An Electronic Medical Record Intervention to Increase Pharmacologic Prophylaxis for Venous Thromboembolism in Emergency Department Observation Patients.

STUDY OBJECTIVE: The role of venous thromboembolism (VTE) prophylaxis among patients receiving emergency department (ED) observation unit care is unclear. We investigated an electronic health record-based clinical decision support tool aimed at increasing pharmacologic VTE prophylaxis use among at-risk patients placed in ED observation units. METHODS: We conducted an interrupted time-series study of an Epic-based best practice advisory implemented in May 2019 at a health care system comprising 2 academic medical centers and 4 community hospitals with dedicated ED observation units. The best practice advisory alerted staff at 24 hours to conduct a risk assessment and linked to a VTE prophylaxis order set. We used an interrupted time series, Bayesian structured time series, and a multivariable mixed-effect regression model to estimate the intervention effect. RESULTS: Prior to the best practice advisory implementation, there were 8,895 ED observation unit patients with a length of stay more than or equal to 24 hours, and 0.9% received pharmacologic VTE prophylaxis. Afterward, there were 12,664 ED observation unit patients with a length of stay more than or equal to 24 hours, and 4.8% received pharmacologic VTE prophylaxis. The interrupted time series and causal impact analysis showed a statistically significant increase in VTE prophylaxis (eg, absolute percent difference 3.8%, 95% confidence interval 3.5 to 4.1). In a multivariable model, only the intervention was significantly associated with receiving VTE prophylaxis (odds ratio 4.56, 95% confidence interval 2.22 to 9.37). CONCLUSION: An electronic health record-based alert helped to prompt staff caring for ED observation unit patients at risk for VTE with prolonged visits to order recommended pharmacologic prophylaxis. The best risk assessment model to use and the true incidence of VTE events in this population are unclear.

Prospective External Validation of a Commercial Model Predicting the Likelihood of Inpatient Admission From the Emergency Department.

STUDY OBJECTIVE: Early notification of admissions from the emergency department (ED) may allow hospitals to plan for inpatient bed demand. This study aimed to assess Epic's ED Likelihood to Occupy an Inpatient Bed predictive model and its application in improving hospital bed planning workflows. METHODS: All ED adult (18 years and older) visits from September 2021 to August 2022 at a large regional health care system were included. The primary outcome was inpatient admission. The predictive model is a random forest algorithm that uses demographic and clinical features. The model was implemented prospectively, with scores generated every 15 minutes. The area under the receiver operator curves (AUROC) and precision-recall curves (AUPRC) were calculated using the maximum score prior to the outcome and for each prediction independently. Test characteristics and lead time were calculated over a range of model score thresholds. RESULTS: Over 11 months, 329,194 encounters were evaluated, with an incidence of inpatient admission of 25.4%. The encounter-level AUROC was 0.849 (95% confidence interval [CI], 0.848 to 0.851), and the AUPRC was 0.643 (95% CI, 0.640 to 0.647). With a prediction horizon of 6 hours, the AUROC was 0.758 (95% CI, 0.758 to 0.759,) and the AUPRC was 0.470 (95% CI, 0.469 to 0.471). At a predictive model threshold of 40, the sensitivity was 0.49, the positive predictive value was 0.65, and the median lead-time warning was 127 minutes before the inpatient bed request. CONCLUSION: The Epic ED Likelihood to Occupy an Inpatient Bed model may improve hospital bed planning workflows. Further study is needed to determine its operational effect.

Implementation of an Electronic Alert to Improve Timeliness of Second Dose Antibiotics for Patients With Suspected Serious Infections in the Emergency Department: A Quasi-Randomized Controlled Trial.

STUDY OBJECTIVE: Delays in the second dose of antibiotics in the emergency department (ED) are associated with increased morbidity and mortality in patients with serious infections. We analyzed the influence of clinical decision support to prevent delays in second doses of broad-spectrum antibiotics in the ED. METHODS: We allocated adult patients who received cefepime or piperacillin/tazobactam in 9 EDs within an integrated health care system to an electronic alert that reminded ED clinicians to reorder antibiotics at the appropriate interval vs usual care. The primary outcome was a median delay in antibiotic administration. Secondary outcomes were rates of intensive care unit (ICU) admission, hospital mortality, and hospital length of stay. We included a post hoc secondary outcome of frequency of major delay (>25% of expected interval for second antibiotic dose). RESULTS: A total of 1,113 ED patients treated with cefepime or piperacillin/tazobactam were enrolled in the study, of whom 420 remained under ED care when their second dose was due and were included in the final analysis. The clinical decision support tool was associated with reduced antibiotic delays (median difference 35 minutes, 95% confidence interval [CI], 5 to 65). There were no differences in ICU transfers, inpatient mortality, or hospital length of stay. The clinical decision support tool was associated with decreased probability of major delay (absolute risk reduction 13%, 95% CI, 6 to 20). CONCLUSIONS: The implementation of a clinical decision support alert reminding clinicians to reorder second doses of antibiotics was associated with a reduction in the length and frequency of antibiotic delays in the ED. There was no effect on the rates of ICU transfers, inpatient mortality, or hospital length of stay.