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Development of a specialty pharmacy productivity benchmarking model

Background: Benchmarking in healthcare is used to evaluate productivity on the basis of workflows, policies and performance in hopes of optimizing current practices and improving patient outcomes. Benchmarking has long been used in pharmacy practice, whether in tracking dispensing activities or opti...

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Detalles Bibliográficos
Autores principales: Platt, Thom, Shah, Rushabh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764474/
http://dx.doi.org/10.1080/21556660.2019.1658314
Descripción
Sumario:Background: Benchmarking in healthcare is used to evaluate productivity on the basis of workflows, policies and performance in hopes of optimizing current practices and improving patient outcomes. Benchmarking has long been used in pharmacy practice, whether in tracking dispensing activities or optimizing clinical workflows. Internal benchmarking allows organizations to examine internal processes to determine allocation of institutional resources. Currently there is no validated model to evaluate productivity in specialty pharmacy workflows. Aims: To develop and validate a specialty pharmacy productivity benchmarking tool. Methods: A timer tool was developed to allow pharmacists to track the time spent performing activities which we identified as key performance indicators. Key performance indicators were identified as: prior authorizations, appeals of coverage denial, financial assistance activities, clinical onboarding activities, care plan activities and clinical assessments. These times were utilized to establish benchmarks for each key performance indicator. Raw activity number was tracked for each branch utilizing data from specialty management software database, Therigy. From these data, benchmark standards were derived, and all branches were evaluated. Results: Benchmarking standardized to the inflammatory diseases branch showed a near 2.5 fold elevation in workload in the hematology and oncology branch. The pulmonary branch showed a decreased workload compared to inflammatory diseases by approximately 35%. Neurology and infectious diseases within the 20% relative workload range of inflammatory diseases and are considered to have an equal productivity level. Conclusions: Results from this study provide a solid foundation for this benchmarking tool. Moving forward with this model the addition of technician metrics and a broader collection of performance indicators across a larger data collection period will be required to more fully develop the model.