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.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.