Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning.

Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients' and healthcare system's proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors.

[Morbidity observed in a health area: Impact on professionals and funding].

Objective: To analyze morbidity, in the context of a health area, and broken down by health centre, of patients who made contact with healthcare services, in order to propose an adjustment to finance the payment per capita.

Design: A descriptive study of morbidity observed in citizens assigned a health area during year 2010. SITE: Health Area 9.