Background: To improve population health it is crucial to understand the different care needs within a population. Traditional population groups are often based on characteristics such as age or morbidities. However, this does not take into account specific care needs across care settings, and tends to focus on high needs patients only. This paper explores the potential of using utilisation-based cluster analysis to segment a general patient population into homogenous groups.

Methods: Administrative datasets covering primary and secondary care were used to construct a database of 300,000 patients, which included socio-demographics variables, morbidities, care utilisation, and cost. A k-means cluster analysis grouped the patients into segments with distinct care utilisation, based on six utilisation variables: non-elective inpatient admissions, elective inpatient admissions, outpatient visits, GP practice visits, GP home visits, and prescriptions. These segments were analysed post-hoc to understand their morbidity and demographic profile.

Results: Eight population segments were identified, and utilisation of each care setting was significantly different across all segments. Each segment also presented with different morbidity patterns and demographic characteristics, creating eight distinct care user types. Comparing these segments to traditional patient groups shows the heterogeneity of these approaches, especially for lower needs patients.

Conclusions: This analysis shows that utilisation-based cluster analysis segments a patient population into distinct groups with unique care priorities, providing a quantitative evidence base to improve population health. Contrary to traditional methods, this approach also segments lower needs populations, which can be used to inform preventative interventions. In addition, the identification of different care user types provides insight into needs across the care continuum.