Predictors of linkage to care for a nontargeted emergency department hepatitis C screening program.

OBJECTIVE: We implemented a nontargeted, opt-out HCV testing and linkage to care (LTC) program in an academic tertiary care emergency department (ED). Despite research showing the critical role of ED-based HCV testing programs, predictors of LTC have not been defined for patients identified through the nontargeted ED testing strategy. In order to optimize health outcomes for patients with HCV, we sought to identify predictors of LTC failure. METHODS: This was a retrospective cohort study of adult patients who were tested for HCV in the ED between August 2015 and September 2018 and were confirmed to have chronic HCV infection through RNA testing. We used logistic regression to assess the relationship between candidate predictors and the primary outcome, LTC failure, which was defined as a patient not being seen by an HCV treating provider after discharge from the ED. RESULTS: Of 53,297 patients tested, 1,674 (3.1%) had HCV on confirmatory testing, and 355 (21%) linked to care. Predictors of LTC failure included younger age (OR 0.96, 95% CI 0.95-0.97), white race (OR 1.65, 95% CI 1.23-2.22), homelessness (OR 1.91, 95% CI 1.19-3.08), substance use (OR 1.77, 95% CI 1.34-2.34), and comorbid psychiatric illness (OR 2.16, 95% CI 1.59-2.94). Patients with significant medical comorbidities (OR 0.57, 95% CI 0.41-0.78) or HIV co-infection (OR 0.11, 95% CI 0.03-0.46) were less likely to experience LTC failure. CONCLUSIONS: One in five HCV-infected patients identified by ED-based nontargeted testing successfully linked to an HCV treating provider. Predictors of LTC failure may guide the development of targeted interventions to improve LTC success.

Impact of pharmacist-driven referral to increase specialty pharmacy prescription capture.

PURPOSE: To assess the impact of a clinical pharmacy specialist (CPS) embedded within a rheumatology clinic at a large academic medical center on the prescription capture rate at the health-system specialty pharmacy. METHODS: Initially low prescription capture rates for the health-system specialty pharmacy led to the integration of a CPS in the main campus rheumatology clinic. Benchmarking was completed by assessing the prior prescription capture rate using electronic medical record analytics and Loopback Analytics (a database of prescription capture for the health-system specialty pharmacy). The existing workflows for both the rheumatology clinic and specialty pharmacy were observed with regard to biologic medication ordering and processing. Strategies for an updated workflow for biologic ordering with the incorporation of an embedded CPS in the rheumatology clinic were designed. This new workflow was established with key stakeholders, including the CPS, rheumatology providers, clinic staff, and pharmacy technicians. Once the workflow was established, all parties were educated and updated, including rheumatology providers, nursing staff, and specialty pharmacy staff. Prescription capture rate was monitored on a monthly basis. RESULTS: Prescription capture increased from 13.16% before pharmacist implementation (October to December 2021) to 35.42% after pharmacist implementation (October to December 2022) (P = 0.019). During the same periods, the revenue generated increased from $43,222.89 to $135,198.70 (P = 0.224) and the proportion of prescriptions initially sent to the health-system specialty pharmacy compared to other specialty pharmacies increased from 37% to 79% (P < 0.001) with CPS implementation. CONCLUSION: Expansion and implementation of pharmacy services by integrating a CPS in a rheumatology ambulatory clinic increased prescription capture and pharmacy revenue while optimizing patient care. We hope to expand similar CPS services to other clinics within the health system.

Optimization of Lipid-Lowering Therapy for High Cardiovascular Risk Patients Through Electronic Medical Record Reporting and Pharmacist Evaluation.

BACKGROUND: Identification of high cardiovascular risk patients on suboptimal lipid-lowering therapy (LLT) may be possible through electronic medical record (EMR) reporting, presenting an opportunity for pharmacist involvement in optimizing drug regimens. OBJECTIVES: To (a) identify high cardiovascular risk patients with opportunities for LLT optimization through EMR reporting and (b) evaluate effectiveness of pharmacist review and treatment algorithm on recommending treatment modifications compared with algorithm application alone. METHODS: We generated an EMR report to identify adult patients aged 21-75 years with clinical atherosclerotic cardiovascular disease and low-density lipoprotein cholesterol (LDL-C) level ≥ 70 mg/dL during a 6-month period and collected pertinent data elements. We selected a subgroup of patients for remote pharmacist review and determined recommendations based on our predefined LLT optimization algorithm and pharmacist clinical judgment. One pharmacist was responsible for making all recommendations and communicated potential treatment modification to primary care providers via email and/or EMR messaging. We tracked provider acceptable/rejection rate to all recommendations made. We also compared recommendations based on using the algorithm alone to combining pharmacist chart review and algorithm and examined reasons for any discrepancies. RESULTS: 941 patients met inclusion criteria, with 399 patients (42.4%) not currently on any LLT. At baseline, 249 patients (25.3%) were on a high-intensity statin, and 19 (1.9%) were on a proprotein convertase subtilisin/kexin type 9 inhibitor. A subgroup of 34 patients were reviewed, of which 30 (88.2%) were on suboptimal therapy despite not achieving LDL-C goals. The pharmacist recommended to intensify statin therapy for 16 patients (47.1%), initiate nonstatin therapy for 9 patients (26.5%), and initiate statin therapy in 5 patients (14.7%). Pharmacist recommendation acceptance rate was 53.3%, with no response received in 26.6% of cases. The algorithm evaluation alone yielded the same recommendation as the combined pharmacist review with algorithm in 30 (88.2%) of the cases and differed in 4 cases. CONCLUSIONS: The underutilization of LLT among high cardiovascular risk patients remains a growing issue despite effective treatment options with cardiovascular benefits. Pharmacists may be able to identify these patients by using reportable EMR data elements and applying a treatment optimization algorithm to make therapy recommendations and improve outcomes. DISCLOSURES: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors have no relevant declarations of interest to disclose. This study was presented as a poster presentation at the APhA Annual Meeting, March 2019, Seattle, WA, and as a platform presentation at the Eastern States Conference, May 2019, Hershey, PA.

Using Geographic Information Systems (GIS) to understand a community's primary care needs.

INTRODUCTION: A key element for reducing health care costs and improving community health is increased access to primary care and preventative health services. Geographic information systems (GIS) have the potential to assess patterns of health care utilization and community-level attributes to identify geographic regions most in need of primary care access. METHODS: GIS, analytical hierarchy process, and multiattribute assessment and evaluation techniques were used to examine attributes describing primary care need and identify areas that would benefit from increased access to primary care services. Attributes were identified by a collaborative partnership working within a practice-based research network using tenets of community-based participatory research. Maps were created based on socioeconomic status, population density, insurance status, and emergency department and primary care safety-net utilization. RESULTS: Individual and composite maps identified areas in our community with the greatest need for increased access to primary care services. CONCLUSIONS: Applying GIS to commonly available community- and patient-level data can rapidly identify areas most in need of increased access to primary care services. We have termed this a Multiple Attribute Primary Care Targeting Strategy. This model can be used to plan health services delivery as well as to target and evaluate interventions designed to improve health care access.

Geographic Information Systems (GIS) demonstrating primary care needs for a transitioning hispanic community.

BACKGROUND: Hispanics are the largest and fastest growing minority group in the United States. Charlotte, NC, had the 4th fastest growing Hispanic community in the nation between 1990 to 2000. Gaining understanding of the patterns of health care use for this changing population is a key step toward designing improved primary care access and community health. METHODS: The Multiple Attribute Primary Care Targeting Strategy process was applied to key patient- and community-level attributes describing the Charlotte Hispanic community. Maps were created based on socioeconomic status, population density, insurance status, and use of the emergency department as a primary care safety net. Each of these variables was weighed and added to create a single composite map. RESULTS: Individual attribute maps and the composite map identified geographic locations where Hispanic community members would most benefit from increased access to primary care services. CONCLUSIONS: Using the Multiple Attribute Primary Care Targeting Strategy process we were able to identify geographic areas within our community where many Hispanic immigrants face barriers to accessing appropriate primary care services. These areas can subsequently be targeted for interventions that improve access to primary care and reduce emergency department use. The geospatial model created through this process can be monitored over time to determine the effectiveness of these interventions.