Estimating community health needs against a Triple Aim background: What can we learn from current predictive risk models?

A.M.J. Elissen*, J.N. Struijs, C.A. Baan, D. Ruwaard

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


INTRODUCTION: To support providers and commissioners in accurately assessing their local populations' health needs, this study produces an overview of Dutch predictive risk models for health care, focusing specifically on the type, combination and relevance of included determinants for achieving the Triple Aim (improved health, better care experience, and lower costs). METHODS: We conducted a mixed-methods study combining document analyses, interviews and a Delphi study. Predictive risk models were identified based on a web search and expert input. Participating in the study were Dutch experts in predictive risk modelling (interviews; n=11) and experts in healthcare delivery, insurance and/or funding methodology (Delphi panel; n=15). RESULTS: Ten predictive risk models were analysed, comprising 17 unique determinants. Twelve were considered relevant by experts for estimating community health needs. Although some compositional similarities were identified between models, the combination and operationalisation of determinants varied considerably. CONCLUSIONS: Existing predictive risk models provide a good starting point, but optimally balancing resources and targeting interventions on the community level will likely require a more holistic approach to health needs assessment. Development of additional determinants, such as measures of people's lifestyle and social network, may require policies pushing the integration of routine data from different (healthcare) sources.
Original languageEnglish
Pages (from-to)672-679
Number of pages8
JournalHealth Policy
Issue number5
Early online date9 Jan 2015
Publication statusPublished - May 2015


  • Community-based care
  • Integrated care
  • Health needs assessment
  • Predictive risk models
  • Triple Aim


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