Can cluster analyses of linked healthcare data identify unique population segments in a General Practice-registered population?

Abstract

Background: Population segmentation is useful for understanding the health needs of populations.Expert-driven segmentation is a traditional approach which involves subjective decisions on how to segment data, with no agreed best practice. The limitations of this approach are theoretically overcome by more data-driven approaches such as utilisation-based cluster analysis. Previous explorations of using utilisation-based cluster analysis for segmentation have demonstrated feasibility but were limited in potential usefulness for local service planning. This study explores the potential for practical application of using utilisation-based cluster analyses to segment a local General Practice-registered population in the South Wales Valleys.Methods: Primary and secondary care datasets were linked to create a database of 79,607 patients including socio-demographic variables, morbidities, care utilisation, cost and risk factor information.We undertook utilisation-based cluster analysis, using k-means methodology to group the population into segments with distinct healthcare utilisation patterns based on seven utilisation variables: elective inpatient admissions, non-elective inpatient admissions, outpatient first & follow-up attendances, Emergency Department visits, GP practice visits and prescriptions. We analysed segments post-hoc to understand their morbidity, risk and demographic profiles Results: Ten population segments were identified which had distinct profiles of healthcare use, morbidity, demographic characteristics and risk attributes. Although half of the study population were in segments characterised as 'low need' populations, there was heterogeneity in this group with respect to variables relevant to service planning -e.g. settings in which care was mostly consumed.Significant and complex healthcare need was a feature across age groups and was driven more by deprivation and behavioural risk factors than by age and functional limitation.Conclusions: This analysis shows that utilisation-based cluster analysis of linked primary and secondary healthcare use data for a local GP-registered population can segment the population into distinct groups with unique health and care needs, providing useful intelligence to inform local population health service planning and care delivery. This segmentation approach can offer a detailed understanding of the health and care priorities of population groups, potentially supporting the

Segmentation of health-care consumers: psychological determinants of subjective health and other person-related variables

“…Studies that include psychological factors (as a base for segmentation) are rare (e.g. [ 29 – 35 ]). It should be mentioned that all these studies are empirically driven.…”

Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare

“…There are two broad approaches to segmentation [6]. The traditional approach is to use a priori groups based on expert knowledge, for instance segmenting patients by morbidity, age, or disease.…”

“…The second, more data-driven approach has been adopted recently and addresses many of the limitations of a priori methods, allowing more variables to be used, and for feature breakpoints to be derived naturally [6,7]. However, this approach requires a large amount of data and may result in less readily comprehensible clusters.…”

“…However, this approach requires a large amount of data and may result in less readily comprehensible clusters. For example, while a priori segmentation allows precise classification of segments [2], data-driven methods can result in more nuanced groupings, requiring interpretation [6]. Therefore, the purpose of the segmentation is likely to influence the method chosen.…”

Moving Beyond Simple Risk Prediction: Segmenting Patient Populations Using Consumer Data

“…Population health aims to improve the overall health of a population across the full continuum of care by more targeted, effective and coordinated health services (3). Given the rising trend of aging population and chronic disease burden, managing population health becomes more important for health systems trying to control cost (4).…”