Abstract
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 language | English |
|---|---|
| Pages (from-to) | 672-679 |
| Journal | Health Policy |
| Volume | 119 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2015 |
Fingerprint
Cite this
}
Estimating community health needs against a Triple Aim background : What can we learn from current predictive risk models? / Elissen, Arianne M.j.; Struijs, Jeroen N.; Baan, C.A.; Ruwaard, Dirk.
In: Health Policy, Vol. 119, No. 5, 2015, p. 672-679.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Estimating community health needs against a Triple Aim background
T2 - What can we learn from current predictive risk models?
AU - Elissen, Arianne M.j.
AU - Struijs, Jeroen N.
AU - Baan, C.A.
AU - Ruwaard, Dirk
PY - 2015
Y1 - 2015
N2 - IntroductionTo 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).MethodsWe 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).ResultsTen 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.ConclusionsExisting 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.
AB - IntroductionTo 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).MethodsWe 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).ResultsTen 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.ConclusionsExisting 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.
U2 - 10.1016/j.healthpol.2014.12.007
DO - 10.1016/j.healthpol.2014.12.007
M3 - Article
VL - 119
SP - 672
EP - 679
JO - Health Policy
JF - Health Policy
SN - 0168-8510
IS - 5
ER -