Piloting a specialty pharmacy turnaround time quality measure: Findings and lessons learned.

backgroundInterested parties have recognized the need for quality measurement focused on pharmacy services. As the use of specialty medications continues to grow, quality measurement is increasingly critical. One key area of quality is prescription turnaround time (TAT) given that prompt treatment initiation is important for optimal management of a variety of complex conditions. However, pharmacy data availability remains highly variable and may impact consistent measurement of TAT.

objectiveTo investigate potential inconsistencies in TAT calculation with the Pharmacy Quality Alliance-endorsed

methodsFive specialty pharmacy organizations used the SP-TAT-PH measure specifications to calculate baseline, midpoint, and endpoint TAT rates at the pharmacy National Provider Identifier level for each organization's participating pharmacies. At each time point, organizations provided their TAT rates and raw data to the authors for validation, who then developed and shared a report of calculation discrepancies. Each organization then recalculated their measure rates and resubmitted to the authors for further comparison and resolution. Surveys and online debrief meetings were used to capture feedback and lessons learned on calculating and improving TAT. Sensitivity analyses were conducted to explore mean TAT by specialty medication category.

resultsMean TAT (in days) across all participating National Provider Identifiers was 2.42 (range: 1.17, 11.09), 2.42 (range: 1.12, 10.03), and 2.52 (range: 1.11, 10.20) for baseline, midpoint, and endpoint measurement, respectively. Baseline validation activities resulted in a negligible impact on TAT rates for 3 organizations, moderate impact for 1 organization, and substantial impact (close to 1 full day of TAT) for 1 organization. As anticipated, resolving discrepancies between organization- and author-calculated rates during baseline validation resulted in fewer discrepancies at midpoint and endpoint and therefore had negligible impact on these subsequent TAT rates for all 5 organizations. In the debrief meetings, participants highlighted areas in the SP-TAT-PH measure specifications with opportunity for clarification to support accurate and consistent implementation. Full staffing levels, technology improvements, and process improvements were cited as main facilitators to optimizing TAT, whereas prior authorizations and processes that required coordination between patients and providers were identified as barriers. Participants also noted that variations in data availability posed challenges to consistent measure calculation and universally agreed it would be beneficial to standardize the way specialty pharmacies measure TAT across the industry. Stratifying TAT by medication category did not suggest a strong, consistent association between specific medication categories and higher or lower TAT.

conclusionsThis project generated important learnings, including those to support accurate TAT calculation, facilitators and barriers to improving TAT, and valuable information on the variability in specialty pharmacy data. Throughout the pilot, each organization demonstrated the effects of their own specific systems, configurations, and data element terminologies on measure calculations. Quality measurement of specialty pharmacy TAT for accountability purposes will continue to be challenging until standardized data elements can consistently be collected and reported.