Can cluster analyses of linked healthcare data identify unique population segments in a General Practice-registered population?
| Autor: | Kimberley Cann, Kelechi E Nnoaham |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Office Visits Care utilisation General Practice Population Population health Disease cluster Secondary Care Population segmentation 03 medical and health sciences Cluster analysis 0302 clinical medicine Environmental health Outpatients Health care Epidemiology medicine Humans 030212 general & internal medicine education education.field_of_study Primary Health Care business.industry lcsh:Public aspects of medicine 030503 health policy & services Public health Public Health Environmental and Occupational Health lcsh:RA1-1270 Health Care Costs Middle Aged Patient Acceptance of Health Care Hospitalization Population study Female Biostatistics Family Practice 0305 other medical science business Research Article |
| Zdroj: | BMC Public Health BMC Public Health, Vol 20, Iss 1, Pp 1-10 (2020) |
| DOI: | 10.21203/rs.2.12272/v2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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