Opioid Prescribing: Where Should Academic Emergency Departments Focus Their Efforts?

BACKGROUND: Opioid prescriptions from the emergency department (ED) are being heavily scrutinized. This has resulted in prescribing guidelines and laws. OBJECTIVE: We analyzed the "current state" of opioid prescribing practices by emergency medicine (EM) trainees to gain understanding where operational, educational, or supervisory efforts should be directed to comply with current guidelines and future legislation. METHODS: Our retrospective, observational, study was performed at an academic ED with an annual census of 61,289 visits. We extracted all 6962 opioid prescriptions attributed to the ED during calendar year 2015 from the electronic health record. Error prescriptions were excluded from the analysis. Overall prescribing by opioid class was performed. Prescriptions written by EM trainees were categorized by postgraduate year and compared with other prescribers. Prescribing patterns for recurrent visits were also analyzed. RESULTS: Of the 6962 opioid discharge prescriptions, 5515 were written by EM providers. No refills were provided. A mean of 15.4 pills (95% confidence interval 13.9-16.9) were prescribed. Analysis of variance did not detect a significant difference between mean numbers of pills prescribed by EM providers. However, there was a significant difference between EM and non-EM prescribers. Less-experienced EM providers exhibited greater variability with regard to class and preparation. There were 389 prescriptions written for patients who received at least one other opioid prescription in the preceding 30 days. The number of pills dispensed decreased with increasing prior visits. CONCLUSION: EM providers prescribe short courses of opiates regardless of postgraduate year. Patients returning to the ED received fewer pills on subsequent visits. Non-EM providers prescribe larger numbers of pills per prescription. This information will help focus future operational efforts and educational efforts to comply with guidelines and laws.

Evaluating the Relationship between Productivity and Quality in Emergency Departments.

BACKGROUND: In the United States, emergency departments (EDs) are constantly pressured to improve operational efficiency and quality in order to gain financial benefits and maintain a positive reputation. OBJECTIVES: The first objective is to evaluate how efficiently EDs transform their input resources into quality outputs. The second objective is to investigate the relationship between the efficiency and quality performance of EDs and the factors affecting this relationship. METHODS: Using two data sources, we develop a data envelopment analysis (DEA) model to evaluate the relative efficiency of EDs. Based on the DEA result, we performed multinomial logistic regression to investigate the relationship between ED efficiency and quality performance. RESULTS: The DEA results indicated that the main source of inefficiencies was working hours of technicians. The multinomial logistic regression result indicated that the number of electrocardiograms and X-ray procedures conducted in the ED and the length of stay were significantly associated with the trade-offs between relative efficiency and quality. Structural ED characteristics did not influence the relationship between efficiency and quality. CONCLUSIONS: Depending on the structural and operational characteristics of EDs, different factors can affect the relationship between efficiency and quality.

Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients?

BACKGROUND: Emergency department crowding has led to innovative "front end" care models to safely and efficiently care for medium and lower acuity patients. In the United States, most treatment algorithms rely on the emergency severity index (ESI) triage tool to sort patients. However, there are no objective criteria used to differentiate ESI 3 patients. OBJECTIVE: We seek to derive and validate a model capable of predicting patient discharge disposition (DD) using variables present on arrival to the emergency department for ESI 3 patients. METHODS: Our retrospective cohort study included adult patients with an ESI triage designation 3 treated in an academic emergency department over the course of 2 successive years (2013-2015). The main outcome was DD. Two datasets were used in the modeling process. One dataset, the derivation dataset (n = 25,119), was used to develop the statistical model, while the second dataset, the validation dataset (n = 24,639), was used to evaluate the statistical model's prediction performance. RESULTS: All variables included in the derivation model were uniquely associated with DD status (p < 0.001). We assessed multivariate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for age (2.50 [95% CI 2.35-2.65]), arrival mode (1.85 [95% CI 1.74-1.96]), heart rate (1.31 [95% CI 1.26-1.37]), sex (1.35 [95% CI 1.28-1.43]), oxygen saturation (1.06 [95% CI 1.01-1.10]), temperature (1.10 [95% CI 1.06-1.15]), systolic blood pressure (1.18 [95% CI 1.12-1.25]), diastolic blood pressure (1.16 [95% CI 1.09-1.22]), respiratory rate (1.05 [95% CI 1.01-1.10]), and pain score (1.13 [95% CI 1.06-1.21]). The validation C-statistic was 0.73. CONCLUSION: We derived and validated a model and created a nomogram with acceptable discrimination of ESI 3 patients on arrival for purposes of predicting DD. Incorporating these variables into the care of these patients could improve patient flow by identifying patients who are likely to be discharged.

Training in transvaginal sonography using pelvic ultrasound simulators versus live models: a randomized controlled trial.

PURPOSE: To compare pelvic ultrasound simulators (PSs) with live models (LMs) for training in transvaginal sonography (TVS). METHOD: The authors conducted a prospective, randomized controlled trial of 145 eligible medical students trained in TVS in 2011-2012 with either a PS or an LM. A patient educator was used for LM training. Simulated intrauterine and ectopic pregnancy models were used for PS training. Students were tested using a standardized patient who evaluated their professionalism. A proctor, blinded to training type, scored their scanning technique. Digital images were saved for blinded review. Students rated their training using a Likert scale (0 = not very well; 10 = very well). The primary outcome measure was students' overall performance on a 40-point assessment tool for professionalism, scanning technique, and image acquisition. Poisson regression and Student t test were used for comparisons. RESULTS: A total of 134 students participated (62 trained using a PS; 72 using an LM). Mean overall test scores were 56% for the PS group and 69% for the LM group (P = .001). A significant difference was identified in scanning technique (PS, 60% versus LM, 73%; P = .001) and image acquisition (PS, 37% versus LM, 59%; P = .001). None was observed for professionalism. The PS group rated their training experience at 4.4, whereas the LM group rated theirs at 6.2 (P < .001). CONCLUSIONS: Simulators do not perform as well as LMs for training novices in TVS, but they may be useful as an adjunct to LM training.