TY - JOUR
T1 - Proposals for enhanced health risk assessment and stratification in an integrated care scenario
AU - Espieén, Ivan Dueñas
AU - Vela, Emili
AU - Pauws, Steffen
AU - Bescos, Cristina
AU - Cano, Isaac
AU - Cleries, Montserrat
AU - Contel, Joan Carles
AU - De Keenoy, Esteban Manuel
AU - Garcia-Aymerich, Judith
AU - Gomez-Cabrero, David
AU - Kaye, Rachelle
AU - Lahr, Maarten M.H.
AU - Lluch-Ariet, Magieé
AU - Moharra, Montserrat
AU - Monterde, David
AU - Mora, Joana
AU - Nalin, Marco
AU - Pavlickova, Andrea
AU - Piera, Jordi
AU - Ponce, Sara
AU - Santaeugenia, Sebastià
AU - Schonenberg, Helen
AU - Störk, Stefan
AU - Tegner, Jesper
AU - Velickovski, Filip
AU - Westerteicher, Christoph
AU - Roca, Josep
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-16
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2015-010301
UR - http://www.scopus.com/inward/record.url?scp=84971323675&partnerID=8YFLogxK
U2 - 10.1136/bmjopen-2015-010301
DO - 10.1136/bmjopen-2015-010301
M3 - Article
C2 - 27084274
SN - 2044-6055
VL - 6
JO - BMJ Open
JF - BMJ Open
IS - 4
ER -
