Dataset Information

Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality.

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.

SUBMITTER: Low LL

PROVIDER: S-EPMC5886524 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality.

Low Lian Leng LL Yan Shi S Kwan Yu Heng YH Tan Chuen Seng CS Thumboo Julian J

PloS one 20180405 4

<h4>Background</h4>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 ...[more]

PMID: 29621280

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Project description:Delirium severity has been associated with a higher risk of mortality and an increasing morbidity burden. Recently defined delirium severity trajectories were predictive of 30-day mortality in a critically ill patient population. No studies to date have examined associations between delirium severity trajectories and 2-year mortality and healthcare utilization outcomes.ObjectivesTo examine the associations between recently defined delirium severity trajectories and 2-year healthcare utilization outcomes of emergency department visits, rehospitalizations, and mortality.Design setting and participantsThis is a secondary analysis using data from the randomized controlled clinical trial Pharmacological Management of Delirium in the Intensive Care Unit and Deprescribing in the Pharmacologic Management of Delirium trial conducted from 2009 to 2015. Patients who were greater than or equal to 18 years old, were in the ICU for greater than or equal to 24 hours, and had a positive delirium assessment (Confusion Assessment Method for the ICU) were included in the original trial. Participants were included in the secondary analysis if 2-year healthcare utilization and mortality data were available (n = 431).Main outcomes and measuresHealthcare utilization data within 2 years of the initial discharge date were pulled from the Indiana Network for Patient Care. Data over a 2-year period on emergency department visits (days to first emergency department visit, number of emergency department visits), inpatient hospitalizations (days to first hospitalizations, number of hospitalizations), and mortality (time to death) were extracted. Univariate relationships, Cox proportional hazard models, and competing risk modeling were used to examine statistical relationships in SAS v9.4.ResultsThe overall sample (n = 431) had a mean age of 60 (sd, 16), 56% were females, and 49% African-Americans. No significant associations were identified between delirium severity trajectories and time to event for emergency department visit, mortality, or rehospitalization within 2 years of the index hospital discharge.Conclusions and relevanceThis secondary analysis did not identify a significant relationship between delirium severity trajectories and healthcare utilization or mortality within 2 years of hospital discharge.

Dataset Information

Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality.

Background

Methods

Results

Conclusion

Publications

Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality.

Similar Datasets

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