ITERATIVE MODELING AND EVALUATION OF THE CLINICAL AND ECONOMIC OUTCOMES OF PAC

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The aims of this project are to measure, estimate, and model the effect of a series of specific interventions involving the implementation of a Picture Archiving and Communication System (PACS) on the health outcomes of patients and on the finances of a radiology practice, clinical unit, and medical center. The three principal components of our approach are (1) develop a financial model from estimated and actual expenses before and after each intervention, (2) derive multivariate models that estimate the independent effects of PACS interventions on health outcome surrogates, and (3) construct a cost effectiveness model that integrates incremental costs and health outcomes associated with each intervention in a single metric. First, we will estimate the cost of equipment, supplies, and personnel from actual expense information (prior to PACS interventions), and from actual and projected needs of the radiology department (after PACS interventions). These data will be used to develop an incremental direct-cost model of PACS from the perspective of a health system. Next, we will measure readily available surrogate variables that are likely to correlate highly with the health outcomes of patients: the rate of inpatient admission, the length of inpatient stay (for admitted patients), the need for and cost of subsequent health care. Multivariate analysis will be used to detect whether the outcome surrogates are influenced by the interventions, and by other factors known to influence these variables (e.g., case mix). Our hypothesis is that, after controlling for the underlying clinical and demographic differences among patients, patients imaged after a PACS intervention compared to those imaged before the intervention, will have shorter lengths of stay, shorter ED visits, shorter exam performance times, and decreased costs of care. Finally, cost-effectiveness metrics will be computed as cost per diagnostic value, cost per increment of process improvement, and as cost per health outcome surrogate. We will perform sensitivity analyses of the combined model to test the robustness of the model, to assess the applicability of the model to other health care facilities, to reveal the variables on which the conclusions depend, and to suggest implementation strategies that may improve operational efficiency and cost-effectiveness.