Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies
Lilford, Richard J
Girling, Alan J
Coleman, Jamie J
Chilton, Peter J
Burn, Samantha L
Jenkinson, David J
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CitationLilford, Richard J, Alan J Girling, Aziz Sheikh, Jamie J Coleman, Peter J Chilton, Samantha L Burn, David J Jenkinson, Laurence Blake, and Karla Hemming. 2014. “Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies.” BMC Health Services Research 14 (1): 314. doi:10.1186/1472-6963-14-314. http://dx.doi.org/10.1186/1472-6963-14-314.
AbstractBackground: This protocol concerns the assessment of cost-effectiveness of hospital health information technology (HIT) in four hospitals. Two of these hospitals are acquiring ePrescribing systems incorporating extensive decision support, while the other two will implement systems incorporating more basic clinical algorithms. Implementation of an ePrescribing system will have diffuse effects over myriad clinical processes, so the protocol has to deal with a large amount of information collected at various ‘levels’ across the system. Methods/Design The method we propose is use of Bayesian ideas as a philosophical guide. Assessment of cost-effectiveness requires a number of parameters in order to measure incremental cost utility or benefit – the effectiveness of the intervention in reducing frequency of preventable adverse events; utilities for these adverse events; costs of HIT systems; and cost consequences of adverse events averted. There is no single end-point that adequately and unproblematically captures the effectiveness of the intervention; we therefore plan to observe changes in error rates and adverse events in four error categories (death, permanent disability, moderate disability, minimal effect). For each category we will elicit and pool subjective probability densities from experts for reductions in adverse events, resulting from deployment of the intervention in a hospital with extensive decision support. The experts will have been briefed with quantitative and qualitative data from the study and external data sources prior to elicitation. Following this, there will be a process of deliberative dialogues so that experts can “re-calibrate” their subjective probability estimates. The consolidated densities assembled from the repeat elicitation exercise will then be used to populate a health economic model, along with salient utilities. The credible limits from these densities can define thresholds for sensitivity analyses. Discussion The protocol we present here was designed for evaluation of ePrescribing systems. However, the methodology we propose could be used whenever research cannot provide a direct and unbiased measure of comparative effectiveness.
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