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Enhancing risk stratification for use in integrated care - A cluster analysis of high-risk patients in a retrospective cohort study

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Title: Enhancing risk stratification for use in integrated care - A cluster analysis of high-risk patients in a retrospective cohort study
Authors: Vuik, SI
Mayer, E
Darzi, A
Item Type: Journal Article
Abstract: Objective To show how segmentation can enhance risk stratification tools for integrated care, by providing insight into different care usage patterns within the high-risk population. Design A retrospective cohort study. A risk score was calculated for each person using a logistic regression, which was then used to select the top 5% high-risk individuals. This population was segmented based on the usage of different care settings using a k-means cluster analysis. Data from 2008 to 2011 were used to create the risk score and segments, while 2012 data were used to understand the predictive abilities of the models. Setting and participants Data were collected from administrative data sets covering primary and secondary care for a random sample of 300 000 English patients. Main measures The high-risk population was segmented based on their usage of 4 different care settings: emergency acute care, elective acute care, outpatient care and GP care. Results While the risk strata predicted care usage at a high level, within the high-risk population, usage varied significantly. 4 different groups of high-risk patients could be identified. These 4 segments had distinct usage patterns across care settings, reflecting different levels and types of care needs. The 2008–2011 usage patterns of the 4 segments were consistent with the 2012 patterns. Discussion Cluster analyses revealed that the high-risk population is not homogeneous, as there exist 4 groups of patients with different needs across the care continuum. Since the patterns were predictive of future care use, they can be used to develop integrated care programmes tailored to these different groups. Conclusions Usage-based segmentation augments risk stratification by identifying patient groups with different care needs, around which integrated care programmes can be designed.
Issue Date: 1-Dec-2016
Date of Acceptance: 2-Nov-2016
URI: http://hdl.handle.net/10044/1/42294
DOI: https://dx.doi.org/10.1136/bmjopen-2016-012903
ISSN: 2044-6055
Publisher: BMJ Publishing Group
Journal / Book Title: BMJ Open
Volume: 6
Copyright Statement: © 2016 The Authors. Published by the BMJ Publishing Group Limited. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http:// creativecommons.org/licenses/by-nc/4.0/
Sponsor/Funder: The Peter Sowerby Charitable Foundation
Funder's Grant Number: N/A
Keywords: Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
General & Internal Medicine
REDUCING EMERGENCY ADMISSIONS
QUALITY-OF-LIFE
ASSESSING PATTERNS
HEALTH-CARE
POPULATION
READMISSION
OUTCOMES
PROFILE
EVENTS
HOME
STATISTICS & RESEARCH METHODS
Publication Status: Published
Article Number: e012903
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation