Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study.

BACKGROUND: Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR). OBJECTIVE: We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity. DESIGN: Retrospective cohort study. PARTICIPANTS: Adult patients discharged from a CCHS hospital April 2017-September 2020. MAIN MEASURES: Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic. RESULTS: The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories. CONCLUSIONS: The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Electronic medical record-based interventions to encourage opioid prescribing best practices in the emergency department.

OBJECTIVE: Overdose from opioids has reached epidemic proportions. Large healthcare systems can utilize existing technology to encourage responsible opioid prescribing practices. Our study measured the effects of using the electronic medical record (EMR) with direct clinician feedback to standardize opioid prescribing practices within a large healthcare system. METHODS: This retrospective multicenter study compared a 12 month pre- and post-intervention in 14 emergency departments after four interventions utilizing the EMR were implemented: (1) deleting clinician preference lists, (2) defaulting dose, frequency, and quantity, (3) standardizing formulary to encourage best practices, and (4) creating dashboards for clinician review with current opioid prescribing practices. Outlying clinicians received feedback through email and direct counseling. Total number of opioid prescriptions per 100 discharges pre- and post-intervention were recorded as primary outcome. Secondary outcomes included number of prescriptions per 100 discharges/clinician exceeding 3-day supply (defined as 12 tablets), number exceeding 30 morphine equivalent daily dose (MEDD)/day, and number of non-formulary prescriptions. RESULTS: There were >700,000 discharges during pre- and post-intervention periods. Percentage of total number opioid prescriptions per 100 discharges decreased from 14.4% to 7.4%, a 7.0% absolute reduction, (95% CI,6.9%-7.2%). There was a 5.9% to 0.7% reduction in prescriptions exceeding 3-days, (95% CI, 5.1%-5.3%), a 4.3% to 0.3% reduction in prescriptions exceeding 30 MEDD, (95% CI, 3.9%-4.0%), and a 0.3% to 0.1% reduction in non-formulary prescriptions, (95% CI, 0.2%-0.3%). CONCLUSIONS: A multi modal approach using EMR interventions which provide real time data and direct feedback to clinicians can facilitate appropriate opioid prescribing.

Feasibility of a pharmacy-based influenza immunization program in an academic emergency department.

BACKGROUND: From September 2002 to August 2010, 13 patients who were discharged from the emergency department (ED) were immunized against influenza. This correlates with a time when pharmacists were not permitted to vaccinate patients in New York. OBJECTIVE: The objectives of this study were to determine the feasibility of a pharmacist-based influenza vaccination program in the ED, assess patients' willingness to be vaccinated by a pharmacist, and identify reasons for declination. METHODS: This was a cross-sectional study involving English-speaking patients older than 18 years. The pharmacist vaccinated patients, if they consented. Patients who refused to be vaccinated were asked why they did not want to receive the vaccine and their perception of pharmacists vaccinating patients in the ED. The percentage of patients vaccinated; time elements associated with the process of screening, counseling, and vaccinating; and the type and frequency of adverse events were recorded. RESULTS: Of 149 patients, 62 patients (41%) agreed to receive the vaccine, a 4-fold increase from the previous 8 years. The median screening and vaccination time was 8 minutes. Of those not receiving a vaccine in the ED, 74% were willing to receive the influenza vaccine from a pharmacist, and 78% were willing to receive the vaccine in the ED. The most common reason for refusal was perception of low self-risk (43.9%). No adverse events were reported. CONCLUSIONS: A pharmacist-based influenza immunization program is feasible in the ED and has the potential to successfully and safely increase the percentage of adult patients receiving the vaccine.

A multifaceted approach in leveraging electronic health record tools for safe inpatient use of concentrated U-500 insulin.

PURPOSE: The Institute for Safe Medication Practices classifies subcutaneous insulin as a high-risk medication. Concentrated U-500 insulin carries additional risks in comparison to conventional U-100 insulin, as the 5-fold more concentrated nature of this product, limitations to insulin pen dosing, and various devices for dose measurement may lead to miscommunication of patient-reported doses, resulting in downstream errors in ordering, verification, or administration. We describe a multifaceted approach to leveraging technical tools within the electronic health record (EHR) for U-500 insulin use. SUMMARY: At Cleveland Clinic, the U-500 insulin use process evolved in a number of phases using EHR tools. Phase 1 included new clinical decision support and documentation tools during order entry, including a customized alert that fired during order entry recommending that the prescriber order a consult with endocrinology and requiring the prescriber to provide the patient's home insulin measuring device and the source of the patient's reported home dose. In order verification, a customized alert fired directing the pharmacist to contact the patient or patient's nurse and validate the information provided by the prescriber. Phase 2 involved transitioning dispensing of patient-specific doses from tuberculin syringes to U-500 insulin syringes. Phase 3 transitioned to use of U-500 insulin pens and included automatic dose rounding of ordered doses down to the nearest 5 units, and an additional customized pharmacist alert intended for cost conservation was added to fire if the patient had a recent administration of U-500 insulin documented, directing the pharmacist to determine whether the nurse needed a new pen dispensed. CONCLUSION: Cleveland Clinic successfully implemented customized tools and processes within the EHR pertaining to the prescribing, verification, dispensing, and administration of U-500 insulin.

Implementation of Clinical Pharmacogenomics within a Large Health System: From Electronic Health Record Decision Support to Consultation Services.

The number of clinically relevant gene-based guidelines and recommendations pertaining to drug prescribing continues to grow. Incorporating gene-drug interaction information into the drug-prescribing process can help optimize pharmacotherapy outcomes and improve patient safety. However, pharmacogenomic implementation barriers exist such as integration of pharmacogenomic results into electronic health records (EHRs), development and deployment of pharmacogenomic decision support tools to EHRs, and feasible models for establishing ambulatory pharmacogenomic clinics. We describe the development of pharmacist-managed pharmacogenomic services within a large health system. The Clinical Pharmacogenetics Implementation Consortium guidelines for HLA-B*57:01-abacavir, HLA-B*15:02-carbamazepine, and TPMT-thiopurines (i.e., azathioprine, mercaptopurine, and thioguanine) were systematically integrated into patient care. Sixty-three custom rules and alerts (20 for TPMT-thiopurines, 8 for HLA-B*57:01-abacavir, and 35 for HLA-B*15:02-anticonvulsants) were developed and deployed to the EHR for the purpose of providing point-of-care pharmacogenomic decision support. In addition, a pharmacist and physician-geneticist collaboration established a pharmacogenomics ambulatory clinic. This clinic provides genetic testing when warranted, result interpretation along with pharmacotherapy recommendations, and patient education. Our processes for developing these pharmacogenomic services and solutions for addressing implementation barriers are presented.