multiple chronic conditions: a risk stratification approach using cluster analysis(BioMed Central, 2020) Bretos Azcona, Pablo Evaristo; SĆ”nchez Iriso, Eduardo; CabasĆ©s Hita, Juan Manuel; EconomĆa; Ekonomia; Gobierno de Navarra / Nafarroako GobernuaBackground: The purpose of this study was to produce a risk stratification within a population of high-risk patients with multiple chronic conditions who are currently treated under a case management program and to explore the existence of different risk subgroups. Different care strategies were then suggested for healthcare reform according to the characteristics of each subgroup. Methods: All high-risk multimorbid patients from a case management program in the Navarra region of Spain were included in the study (n = 885). A 1-year mortality risk score was estimated for each patient by logistic regression. The population was then divided into subgroups according to the patients' estimated risk scores. We used cluster analysis to produce the stratification with Ward's linkage hierarchical algorithm. The characteristics of the resulting subgroups were analyzed, and post hoc pairwise tests were performed. Results: Three distinct risk strata were found, containing 45, 38 and 17% of patients. Age increased from cluster to cluster, and functional status, clinical severity, nursing needs and nutritional values deteriorated. Patients in cluster 1 had lower renal deterioration values, and patients in cluster 3 had higher rates of pressure skin ulcers, higher rates of cerebrovascular disease and dementia, and lower prevalence rates of chronic obstructive pulmonary disease. Conclusions: This study demonstrates the existence of distinct subgroups within a population of high-risk patients with multiple chronic conditions. Current case management integrated care programs use a uniform treatment strategy for patients who have diverse needs. Alternative treatment strategies should be considered to fit the needs of each patient subgroup.Publication Open Access A risk stratification using machine learning techniques to identify types of high-risk multiple chronic conditions patients, their needs and subsequent organization of integrated care services(2021) Bretos Azcona, Pablo Evaristo; CabasĆ©s Hita, Juan Manuel; SĆ”nchez Iriso, Eduardo; EconomĆa; EkonomiaEsta tesis tiene como objetivo responder a la pregunta clave de si la poblaciĆ³n de pacientes con MEC de alto riesgo incluida en los programas de gestiĆ³n de casos es heterogĆ©nea en tĆ©rminos de riesgo. Para ello, se presenta una estratificaciĆ³n de riesgo que determina si y cuĆ”ntas subpoblaciones de pacientes existen, asĆ como sus caracterĆsticas particulares. Posteriormente, se presentan diferentes opciones para organizar y planificar los cuidados para cada subpoblaciĆ³n resultante. Esta estratificaciĆ³n ayudarĆ” a identificar aquellos subgrupos de pacientes que no se benefician de su atenciĆ³n actual y a adaptar las estrategias de atenciĆ³n para ellos, dirigiendo la atenciĆ³n adecuada a los pacientes adecuados. TambiĆ©n ayuda a mejorar la eficiencia de los cuidados de los pacientes con MEC de alto riesgo. Finalmente, se exploraron los patrones de supervivencia de los subgrupos de pacientes resultantes, con el objeto de estimar el tiempo hasta la muerte para cada tipo de paciente. Estos datos ayudan a planificar los cuidados de final de vida para la poblaciĆ³n de interĆ©s. Una de las novedades que se presentan en esta tesis es que se utilizaron mĆ©todos de Inteligencia Artificial (IA) para estratificar la poblaciĆ³n en diferentes subgrupos. En particular, se utilizaron algoritmos de aprendizaje automĆ”tico ('machine learning'). Esta tesis busca mejorar los resultados de salud y la atenciĆ³n brindada a los pacientes con MEC de alto riesgo incluidos actualmente en el programa de gestiĆ³n de casos del SNS-O. La estratificaciĆ³n del riesgo presentada aquĆ consigue este propĆ³sito, identificando tipos de pacientes y ayudando a una organizaciĆ³n y planificaciĆ³n de la atenciĆ³n que se adapte mejor a las necesidades de cada uno de ellos.