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Development of quality indicators for a community pharmacy setting
Ann Helen Jakobsen1, Noriko Sato2, Timothy F Chen2
1Clinical Pharmacy and Pharmacoepidemiology (IPSUM research group), Department of Pharmacy, The Faculty of Health Sciences, UiT The Arctic University of Norway, 9037 Tromsoe, Norway.
Objectives:
Community pharmacies in Norway offer accessible healthcare services that require monitoring. Relevant and realistic quality indicators (QIs) must be developed to establish standards. This study aimed to establish consensus on healthcare quality measures in a community pharmacy setting by implementing two distinct approaches.
Methods:
A comprehensive multi-phase research design was implemented to identify, define, and select potential QIs for community pharmacies. Potential QIs were identified and nominated from workshops, focus groups, and literature. Thirteen panellists were recruited for a modified Delphi study over two rounds. We used the RAND/UCLA Appropriateness Method (RAM) panel median ratings and disagreement index (DI) to assess appropriateness and disagreement and define consensus.
Key Findings:
We identified 192 QIs from workshops, focus groups and literature searches. After duplicates were removed and QIs with similar wording were merged, 137 QIs were nominated for the first Delphi round. The panellists deemed 61 appropriate, two inappropriate and excluded six QIs in the first round. The remaining 68 QIs were assessed in Round 2, where 23 achieved consensus as appropriate without disagreement. After DI was calculated, the number of QIs categorized as appropriate without disagreement after Round 1 and 2 was 34 and 10, respectively.
Conclusion:
This study demonstrates the use of the RAM combined with the DI to establish consensus on healthcare quality measures, i.e. QIs for community pharmacy services. Our findings indicate that the number of QIs considered acceptable is strongly impacted by the method chosen to handle disagreement in the ratings. Incorporating DI and conventional RAM disagreement calculations reduced the number of QIs deemed acceptable by half.
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