Impact of pharmacist participation in the patient care team on value-based health measures.

PURPOSE: To evaluate whether pharmacist engagement on the interdisciplinary team leads to improved performance on diabetes-related quality measures. METHODS: This was a retrospective observational study of patients seen in primary care and specialty clinics from October 2014 to October 2020. Patients were included if they had a visit with a physician, nurse practitioner, physician's assistant, or clinical pharmacist practitioner (CPP) within the study period and had a diagnosis of diabetes. The intervention group included patients with at least one visit with a CPP, while the control group consisted of patients who were exclusively managed by non-CPP providers. The primary outcome of this study was the median change in glycosylated hemoglobin (HbA1c) from baseline to follow-up at 3, 6, and 12 months. The secondary outcome was the probability of achieving the HbA1c targets of <7% and <8% at 3, 6, and 12 months. RESULTS: Patients referred to a CPP had higher HbA1c levels at baseline and were more likely to have concomitant hypertension (P < 0.01). Patients seen by a CPP had 0.31%, 0.41%, and 0.44% greater reductions in HbA1c compared to patients in the control group at 3, 6, and 12 months, respectively (P < 0.01). Patients managed by a CPP were also more likely to achieve the identified HbA1c targets of <7% and <8%. CONCLUSION: Patients referred to a CPP were more complex, but had greater reductions in HbA1c and were more likely to achieve HbA1c goals included in the organization's quality measures. This study demonstrates the value of pharmacists in improving patient care and their role in supporting an organization's achievement of value-based quality measures.

Identifying health-system pharmacy operational process categories and corresponding tasks across a diverse health system using a modified Delphi process.

PURPOSE: The purpose of this study was to identify and build consensus on operational tasks that occur within a health-system pharmacy. METHODS: An expert panel of 8 individuals was invited to participate in a 3-round modified Delphi process. In the first round, the expert panel independently reviewed an initial list and provided feedback. All feedback was incorporated into the second round and then reviewed and discussed as a group. The expert panel reviewed an updated list based on feedback from the second round and reached consensus on a final list of operational processes and corresponding tasks. RESULTS: All 8 participants agreed to serve on the Delphi expert panel and reviewed an initial list of 9 process categories (hazardous intravenous [IV] medications, nonhazardous IV medications, hazardous oral medications, nonhazardous oral medications, controlled substances, total parenteral nutrition [TPN]/fluid preparations, distribution and delivery, clinical tasks, and miscellaneous operational tasks) and 44 corresponding tasks. Through the Delphi process, 72 new tasks were identified in the first round, while 34 new tasks were identified in the second round. In the third and final round, the expert panel reviewed the updated list of 9 process categories and 150 corresponding tasks, made additional edits, and reached consensus on a final list of 9 processes and 138 corresponding tasks that represented operational work within a health-system pharmacy. CONCLUSION: The modified Delphi process effectively identified operational processes and corresponding tasks occurring within hospital pharmacies in a diverse health system. This process facilitated consensus building, and the findings may inform development of an operational workload model.

Formation and validation of an acute care clinical pharmacist productivity model: Part 2.

PURPOSE: The purpose of the project described here was to use the work outputs identified in part 1 of a 2-part research initiative to build and validate an acute care clinical pharmacist productivity model. METHODS: Following the identification of work outputs in part 1 of the project, relative weighting was assigned to all outputs based on the time intensity and complexity of each task. The number of pharmacists verifying an inpatient medication order each day was selected to represent the labor input. A multivariable linear regression was performed to determine the final work outputs for inclusion in the model. Productivity and productivity index values were calculated for each day from July 1, 2018, through June 30, 2019. RESULTS: Of the 27 work outputs identified via consensus by the clinical pharmacist working team, 17 work outputs were ultimately included in the productivity model. The average productivity during the period July 2018 through June 2019 was derived from the model and will serve as the baseline productivity for acute care clinical pharmacists. CONCLUSION: Validated consensus methodology can be useful for engaging clinical pharmacist in decision-making and developing a clinical productivity model. When thoughtfully designed, the model can replace obsolete measures of productivity that do not account for the responsibilities of clinical pharmacists.

Development and validation of a predictive model to predict and manage drug shortages.

PURPOSE: Pharmacy departments across the country are problem-solving the growing issue of drug shortages. We aim to change the drug shortage management strategy from a reactive process to a more proactive approach using predictive data analytics. By doing so, we can drive our decision-making to more efficiently manage drug shortages. METHODS: Internal purchasing, formulary, and drug shortage data were reviewed to identify drugs subject to a high shortage risk ("shortage drugs") or not subject to a high shortage risk ("nonshortage drugs"). Potential candidate predictors of drug shortage risk were collected from previous literature. The dataset was trained and tested using 2 methods, including k-fold cross-validation and a 70/30 partition into a training dataset and a testing dataset, respectively. RESULTS: A total of 1,517 shortage and nonshortage drugs were included. The following candidate predictors were used to build the dataset: dosage form, therapeutic class, controlled substance schedule (Schedule II or Schedules III-V), orphan drug status, generic versus branded status, and number of manufacturers. Predictors that positively predicted shortages included classification of drugs as intravenous-only, both oral and intravenous, antimicrobials, analgesics, electrolytes, anesthetics, and cardiovascular agents. Predictors that negatively predicted a shortage included classification as an oral-only agent, branded-only agent, antipsychotic, Schedule II agent, or orphan drug, as well as the total number of manufacturers. The calculated sensitivity was 0.71; the specificity, 0.93; the accuracy, 0.87; and the C statistic, 0.93. CONCLUSION: The study demonstrated the use of predictive analytics to create a drug shortage model using drug characteristics and manufacturing variables.