%0 Journal Article %@ 1472-6963 %A Aicken, CR %A Armstrong, NT %A Cassell, JA %A Macdonald, N %A Bailey, AC %A Johnson, SA %A Mercer, CH %D 2012 %F discovery:1311057 %J BMC Health Services Research %K Decision Support Techniques, Evidence-Based Medicine, Great Britain, Health Planning, Health Services Administration, Humans, Sexually Transmitted Diseases %P - %T Barriers and opportunities for evidence-based health service planning: the example of developing a Decision Analytic Model to plan services for sexually transmitted infections in the UK %U https://discovery.ucl.ac.uk/id/eprint/1311057/ %V 12 %X Decision Analytic Models (DAMs) are established means of evidence-synthesis to differentiate between health interventions. They have mainly been used to inform clinical decisions and health technology assessment at the national level, yet could also inform local health service planning. For this, a DAM must take into account the needs of the local population, but also the needs of those planning its services. Drawing on our experiences from stakeholder consultations, where we presented the potential utility of a DAM for planning local health services for sexually transmitted infections (STIs) in the UK, and the evidence it could use to inform decisions regarding different combinations of service provision, in terms of their costs, cost-effectiveness, and public health outcomes, we discuss the barriers perceived by stakeholders to the use of DAMs to inform service planning for local populations, including (1) a tension between individual and population perspectives; (2) reductionism; and (3) a lack of transparency regarding models, their assumptions, and the motivations of those generating models. %Z © 2012 Aicken et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. PMCID: PMC3519719