CLINICAL RISK ANALYSIS OF PATIENTS WITH NON-COMMUNICABLE DISEASES USING DATA-DRIVEN PREDICTIVE MODELS IN PRIMARY HEALTHCARE FACILITIES

Abstract

This study aims to analyze clinical risk among patients with non-communicable diseases, particularly diabetes mellitus and hypertension, through the application of data-driven predictive models in primary healthcare facilities. A qualitative approach with a case study design was employed to obtain an in-depth understanding of clinical risk assessment practices and the utilization of data in healthcare decision-making processes. This approach was selected because the study does not merely focus on analytical outcomes but also explores contextual dimensions, professional experiences, and healthcare workers’ perceptions regarding the implementation of predictive approaches in routine services. The research was conducted at a primary healthcare center in Sukabumi City, West Java Province, which was purposively selected based on the availability of patient clinical data and the continuity of non-communicable disease management services. Three key informants participated in this study, consisting of a physician responsible for non-communicable disease services, a nurse implementing chronic disease management programs, and the head of the primary healthcare center. These informants were chosen due to their strategic roles in clinical, operational, and managerial aspects of healthcare delivery. The findings indicate that the utilization of data-driven predictive models supports a more systematic identification of patients’ clinical risk and demonstrates the potential to improve the quality of clinical decision-making. This study recommends strengthening clinical data management, enhancing the analytical capacity of healthcare personnel, and integrating predictive analytics into primary healthcare service systems.