Proposals for enhanced health risk assessment and stratification in an integrated care scenario

Dueñas Espín, Ivan
Vela, Emili
Pauws, Steffen
Bescos, Cristina
Cano, Isaac
Cleries, Montserrat
Contel, Joan Carles
Keenoy, Esteban de Manuel
Garcia Aymerich, Judith
Gómez Cabrero, David
Kaye, Rachelle
Lahr, Maarten M.H.
Lluch Ariet, Magí
Moharra, Montserrat
Monterde, David
Mora, Joana
Nalin, Marco
Pavlickova, Andrea
Piera, Jordi
Ponce, Sara
Santaeugenia, Sebastià
Schonenberg, Helen
Störk, Stefan
Tegner, Jesper
Velickovski, Filip
Westerteicher, Christoph
Roca, Josep
Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme ( The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation ​
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