80
IRUS Total
Downloads

A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population

File Description SizeFormat 
art%3A10.1186%2Fs12963-016-0115-z.pdfPublished version928.24 kBAdobe PDFView/Open
Title: A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population
Authors: Vuik, SI
Mayer, E
Darzi, A
Item Type: Journal Article
Abstract: 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.
Issue Date: 25-Nov-2016
Date of Acceptance: 25-Oct-2016
URI: http://hdl.handle.net/10044/1/42575
DOI: https://dx.doi.org/10.1186/s12963-016-0115-z
ISSN: 1478-7954
Publisher: BioMed Central
Journal / Book Title: Population Health Metrics
Volume: 14
Copyright Statement: © The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Sponsor/Funder: The Peter Sowerby Charitable Foundation
Funder's Grant Number: N/A
Keywords: General & Internal Medicine
1117 Public Health And Health Services
Publication Status: Published
Article Number: 44
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation