A comparison of population segmentation methods. (September 2019)

• Record Type: Journal Article
• Authors: Wood, R.M.; Murch, B.J.; Betteridge, R.C.
• Is Part Of: Operations research for health care. Volume 22(2019)
• Publication Date: 2019-09
• Subjects: Population health -- Population segmentation -- Decision trees -- Cluster analysis -- Healthcare utilisation; Medical care -- Mathematical models -- Periodicals; Medical policy -- Mathematical models -- Periodicals; Health services administration -- Mathematical models -- Periodicals; Operations research -- Periodicals; Operations Research -- Periodicals; Health Services Research -- Periodicals; Health Policy -- Periodicals; Delivery of Health Care -- Periodicals; 362.106805
• DOI: 10.1016/j.orhc.2019.100192
• Languages: English
• ISSNs: 2211-6923

Abstract

Abstract: This paper presents the first comparison of descriptive segmentation methods for population health management. The aim of descriptive segmentation is to identify heterogeneous segments according to some target observed measure. In healthcare it can be used to understand how utilisation is distributed among a population, and to identify the patient attributes which explain the greatest differences (knowledge of which can help shape segment-tailored services). In reviewing a number of segmentation methods that are both employed on the ground and explored more experimentally within the academic literature, this paper aims to open up a range of options allowing clinicians and managers an informed choice on which approach to use for their situation. Results support the recommendation that decision tree approaches are on-the-whole most suitable, being configurable to local data and providing the best inter-segment discrimination. More basic judgemental splits on patient attributes can be powerful, with the count of chronic conditions being a key variable. Prescribed binning methods such as Bridges to Health are unlikely to achieve high levels of discrimination but do have easily interpretable segments and could be useful for benchmarking. Clustering methods are found to lack discriminative power, which can be attributed to a lack of conceptual appropriateness to the problem. Highlights: Addresses a gap in the literature regarding population segmentation methods. Provides a side-by-side comparison of methods used in practice and by researchers. Results show that locally-calibrated decision trees offer the best discrimination. Findings provide useful advice to healthcare managers on the ground.