Staphylococcus aureus–associated Skin and Soft Tissue Infections in Ambulatory Care

The rise in visits to outpatient and emergency departments for skin and soft tissue infections may reflect the emergence of community-associated methicillin-resistant Staphylococcus aureus

Antimicrobial-Drug Prescription in Ambulatory Care Settings, United States, 1992–2000

During the 1990s, as antimicrobial resistance increased among pneumococci, many organizations promoted appropriate antimicrobial use to combat resistance. We analyzed data from the National Ambulatory Medical Care Survey, an annual sample survey of visits to office-based physicians, and the National Hospital Ambulatory Medical Care Survey, an annual sample survey of visits to hospital emergency and outpatient departments, to describe trends in antimicrobial prescribing from 1992 to 2000 in the United States. Approximately 1,100–1,900 physicians reported data from 21,000–37,000 visits; 200–300 outpatient departments reported data for 28,000–35,000 visits; ~400 emergency departments reported data for 21,000–36,000 visits each year. In that period, the population- and visit-based antimicrobial prescribing rates in ambulatory care settings decreased by 23% and 25%, respectively, driven largely by a decrease in prescribing by office-based physicians. Antimicrobial prescribing rates changed as follows: amoxicillin and ampicillin, –43%; cephalosporins, –28%; erythromycin, –76%; azithromycin and clarithromycin, +388%; quinolones, +78%; and amoxicillin/clavulanate, +72%. This increasing use of azithromycin, clarithromycin, and quinolones warrants concern as macrolide- and fluoroquinolone-resistant pneumococci are increasing

Using Queueing Theory to Increase the Effectiveness of Emergency Department Provider Staffing

Objectives: Significant variation in emergency department (ED) patient arrival rates necessitates the adjustment of staffing patterns to optimize the timely care of patients. This study evaluated the effectiveness of a queueing model in identifying provider staffing patterns to reduce the fraction of patients who leave without being seen. Methods: The authors collected detailed ED arrival data from an urban hospital and used a Lag SIPP queueing analysis to gain insights on how to change provider staffing to decrease the proportion of patients who leave without being seen. The authors then compared this proportion for the same 39-week period before and after the resulting changes. Results: Despite an increase in arrival volume of 1,078 patients (6.3%), an average increase in provider hours of 12 hours per week (3.1%) resulted in 258 fewer patients who left without being seen. This represents a decrease in the proportion of patients who left without being seen by 22.9%. Restricting attention to a four-day subset of the week during which there was no increase in total provider hours, a reallocation of providers based on the queueing model resulted in 161 fewer patients who left without being seen (21.7%), despite an additional 548 patients (5.5%) arriving in the second half of the study. Conclusions: Timely access to a provider is a critical dimension of ED quality performance. In an environment in which EDs are often understaffed, analyses of arrival patterns and the use of queueing models can be extremely useful in identifying the most effective allocation of staff