ArticleBMC health services research2019
Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study.
Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- How predictive of future healthcare utilisation and mortality is data-driven population segmentation based on healthcare utilisation and chronic condition comorbidity?BMC public health · 2024Article
- Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes.JMIR research protocols · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPopulation segmentation of patients into parsimonious and relatively homogenous subgroups or segments based on healthcare requirements can aid healthcare resource planning and the development of targeted intervention programs. In this study, we evaluated the predictive ability of a previously described expert-defined segmentation approach on 3-year hospital utilization and mortality.
methodsWe segmented all adult patients who had a healthcare encounter with Singapore Health Services (SingHealth) in 2012 using the SingHealth Electronic Health Records (SingHealth EHRs). Patients were divided into non-overlapping segments defined as Mostly Healthy, Stable Chronic, Serious Acute, Complex Chronic without Frequent Hospital Admissions, Complex Chronic with Frequent Hospital Admissions, and End of Life, using a previously described expert-defined segmentation approach. Hospital admissions, emergency department attendances (ED), specialist outpatient clinic attendances (SOC) and mortality in different patient subgroups were analyzed from 2013 to 2015.
results819,993 patients were included in this study. Patients in Complex Chronic with Frequent Hospital Admissions segment were most likely to have a hospital admission (IRR 22.7; p < 0.001) and ED visit (IRR 14.5; p < 0.001) in the follow-on 3 years compared to other segments. Patients in the End of Life and Complex Chronic with Frequent Hospital Admissions segments had the lowest three-year survival rates of 58.2 and 62.6% respectively whereas other segments had survival rates of above 90% after 3 years.
conclusionIn this study, we demonstrated the predictive ability of an expert-driven segmentation framework on longitudinal healthcare utilization and mortality.
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