Olesya Baker

Where Do Freestanding Emergency Departments Choose to Locate? A National Inventory and Geographic Analysis in Three States

Study objective We determine the number and location of freestanding emergency departments (EDs) across the United States and determine the population characteristics of areas where freestanding EDs are located. Methods We conducted a systematic inventory of US freestanding EDs. For the 3 states with the highest number of freestanding EDs, we linked demographic, insurance, and health services data, using the 5‐digit ZIP code corresponding to the freestanding ED’s location. To create a comparison nonfreestanding ED group, we matched 187 freestanding EDs to 1,048 nonfreestanding ED ZIP codes on land and population within state. We compared differences in demographic, insurance, and health services factors between matched ZIP codes with and without freestanding EDs, using univariate regressions with weights. Results We identified 360 freestanding EDs located in 30 states; 54.2% of freestanding EDs were hospital satellites, 36.6% were independent, and 9.2% were not classifiable. The 3 states with the highest number of freestanding EDs accounted for 66% of all freestanding EDs: Texas (181), Ohio (34), and Colorado (24). Across all 3 states, freestanding EDs were located in ZIP codes that had higher incomes and a lower proportion of the population with Medicaid. In Texas and Ohio, freestanding EDs were located in ZIP codes with a higher proportion of the population with private insurance. In Texas, freestanding EDs were located in ZIP codes that had fewer Hispanics, had a greater number of hospital‐based EDs and physician offices, and had more physician visits and medical spending per year than ZIP codes without a freestanding ED. In Ohio, freestanding EDs were located in ZIP codes with fewer hospital‐based EDs. Conclusion In Texas, Ohio, and Colorado, freestanding EDs were located in areas with a better payer mix. The location of freestanding EDs in relation to other health care facilities and use and spending on health care varied between states.

The Effect of Opioid Prescribing Guidelines on Prescriptions by Emergency Physicians in Ohio

Study objective The objective of our study is to evaluate the association between Ohio’s April 2012 emergency physician guidelines aimed at reducing inappropriate opioid prescribing and the number and type of opioid prescriptions dispensed by emergency physicians. Methods We used Ohio’s prescription drug monitoring program data from January 1, 2010, to December 31, 2014, and included the 5 most commonly prescribed opioids (hydrocodone, oxycodone, tramadol, codeine, and hydromorphone). The primary outcome was the monthly statewide prescription total of opioids written by emergency physicians in Ohio. We used an interrupted time series analysis to compare pre‐ and postguideline level and trend in number of opioid prescriptions dispensed by emergency physicians per month, number of prescriptions stratified by 5 commonly prescribed opioids, and number of prescriptions for greater than 3 days’ supply of opioids. Results Beginning in January 2010, the number of prescriptions dispensed by all emergency physicians in Ohio decreased by 0.3% per month (95% confidence interval [CI] –0.49% to –0.15%). The implementation of the guidelines in April 2012 was associated with a 12% reduction (95% CI –17.7% to –6.3%) in the level of statewide total prescriptions per month and an additional decline of 0.9% (95% CI –1.1% to –0.7%) in trend relative to the preguideline trend. The estimated effect of the guidelines on total monthly prescriptions greater than a 3‐day supply was an 11.2% reduction in level (95% CI –18.8% to –3.6%) and an additional 0.9% (95% CI –1.3% to –0.5%) decline in trend per month after the guidelines. Guidelines were also associated with a reduction in prescribing for each of the 5 individual opioids, with various effect. Conclusion In Ohio, emergency physician opioid prescribing guidelines were associated with a decrease in the quantity of opioid prescriptions written by emergency physicians. Although introduction of the guidelines occurred in parallel with other opioid‐related interventions, our findings suggest an additional effect of the guidelines on prescribing behavior. Similar guidelines may have the potential to reduce opioid prescribing in other geographic areas and for other specialties as well.

Opioid Prescriptions by Specialty in Ohio, 2010–2014

Background The current US opioid epidemic is attributed to the large volume of prescribed opioids. This study analyzed the contribution of different medical specialties to overall opioids by evaluating the pill counts and morphine milligram equivalents (MMEs) of opioid prescriptions, stratified by provider specialty, and determined temporal trends. Methods This was an analysis of the Ohio prescription drug monitoring program database, which captures scheduled medication prescriptions filled in the state as well as prescriber specialty. We extracted prescriptions for pill versions of opioids written in the calendar years 2010 to 2014. The main outcomes were the number of filled prescriptions, pill counts, MMEs, and extended-released opioids written by physicians in each specialty, and annual prescribing trends. Results There were 56,873,719 prescriptions for the studied opioids dispensed, for which 41,959,581 (73.8%) had prescriber specialty type available. Mean number of pills per prescription and MMEs were highest for physical medicine/rehabilitation (PM&R; 91.2 pills, 1,532 mg, N = 1,680,579), anesthesiology/pain (89.3 pills, 1,484 mg, N = 3,261,449), hematology/oncology (88.2 pills, 1,534 mg, N = 516,596), and neurology (84.4 pills, 1,230 mg, N = 573,389). Family medicine (21.8%) and internal medicine (17.6%) wrote the most opioid prescriptions overall. Time trends in the average number of pills and MMEs per prescription also varied depending on specialty. Conclusions The numbers of pills and MMEs per opioid prescription vary markedly by prescriber specialty, as do trends in prescribing characteristics. Pill count and MME values define each specialtys contribution to overall opioid prescribing more accurately than the number of prescriptions alone.

Association of Emergency Department Length of Stay and Crowding for Patients with ST-Elevation Myocardial Infarction

Introduction With the majority of U.S. hospitals not having primary percutaneous coronary intervention (pPCI) capabilities, the time spent at transferring emergency departments (EDs) is predictive of clinical outcomes for patients with ST-elevation myocardial infarction (STEMI). Compounding the challenges of delivering timely emergency care are the known delays caused by ED crowding. However, the association of ED crowding with timeliness for patients with STEMI is unknown. We sought to examine the relationship between ED crowding and time spent at transferring EDs for patients with STEMI. Methods We analyzed the Centers for Medicare and Medicaid Services (CMS) quality data. The outcome was time spent at a transferring ED (i.e., door-in-door-out [DIDO]), was CMS measure OP-3b for hospitals with ≥10 acute myocardial infarction (AMI) cases requiring transfer (i.e., STEMI) annually: Time to Transfer an AMI Patient for Acute Coronary Intervention. We used four CMS ED timeliness measures as surrogate measures of ED crowding: admitted length of stay (LOS), discharged LOS, boarding time, and waiting time. We analyzed bivariate associations between DIDO and ED timeliness measures. We used a linear multivariable regression to evaluate the contribution of hospital characteristics (academic, trauma, rural, ED volume) to DIDO. Results Data were available for 405 out of 4,129 hospitals for the CMS DIDO measure. These facilities were primarily non-academic (99.0%), non-trauma centers (65.4%), and in urban locations (68.5%). Median DIDO was 54.0 minutes (IQR 42.0,68.0). Increased DIDO time was associated with longer admitted LOS and boarding times. After adjusting for hospital characteristics, a one-minute increase in ED LOS at transferring facilities was associated with DIDO (coefficient, 0.084 [95% CI [0.049,0.119]]; p<0.001). This translates into a five-minute increase in DIDO for every one-hour increase in ED LOS for admitted patients. Conclusion Among patients with STEMI presenting to U.S. EDs, we found that ED crowding has a small but operationally insignificant effect on time spent at the transferring ED.

Provider familiarity with specialty society guidelines for risk stratification and management of patients with febrile neutropenia

Febrile neutropenia (FN) associated with chemotherapy occurs in 10% to 50% of patients with solid tumors and greater than 80% of those with hematological malignancies [1]. These patients are typically hospitalized and receive intravenous antibiotics, although infections occur in only 20% to 30% of febrile episodes [1]. Current guidelines from the Infectious Diseases Society of America and American Society of Clinical Oncology recommend different antibiotic regimens based on risk stratification via the Multinational Association for Supportive Care in Cancer (MASCC) score,which includes age, presence of hypotension, and other predictors [1–3]. Patients with FN and a MASCC score greater than or equal to 21 and no additional indications for inpatient care are at low risk for adverse clinical outcomes, and the guidelines recommend outpatient management with oral antibiotics [4]. Our local experience suggests that clinical practice often does not reflect this recommendation, resulting in the overuse of intravenous, broad-spectrum antibiotics, and inpatient care, but there has been little informative research on the topic [4]. This patient population is particularly vulnerable to adverse medication reactions, colonization with drug-resistant organisms, antibiotic complications (eg, Clostridium difficile colitis and vancomycin-resistant Enterococcus bacteremia) and other iatrogenic adverse events associated with an avoidable hospitalization [1,2,5].We surveyed oncologists and emergency physicians (EPs) to ascertain their familiarity with and adherence to the guidelines. We surveyed all attending physician faculty in the Department of Medical Oncology of the [blinded] (n = 109) and the Department of Emergency Medicine at [blinded] (n = 44) as of April 1, 2015. We asked if the respondentwas familiarwith theMASCC score and national specialty society guidelines regarding risk stratification and treatment for FN. We presented 2 clinical vignettes contrasting lowand highrisk patient scenarios and asked respondents to choose dispositions and treatment options indicating their usual practice. The [blinded] is a comprehensive cancer center, with more than 300 000 annual outpatient clinic visits. The [blinded] is an urban, tertiary care teaching hospital; the emergency department (ED) had 60 050 adult visits in 2014 and is the designated site of emergency and inpatient care for [blinded] patients. Our targeted response rate was greater 80 81 82 83 84 85 86 87 88 ☆ Meetings: Northeast Regional Society for Academic Emergencymeeting inWorcester, MA, in March 2016. ☆☆ Conflicts of interest: The authors have no conflicts to disclose. ☆☆☆ Author contributions: CWB, GAB, and DJP conceived the study, and CWB served as principal investigator. DJP guided the studys design and execution. ONB performed the statistical analysis of main results. JMC provided assistancewith interpretation of the data. CWB drafted themanuscript, and all authors contributed substantially to its revision. CWB takes responsibility for the paper as a whole.

Analysis of a Commercial Insurance Policy to Deny Coverage for Emergency Department Visits With Nonemergent Diagnoses

Key Points Question If commercial insurers retrospectively deny coverage for emergency department (ED) visits based on diagnoses determined to be nonemergent, what visits will be denied coverage? Findings This cross-sectional study found that 1 insurer’s list of nonemergent diagnoses would classify 15.7% of commercially insured adult ED visits for possible coverage denial. However, these visits shared the same presenting symptoms as 87.9% of ED visits, of which 65.1% received emergency-level services. Meaning A retrospective diagnosis-based policy is not associated with accurate identification of unnecessary ED visits and could put many commercially insured patients at risk of coverage denial.

Trends in Emergency Department Visits and Admission Rates Among US Acute Care Hospitals

This observational study describes trends in emergency department visits and admission rates among US acute care hospitals using data from the National Emergency Department Survey.

The Gillette Stadium Experience: A Retrospective Review of Mass Gathering Events From 2010 to 2015

OBJECTIVE Mass gathering events can substantially impact public safety. Analyzing patient presentation and transport rates at various mass gathering events can help inform staffing models and improve preparedness. METHODS A retrospective review of all patients seeking medical attention across a variety of event types at a single venue with a capacity of 68,756 from January 2010 through September 2015. RESULTS We examined 232 events with a total of 8,260,349 attendees generating 8157 medical contacts. Rates were 10 presentations and 1.6 transports per 10,000 attendees with a non-significant trend towards increased rates in postseason National Football League games. Concerts had significantly higher rates of presentation and transport than all other event types. Presenting concern varied significantly by event type and gender, and transport rate increased predictably with age. For cold weather events, transport rates increased at colder temperatures. Overall, on-site physicians did not impact rates. CONCLUSIONS At a single venue hosting a variety of events across a 6-year period, we demonstrated significant variations in presentation and transport rates. Weather, gender, event type, and age all play important roles. Our analysis, while representative only of our specific venue, may be useful in developing response plans and staffing models for similar mass gathering venues. (Disaster Med Public Health Preparedness. 2018;12:752-758).