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

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.

Keywords: Health risk assessment; Patient stratification; case finding; chronic care; clinical decision making.

Figure 1

Explained variability indicated by R2 (expressed as a percentage) in the y-axis, for four main outcomes: mortality, hospital admissions, emergency department visits and total healthcare expenses obtained from the analysis of the Catalan population (7.5 million inhabitants) in 2014, using three different health risk assessment models built-up with different covariates: A+S+SE includes only age, sex and socioeconomic status as covariates; A+S+SE+CRG additionally includes Clinical Risk Groups as morbidity grouper and A+S+SE+GMA includes information from Adjusted Morbidity Groups as morbidity grouper (see online supplementary material for further details, part I).

Figure 2

The dimensions of patient health indicated in the figure may contribute to enrich clinical risk predictive modelling. As a first step, we propose to include the outcome of the population-based risk assessment as a covariate in clinical risk predictive modelling. For future personalised care for chronic patients, enhanced dynamic communication among Informal Care, Health Care and Biomedical Research will allow inclusion of several dimensions into clinical risk predictive modelling. It will be carried out through multilevel/multiscale heterogeneous data integration within a Digital Health Framework, as depicted in figure 3.

Figure 3

Scheme of the Digital Health Framework, composed of digital data normalisation and knowledge management layers for knowledge generation, and novel Clinical Decision Support Systems (CDSS) embedded into integrated care processes.