Karnon, J., McIntosh, A., Dean, J., Bath, P.A., Hutchinson, A., Oakley, A., Thomas, N., Pratt, P., Freeman-Parry, L., Karsh, B.T., Gandhi, T. and Tappenden, P. (2008) Modelling the expected net benefits of interventions to reduce the burden of medication errors. Journal of Health Services Research and Policy, 13 (2). pp. 85-91. ISSN 1355-8196Full text not available from this repository.
Objectives: The aim of this study is to estimate the potential costs and benefits of three key interventions (computerized physician order entry [CPOE], additional ward pharmacists and bar coding) to help prioritize research to reduce medication errors.
Methods: A generic model structure was developed to describe the incidence and impacts of medication errors in hospitals. The model follows pathways from medication error points at alternative stages of the medication pathway through to the outcomes of undetected errors. The model was populated from a systematic review of the medication errors literature combined with novel probabilistic calibration methods. Cost ranges were applied to the interventions, the treatment of preventable adverse drug events (pADEs), and the value of the health lost as a result of an ADE.
Results: The model predicts annual health service costs of between £0.3 million and £1 million for the treatment of pADEs in a 400-bed acute hospital in the UK. Including only health service costs, it is uncertain whether any of the three interventions will produce positive net benefits, particularly if high intervention costs are assumed. When the monetary value of lost health is included, all three interventions have a high probability of producing positive net benefits with a mean estimate of around £31.5 million for CPOE over a five-year time horizon.
Conclusions: The results identify the potential cost-effectiveness of interventions aimed at medication errors, as well as identifying key drivers of cost-effectiveness that should be specifically addressed in the design of primary evaluations of medication error interventions.
|Institution:||The University of Sheffield|
|Academic Units:||The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)|
|Depositing User:||Information Studies|
|Date Deposited:||14 Aug 2009 13:21|
|Last Modified:||14 Aug 2009 13:21|
|Publisher:||Royal Society of Medicine|