Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0195243
Title: Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality
Authors: Low L.L. 
Yan S.
Kwan Y.H.
Tan C.S. 
Thumboo J. 
Keywords: adult
aged
Article
atrial fibrillation
chronic disease
chronic liver disease
chronic obstructive lung disease
cluster analysis
controlled study
cor pulmonale
disease burden
emergency ward
end stage renal disease
female
health care utilization
heart failure
hospital admission
human
hyperlipidemia
hypertension
longitudinal study
major clinical study
male
middle aged
morbidity
mortality rate
mortality risk
outpatient care
peripheral vascular disease
primary medical care
prostate hypertrophy
Singapore
survival time
ambulatory care
cluster analysis
electronic health record
epidemiology
health care cost
health care delivery
health care planning
hospital emergency service
hospital patient
hospitalization
outpatient
patient attitude
reproducibility
trends
utilization
Ambulatory Care
Cluster Analysis
Delivery of Health Care
Electronic Health Records
Emergency Service, Hospital
Female
Health Care Costs
Health Resources
Hospitalization
Humans
Inpatients
Longitudinal Studies
Male
Outpatients
Patient Acceptance of Health Care
Reproducibility of Results
Singapore
Issue Date: 2018
Publisher: Public Library of Science
Citation: Low L.L., Yan S., Kwan Y.H., Tan C.S., Thumboo J. (2018). Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality. PLoS ONE 13 (4) : e0195243. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0195243
Abstract: Background Segmentation of heterogeneous patient populations into parsimonious and relatively homogenous groups with similar healthcare needs can facilitate healthcare resource planning and development of effective integrated healthcare interventions for each segment. We aimed to apply a data-driven, healthcare utilization-based clustering analysis to segment a regional health system patient population and validate its discriminative ability on 4-year longitudinal healthcare utilization and mortality data. Methods We extracted data from the Singapore Health Services Electronic Health Intelligence System, an electronic medical record database that included healthcare utilization (inpatient admissions, specialist outpatient clinic visits, emergency department visits, and primary care clinic visits), mortality, diseases, and demographics for all adult Singapore residents who resided in and had a healthcare encounter with our regional health system in 2012. Hierarchical clustering analysis (Ward’s linkage) and K-means cluster analysis using age and healthcare utilization data in 2012 were applied to segment the selected population. These segments were compared using their demographics (other than age) and morbidities in 2012, and longitudinal healthcare utilization and mortality from 2013–2016. Results Among 146,999 subjects, five distinct patient segments “Young, healthy”; “Middle age, healthy”; “Stable, chronic disease”; “Complicated chronic disease” and “Frequent admitters” were identified. Healthcare utilization patterns in 2012, morbidity patterns and demographics differed significantly across all segments. The “Frequent admitters” segment had the smallest number of patients (1.79% of the population) but consumed 69% of inpatient admissions, 77% of specialist outpatient visits, 54% of emergency department visits, and 23% of primary care clinic visits in 2012. 11.5% and 31.2% of this segment has end stage renal failure and malignancy respectively. The validity of cluster-analysis derived segments is supported by discriminative ability for longitudinal healthcare utilization and mortality from 2013–2016. Incident rate ratios for healthcare utilization and Cox hazards ratio for mortality increased as patient segments increased in complexity. Patients in the “Frequent admitters” segment accounted for a disproportionate healthcare utilization and 8.16 times higher mortality rate. Conclusion Our data-driven clustering analysis on a general patient population in Singapore identified five patient segments with distinct longitudinal healthcare utilization patterns and mortality risk to provide an evidence-based segmentation of a regional health system’s healthcare needs. © 2018 Low et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/165903
ISSN: 19326203
DOI: 10.1371/journal.pone.0195243
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