ABSTRACT
Background: Hospitalized patients are at increased risk of developing venous thromboembolism (VTE). The Padua Prediction Score (PPS) was developed to help quantify the risk of VTE for hospitalized patients and guide prescribing of pharmacologic thromboprophylaxis. This study aims to assess whether PPS embedded within an admission order set was utilized appropriately to prescribe or withhold pharmacologic thromboprophylaxis. Methods: This single center, retrospective observational cohort study evaluated adult patients aged ≥ 18 years between June 2017 and June 2020. A random sample of 250 patient charts meeting inclusion criteria were reviewed to calculate PPSs, and clinician notes were reviewed for documentation as to whether thromboprophylaxis was given or withheld appropriately based on the PPS. A second cohort of patients admitted within the study period meeting inclusion criteria and readmitted for VTE within 45 days of discharge were evaluated to determine appropriateness of inpatient VTE thromboprophylaxis during index hospitalization based on the PPS. Results: Of the 250 patients examined, 118 (47.2%) had a PPS < 4 on admission. Of the 118 patients, 58 (49.2%) were inappropriately prescribed pharmacologic thromboprophylaxis administered within 24 hours of admission. The clinical rationale for giving thromboprophylaxis when not indicated was provided for only 2 (3.4%) of the 58 patients. Of the 132 patients with a PPS ≥ 4, 11 (8.3%) had thromboprophylaxis appropriately withheld and for 33 (25.0%) it was inappropriately withheld. A total of 88 (66.7%) patients received thromboprophylaxis as indicated by a PPS ≥ 4. Conclusions: Despite the inclusion of the PPS calculator in the facility's admission order set, this study showed pharmacologic thromboprophylaxis was frequently inappropriately given or withheld. This suggests written protocols and order sets may not be solely sufficient to ensure appropriate VTE prophylaxis in actual practice. Incorporation of additional tools, such as dashboards and scorecards, should be explored.
ABSTRACT
BACKGROUND: A pharmacist-physician covisit model in which patients see both a pharmacist and physician on the same day was established in a primary care practice. Previously, patients were seen in a referrals-based model in which providers referred patients for clinical pharmacy services on a different day. OBJECTIVE: To assess access to clinical pharmacy services in a pharmacist-physician covisit model compared to a referrals-based model. METHODS: A retrospective chart review was completed for patients who were seen by physicians on pre-specified half-days of clinic before and after implementation of the covisit model. Covisit model half-days between June 29, 2018 and September 30, 2018 and matched half-days from 2015 were included. Charts were reviewed to determine if patients scheduled to see the physician would benefit from clinical pharmacy services, including being seen for chronic disease management, eligible for a Medicare Annual Wellness Visit (AWV), prescribed medications that required counseling, had an adverse medication-related event, or had adherence concerns. Those eligible for clinical pharmacy services were further reviewed to determine if the patient interacted with a pharmacist within three months of their visit. RESULTS: Prior to implementation of the covisit model, 123 patient visits were completed on the pre-specified half-days. Of these, 61 patients (49.6%) were deemed eligible for clinical pharmacy services. In the covisit model, 149 patients were seen by the physician, of which 69 patients (46%) were eligible for clinical pharmacy services. More patients in the covisit cohort went on to interact with a pharmacist (56 patients, 81% vs. 10 patients, 16%, adjusted OR = 32.98, 95% CI [8.89-122.39]). The most common reasons patients were identified for clinical pharmacy services were eligibility for AWV, hypertension, and diabetes. CONCLUSIONS: A pharmacist-physician covisit model significantly increased accessibility to clinical pharmacy services compared to a referrals-based model.
