Impact of scribes on throughput metrics and billing during an electronic medical record transition.

OBJECTIVE: Evaluate an established scribe program on throughput and revenue capture in an Emergency Department (ED) undergoing an EMR transition. METHODS: A prospective cohort design comparing patients managed with and without scribes in an academic ED. Throughput metrics (medians, min) and relative value units (RVUs, means) were collected. Data was evaluated in its entirety (three months), as well as in two subsets: go live (immediate two weeks) and adoption (two weeks post implementation to end). RESULTS: All patients: There was no significant difference in throughput or RVUs during the three month period. During go-live, scribes showed improvement in total RVUs per patient (4.63 vs 4.40, p = 0.048). During adoption, scribed patients had decreased length of stay (LOS, 221 vs 231, p = 0.023). Adults: Door to provider (28 vs 37, p = 0.014) and total RVUs (5.20 vs 4.92, p = 0.042) were improved with scribes in the go-live period. Scribes improved go-live morning and overnight shifts, while lengthening provider to disposition during afternoon shifts. No significant differences were seen in the adoption period, except for increased provider to disposition time overnight with scribes (154 vs 146, p = 0.030). Pediatrics: When all pediatric patients were compared, scribe patients had a decreased professional RVU charge (2.78 vs 2.90, p = 0.037). During go live and adoption, no significant differences were found in any other parameter or subgrouping. CONCLUSIONS: A scribe's ability to mitigate operational inefficiencies introduced by an EMR transition seems limited in an academic hospital. Previous research highlighting the impact of scribes on revenue was not replicated during this study.

Implementation of a pharmacist-led pharmacogenomics service for the Program of All-Inclusive Care for the Elderly (PHARM-GENOME-PACE).

OBJECTIVES: To determine the feasibility of implementing a pharmacist-led pharmacogenomics (PGx) service for the Program of All-Inclusive Care for the Elderly (PACE). SETTING: A national centralized pharmacy providing PGx services to community-based PACE centers. PRACTICE DESCRIPTION: Individuals 55 years of age and older enrolled in PACE who underwent PGx testing as part of their medical care (n = 296). PRACTICE INNOVATION: Pharmacist-led PGx testing, interpreting, and consulting. EVALUATION: Implementation processes and roles were ascertained by reviewing policies and procedures for the PGx service and documented observations made by pharmacists providing the service. Genetic variants and drug-gene interactions (DGIs) were determined by interpretations of PGx test results. Types of recommendations provided by pharmacists were ascertained from PGx consultations. Prescribers' acceptance of recommendations were ascertained by documented responses or drug changes made after PGx consultations. RESULTS: Challenges to implementation included lack of systems interoperability, limited access to medical electronic health records, determining prescribers' responses, and knowledge and competency gaps in PGx. Pharmacist roles most essential to overcoming challenges were interpreting and applying PGx data, determining how to disseminate those data to prescribers, advocating for appropriate PGx testing, and educating about the application of test results to clinical practice. Participants frequently used drugs posing DGI risks, with the majority (73.6%) reporting more than 1 interaction. The overwhelming majority (89.0%) of pharmacists' recommendations to mitigate risks were accepted by referring prescribers. CONCLUSION: Implementing a pharmacist-led PGx service for PACE is feasible. Implementation of this service highlights the leadership role of pharmacists in moving PGx from research to practice.

The role of hospital pharmacists in the adoption and use of pharmacogenomics and precision medicine.

AIM: Our aim was to assess the knowledge and attitudes of US hospital pharmacists about the implementation of clinical pharmacogenomics, and examine liability risks of adopting pharmacogenomics by pharmacists. METHODS: We surveyed hospital pharmacists. Linear regression models of predictor variables for pharmacist adoption and use of pharmacogenomics were analyzed. RESULTS: The survey was administered to 660 hospital pharmacists (23% response rate; n = 149). The majority of respondents (72%) favor implementing pharmacogenomics into pharmacy practice. However, only 25% are confident in their abilities to interpret pharmacogenomic test results. CONCLUSION: Pharmacists lack confidence in their abilities to interpret and use pharmacogenomic information in clinical care. These results raise potential liability risks that are pertinent to pharmacists.

Reducing Off-Label Antipsychotic Use in Older Community-Dwelling Adults With Dementia: A Narrative Review.

The off-label use of antipsychotics for the management of behavioral and psychologic symptoms of dementia (BPSD) in older adults (age ≥65 years) is common, despite evidence of modest benefits and serious risks. Although national initiatives aimed at reducing antipsychotic use among older adults with BPSD in nursing homes have been successful, similar initiatives are lacking for community-dwelling adults with dementia. As a result, older adults with BPSD residing in the community may be at an even greater risk of being negatively affected by antipsychotic use. Physicians should be knowledgeable of this issue and understand the alternatives to antipsychotics, as well as how to reduce antipsychotic use in patients with dementia who are already taking antipsychotics.

Implementation of a Standardized Medication Therapy Management Plus Approach within Primary Care.

PURPOSE: The purpose of this study was to implement a clinical pharmacist-led medication therapy management (MTM) service within a primary-care setting that is enhanced by 1) a clinical decision support system (CDSS) that includes a unique combination of medication risk mitigation factors, which aids the pharmacist in interpreting the medication profile, and 2) pharmacogenomics (PGx) testing. METHODS: This was a service implementation study, whereby Medicare beneficiaries were eligible if they were patients of Elmwood Family Physicians, a private family, primary care practice with 2 locations in New Jersey, and were on at least 7 medications. Patients had a medication reconciliation completed by a pharmacist and performed a PGx buccal swab. Patient information was run through a CDSS to aid the pharmacist with screening for multidrug interactions and assessing patient's medication-related risks. The output of the CDSS was used to create recommendations and provide a consult to the physicians. Recommendations were followed up by return of the consult. RESULTS: Enrolled patients used a mean (± standard deviation) of 12.1 (± 4.6) medications. The turnaround time for the MTM Plus consults was 11.7 (± 6.2) days. During the consults, the pharmacist identified 138 medication-related problems (MRPs). The most common MRPs were drug-drug interactions (29.0%) and drug-gene interactions (DGIs; 24.6%). CONCLUSION: Implementing a clinical pharmacist-led MTM Plus service in the primary care setting is feasible. This study highlights that DGIs are common in older adults in family practice and indicates that PGx testing identifies additional MRPs that may otherwise go unnoticed in these patients. The experiences we shared can aid other clinicians in establishing successful MTM Plus services. Future studies should also measure the impact of such personalized medicine services on economic, clinical, and humanistic outcomes. This study has been registered with ClinicalTrials.gov (study No. NCT02748148).