Development of a web-based tool for the assessment of health and economic outcomes of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). Issue 3 (December 2015)

• Record Type: Journal Article
• Authors: Boehler, Christian; de Graaf, Gimon; Steuten, Lotte; Yang, Yaling; Abadie, Fabienne
• Is Part Of: BMC medical informatics and decision making. Volume 15:Issue 3(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: decision analytic modelling -- health and economic outcomes -- active and healthy ageing -- European Innovation Partnership -- web-based tool; Medical informatics -- Periodicals; Clinical medicine -- Decision making -- Periodicals; 610.285
• DOI: 10.1186/1472-6947-15-S3-S4
• Languages: English
• ISSNs: 1472-6947

Abstract

Abstract Background The European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) is a European Commission led policy initiative to address the challenges of demographic change in Europe. For monitoring the health and economic impact of the social and technological innovations carried out by more than 500 stakeholder's groups ('commitments') participating in the EIP on AHA, a generic and flexible web-based monitoring and assessment tool is currently being developed. Aim This paper describes the approach for developing and implementing this web-based tool, its main characteristics and capability to provide specific outcomes that are of value to the developers of an intervention, as well as a series of case studies planned before wider rollout. Methods The tool builds up from a variety of surrogate endpoints commonly used across the diverse set of EIP on AHA commitments in order to estimate health and economic outcomes in terms of incremental changes in quality adjusted life years (QALYs) as well as health and social care utilisation. A highly adaptable Markov model with initially three mutually exclusive health states ('baseline health', 'deteriorated health' and 'death') provides the basis for the tool which draws from an extensive database of epidemiological, economic and effectiveness data; and also allows further customisation through remote data entry enabling more accurate and context specific estimation of intervention impact. Both probabilistic sensitivity analysis and deterministic scenario analysis allow assessing the impact of parameter uncertainty on intervention outcomes. A set of case studies, ranging from the pre-market assessment of early healthcare technologies to the retrospective analysis of established care pathways, will be carried out before public rollout, which is envisaged end 2015. Conclusion Monitoring the activities carried out within the EIP on AHA requires an approach that is both flexible and consistent in the way health and economic impact is estimated across interventions and commitments. The added value for users of the MAFEIP-tool is its ability to provide an early assessment of the likelihood that interventions in their current design will achieve the anticipated impact, and also to identify what drives interventions' effectiveness or efficiency to guide further design, development or evaluation.