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

Kelechi Ebere Nnoaham,Kimberley Frances Cann

doi:10.1186/s12889-020-08930-z

Kelechi Ebere Nnoaham, Kimberley Frances Cann

Open Access

https://doi.org/10.1186/s12889-020-08930-z

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Abstract

BackgroundPopulation 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.MethodsPrimary 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.ResultsTen 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.ConclusionsThis 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 integration of health and care, reducing fragmentation of healthcare and reducing healthcare costs in the population.

Highlights

Population segmentation is useful for understanding the health needs of populations
We identified seven healthcare utilisation variables on which cluster analyses were based – elective inpatient admissions, non-elective inpatient admissions, outpatient first attendances, outpatient follow-up attendances, Accident & Emergency (A&E) attendances, General Practice (GP) visits and count of distinct drugs used in the year
K-means cluster analysis produced ten segments based on healthcare utilisation patterns across diverse settings of health care provision (Table 1)

Summary

Introduction

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. Growing interest in population health is possibly due to recognition of the challenges facing health care systems – rising costs, ageing populations, unhealthy lifestyle choices and deepening poverty in society [2]. These challenges lend themselves to explanatory and interventional models inherent in the population health approach. At the core of this approach is the goal of improving health outcomes for whole populations – not just for those seeking care – while paying attention to the distribution of those outcomes within the population [3]

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