PNS290 EVIDENCE-BASED VALUE FRAMEWORK FOR THE EVALUATION OF CLINICAL PHARMACY SERVICES

Abstract

To identify and evaluate potential outcome measures for the assessment of clinical pharmacy services (CPSs) in order to facilitate the implementation and monitoring of CPSs within the Hungarian healthcare system. A literature review on PubMed was conducted to identify original controlled studies assessing the effects of CPSs. Studies eligible for full-text data extraction were selected by title and abstract screening. Outcome measures were aggregated in four categories: clinical, patient-related, cost-related or health care professional-related outcomes. They were further analysed according to 1) sensitivity to capture benefits of CPSs, 2) generalisability and transferability, 3) measurability including the availability of standard measurement methods. In total, 46 publications with 73 CPSs interventions were included in the analysis. A large number (83) of outcome measures had been used to evaluate the effect of the interventions. The most prevalent measures included medication appropriateness (n=17) and readmission rates (n=12). Significant positive effects were most frequently related to outcomes sensitive to pharmacotherapy management. Very few studies could prove the benefit of CPSs in hard clinical endpoints partly due to short study duration and limited statistical power. List of recommended outcome measures for evaluation of CPSs were created based on feasibility of measuring potential outcomes. Evidence generated from low quality studies may lead to false expectations concerning the added value of CPSs in healthcare. More methodological research is needed to address the limitations of existing research methods to get more realistic evidence on the contribution of CPSs to health care. The proposed value framework may support implementation and improvement of CPSs. The short-listed outcome measures are recommended for inclusion to local studies for the evaluation of CPSs. Standardization of CPS interventions, appropriate study design, long study duration and sufficient sample size are also suggested in order to improve the validity and generalizability of conclusions.