title: Patient Segmentation Analysis Offers Significant Benefits For Integrated Care And Support.
abstract: Integrated care aims to organize care around the patient instead of the provider. It is therefore crucial to understand differences across patients and their needs. Segmentation analysis that uses big data can help divide a patient population into distinct groups, which can then be targeted with care models and intervention programs tailored to their needs. In this article we explore the potential applications of patient segmentation in integrated care. We propose a framework for population strategies in integrated care-whole populations, subpopulations, and high-risk populations-and show how patient segmentation can support these strategies. Through international case examples, we illustrate practical considerations such as choosing a segmentation logic, accessing data, and tailoring care models. Important issues for policy makers to consider are trade-offs between simplicity and precision, trade-offs between customized and off-the-shelf solutions, and the availability of linked data sets. We conclude that segmentation can provide many benefits to integrated care, and we encourage policy makers to support its use.
authors.affiliation: Sabine I. Vuik (s.vuik@imperial.ac.uk) is a policy fellow at the Institute of Global Health Innovation, Imperial College London, in the United Kingdom.
authors.forename: Sabine I
authors.initials: SI
authors.lastname: Vuik
authors.affiliation: Erik K. Mayer is a clinical senior lecturer at the Centre for Health Policy, Imperial College London.
authors.forename: Erik K
authors.initials: EK
authors.lastname: Mayer
authors.affiliation: Ara Darzi is executive chair of the World Innovation Summit for Health, Qatar Foundation, and director of the Institute of Global Health Innovation, Imperial College London.
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
mesh_terms: D003627:Data Interpretation, Statistical; D019033:Delivery of Health Care, Integrated / Q000458:organization & administration*; D006801:Humans; D018802:Patient-Centered Care / Q000379:methods*; D000075485:Population Health
keywords: Chronic Care; Elderly; Information Technology; Long-Term Care; Organization and Delivery of Care