A Classification System for Decision-Making in the Management of Patients with Chronic Conditions

Patients with chronic diseases are frequent users of healthcare services. The systematic use of stratification tools and predictive models for this group of patients can be useful for health professionals in decision-making processes. The aim of this study was to design two new classifier systems for detecting the risk of hospital admission for elderly patients with chronic conditions. In this retrospective cohort study, a set of variables related to hospital admission for patients with chronic conditions was obtained through focus groups, a health database analysis and statistical processing. To predict the probability of admission from the set of predictor variables, a logistic regression within the framework of Generalized Linear Models was used. The target population consisted of patients aged 65 years or older treated in February 2016 at the Primary Health Care Centre of Burjassot (Spain). This sample was selected through the consecutive sampling of the patient quotas of the physicians who participated in the study (1000 patients). The result was two classification systems, with reasonable values of 0.722 and 0.744 for the area under the ROC curve. The proposed classifier systems could facilitate a change in the current patient management models and make them more proactive.

Automated summary

This study aimed to design two new classifier systems to detect the risk of hospital admission for elderly patients with chronic conditions. Through logistic regression analysis of predictor variables, reasonable values of 0.722 and 0.744 were obtained for the area under the ROC curve.