Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/105207
Title: Proposals for enhanced health risk assessment and stratification in an integrated care scenario
Author: Dueñas Espín, Ivan
Vela, Emili
Pauws, Steffen
Bescos, Cristina
Cano Franco, Isaac
Cleries, Montserrat
Contel, Joan Carles
Manuel Keenoy, Esteban de
García Aymerich, Judith
Gomez Cabrero, David
Kaye, Rachelle
Lahr, Maarten M H
Lluch Ariet, Magí
Moharra, Montserrat
Monterde Prat, David
Mora, Joana
Nalin, Marco
Pavlickova, Andrea
Piera, Jordi
Ponce, Sara
Santaeugenia, Sebastià
Schonenberg, Helen
Störk, Stefan
Tegnér, Jesper
Velickovski, Filip
Westerteicher, Christoph
Roca Torrent, Josep
Keywords: Avaluació del risc per la salut
Presa de decisions
Salut pública
Health risk assessment
Decision making
Public health
Issue Date: 15-Apr-2016
Publisher: BMJ Publishing Group
Abstract: Objectives: 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 (http://www.act-programme.eu). 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
Note: Reproducció del document publicat a: https://doi.org/10.1136/bmjopen-2015-010301
It is part of: BMJ Open, 2016, vol. 6 , num. 4, p. e010301
Related resource: https://doi.org/10.1136/bmjopen-2015-010301
URI: http://hdl.handle.net/2445/105207
ISSN: 2044-6055
Appears in Collections:Articles publicats en revistes (Medicina)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)

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