ArticleAtencion primaria2026
[Definition of polyconsultant patients in primary health care using Data Science].
Article in Atencion primaria, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
introductionThe concept of frequent patients (FP) has been approached from different perspectives: economic, efficiency, opportunity, effectiveness, and impact on resources allocated to healthcare. The various definitions of FP are associated with frequencies or percentiles of consultations in health services, within a specific period, but standardized within that range.
objectiveTo generate a dynamic definition of FP for the High-Resolution Emergency Primary Care Service (SAR) using Data Science. METHODOLOGY: Quantitative, descriptive, retrospective research using secondary data from patients treated in a SAR in Chile, employing Data Science.
results174,593 patient visits were analyzed, corresponding to 55,715 patients. Text, categorical, and numerical variables were used in univariate, bivariate, and multivariate analyses. A patient FP was identified as someone who attends more than once every two months, meaning they consult ≥7times per year. Using clustering, over 90% of FPs were identified, with 9.1 visits, an average duration of 0.5hours, frequent visits to the system every 40.3days, an average age of 34.3years, and a discharge diagnosis classified in ICD-10 Family «J».
conclusionsThis type of segmentation using text, categorical, and numerical variables allows for a dynamic approach to the concept of FP, using Data Science based on the distributional configuration of the study population. These results are relevant for decision-making, considering the context and characteristics of the population, to optimize system resources according to the dynamics of society in a given geographic area.
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