Reliability of a Measure of Admission Intensity for Emergency Physicians.

STUDY OBJECTIVE: We assess the stability of a measure of emergency department (ED) admission intensity for value-based care programs designed to reduce variation in ED admission rates. Measure stability is important to accurately assess admission rates across sites and among physicians. METHODS: We sampled data from 358 EDs in 41 states (January 2018 to December 2021), separate from sites where the measure was derived. The measure is the ED admission rate per 100 ED visits for 16 clinical conditions and 535 included International Classification of Disease 10 diagnosis codes. We used descriptive plots and multilevel linear probability models to assess stability over time across EDs and among physicians. RESULTS: Across included 3,571 ED-quarters, the average admission rate was 27.6% (95% confidence interval [CI] 26.0% to 28.2%). The between-facility standard deviation was 9.7% (95% CI 9.0% to 10.6%), and the within-facility standard deviation was 3.0% (95% CI 2.95% to 3.10%), with an intraclass correlation coefficient of 0.91. At the physician-quarter level, the average admission rate was 28.3% (95% CI 28.0% to 28.5%) among 7,002 physicians. Relative to their site's mean in each quarter, the between-physician standard deviation was 6.7% (95% CI 6.6% to 6.8%), and the within-physician standard deviation was 5.5% (95% CI 5.5% to 5.6%), with an intraclass correlation coefficient of 0.59. Moreover, 2.9% of physicians were high-admitting in 80%+ of their practice quarters relative to their peers in the same ED and in the same quarter, whereas 3.9% were low-admitting. CONCLUSION: The measure exhibits stability in characterizing ED-level admission rates and reliably identifies high- and low-admitting physicians.

Are Smaller Emergency Departments More Prone to Volume Variability?

INTRODUCTION: Daily patient volume in emergency departments (ED) varies considerably between days and sites. Although studies have attempted to define "high-volume" days, no standard definition exists. Furthermore, it is not clear whether the frequency of high-volume days, by any definition, is related to the size of an ED. We aimed to determine the correlation between ED size and the frequency of high-volume days for various volume thresholds, and to develop a measure to identify high-volume days. METHODS: We queried retrospective patient arrival data including 1,682,374 patient visits from 32 EDs in 12 states between July 1, 2018-June 30, 2019 and developed linear regression models to determine the correlation between ED size and volume variability. In addition, we performed a regression analysis and applied the Pearson correlation test to investigate the significance of median daily volumes with respect to the percent of days that crossed four volume thresholds ranging from 5-20% (in 5% increments) greater than each site's median daily volume. RESULTS: We found a strong negative correlation between ED median daily volume and volume variability (R2 = 81.0%; P < 0.0001). In addition, the four regression models for the percent of days exceeding specified thresholds greater than their daily median volumes had R2 values of 49.4%, 61.2%, 70.0%, and 71.8%, respectively, all with P < 0.0001. CONCLUSION: We sought to determine whether smaller EDs experience high-volume days more frequently than larger EDs. We found that high-volume days, when defined as days with a count of arrivals at or above certain median-based thresholds, are significantly more likely to occur in lower-volume EDs than in higher-volume EDs. To the extent that EDs allocate resources and plan to staff based on median volumes, these results suggest that smaller EDs are more likely to experience unpredictable, volume-based staffing challenges and operational costs. Given the lack of a standard measure to define a high-volume day in an ED, we recommend 10% above the median daily volume as a metric, for its relevance, generalizability across a broad range of EDs, and computational simplicity.

Emergency physicians' active patient queues over the course of a shift.

OBJECTIVES: When emergency physicians see new patients in an ad libitum system, they see fewer patients as the shift progresses. However, it is unclear if this reflects a decreasing workload, as patient assessments often span many hours. We sought to investigate whether the size of a physician's queue of active patients similarly declines over a shift. METHODS: Retrospective cohort study, conducted over two years in three community hospitals in the Northeastern United States, with 8 and 9-h shifts. Timestamps of all encounters were recorded electronically. Generalized estimating equations were constructed to predict the number of active patients a physician concurrently managed per hour. RESULTS: We evaluated 64 physicians over a two-year period, with 9822 physician-shifts. Across all sites, physicians managed an increasing queue of active patients in the first several hours. This queue plateaued in the middle of the shift, declining in the final hours, independently of other factors. Physicians' queues of active patients increased slightly with greater volume and acuity, but did not affect the overall pattern of work. Similarly, working alone or with colleagues had little effect on the number of active patients managed. CONCLUSIONS: Emergency physicians in an ad libitum system tend to see new patients until reaching a stable roster of active patients. This pattern may help explain why physicians see fewer new patients over the course of a shift, should be factored into models of throughput, and suggests new avenues for evaluating relationships between physician workload, patient safety, physicians' well-being, and the quality of care.

Modelling attending physician productivity in the emergency department: a multicentre study.

OBJECTIVES: Emergency physician productivity, often defined as new patients evaluated per hour, is essential to planning clinical operations. Prior research in this area considered this a static quantity; however, our group's study of resident physicians demonstrated significant decreases in hourly productivity throughout shifts. We now examine attending physicians' productivity to determine if it is also dynamic. METHODS: This is a retrospective cohort study, conducted from 2014 to 2016 across three community hospitals in the north-eastern USA, with different schedules and coverage. Timestamps of all patient encounters were automatically logged by the sites' electronic health record. Generalised estimating equations were constructed to predict productivity in terms of new patients per shift hour. RESULTS: 207 169 patients were seen by 64 physicians over 2 years, comprising 9822 physician shifts. Physicians saw an average of 15.0 (SD 4.7), 20.9 (SD 6.4) and 13.2 (SD 3.8) patients per shift at the three sites, with 2.97 (SD 0.22), 2.95 (SD 0.24) and 2.17 (SD 0.09) in the first hour. Across all sites, physicians saw significantly fewer new patients after the first hour, with more gradual decreases subsequently. Additional patient arrivals were associated with greater productivity; however, this attenuates substantially late in the shift. The presence of other physicians was also associated with slightly decreased productivity. CONCLUSIONS: Physician productivity over a single shift follows a predictable pattern that decreases significantly on an hourly basis, even if there are new patients to be seen. Estimating productivity as a simple average substantially underestimates physicians' capacity early in a shift and overestimates it later. This pattern of productivity should be factored into hospitals' staffing plans, with shifts aligned to start with the greatest volumes of patient arrivals.

The Impact of Hospital and Patient Factors on the Emergency Department Decision to Admit.

BACKGROUND: Substantial variation exists in rates of emergency department (ED) admission. We examine this variation after accounting for local and community characteristics. OBJECTIVES: Elucidate the factors that contribute to admission variation that are amenable to intervention with the goal of reducing variation and health care costs. METHODS: We conducted a retrospective cross-sectional study of 1,412,340 patient encounters across 18 sites from 2012-2013. We calculated the adjusted hospital-level admission rates using multivariate logistic regression. We adjusted for patient, provider, hospital, and community factors to compare admission rate variation and determine the influence of these characteristics on admission rates. RESULTS: The average adjusted admission rate was 22.9%, ranging from 16.1% (95% confidence interval [CI] 11.5-22%) to 32% (95% CI 26.0-38.8). There were higher odds of hospital admission with advancing age, male sex (odds ratio [OR] 1.20, 95% CI 1.91-1.21), and patients seen by a physician vs. mid-level provider (OR 2.26, 95% CI 2.23-2.30). There were increased odds of admission with rising ED volume, at academic institutions (OR 2.23, 95% CI 2.20-2.26) and at for-profit hospitals (OR 1.15, 95% CI 1.12-1.18). Admission rates were lower in communities with a higher per capita income, a higher rate of uninsured patients, and in more urban hospitals. In communities with the most primary providers, there were lower odds of admission (OR 0.60, 95% CI 0.57-0.68). CONCLUSION: Variation in hospital-level admission rates is associated with a number of local and community characteristics. However, the presence of persistent variation after adjustment suggests there are other unmeasured variables that also affect admission rates that deserve further study, particularly in an era of cost containment.

The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission.

STUDY OBJECTIVE: We examine adult emergency department (ED) admission rates for the top 15 most frequently admitted conditions, and assess the relative contribution in admission rate variation attributable to the provider and hospital. METHODS: This was a retrospective, cross-sectional study of ED encounters (≥18years) from 19 EDs and 603 providers (January 2012-December 2013), linked to the Area Health Resources File for county-level information on healthcare resources. "Hospital admission" was the outcome, a composite of inpatient, observation, or intra-hospital transfer. We studied the 15 most commonly admitted conditions, and calculated condition-specific risk-standardized hospital admission rates (RSARs) using multi-level hierarchical generalized linear models. We then decomposed the relative contribution of provider-level and hospital-level variation for each condition. RESULTS: The top 15 conditions made up 34% of encounters and 49% of admissions. After adjustment, the eight conditions with the highest hospital-level variation were: 1) injuries, 2) extremity fracture (except hip fracture), 3) skin infection, 4) lower respiratory disease, 5) asthma/chronic obstructive pulmonary disease (A&C), 6) abdominal pain, 7) fluid/electrolyte disorders, and 8) chest pain. Hospital-level intra-class correlation coefficients (ICC) ranged from 0.042 for A&C to 0.167 for extremity fractures. Provider-level ICCs ranged from 0.026 for abdominal pain to 0.104 for chest pain. Several patient, hospital, and community factors were associated with admission rates, but these varied across conditions. CONCLUSION: For different conditions, there were different contributions to variation at the hospital- and provider-level. These findings deserve consideration when designing interventions to optimize admission decisions and in value-based payment programs.

How (ED) admission decisions differ when the same physician works in two different emergency department.

STUDY OBJECTIVE: Emergency physicians often work in multiple hospital emergency departments (EDs). We study how emergency physician admission decisions vary in different settings. METHODS: We conducted a retrospective, cross-sectional study over two years (2012-3) in six EDs in three states. Included physicians had ≥200 encounters per site in two different EDs. "Admissions" were ED encounters resulting in admission to the hospital or transfer to another hospital. The primary outcome was the adjusted admission rate difference between the two sites. Hierarchical logistic regression analysis was used to calculate adjusted admission rates for each physician, which were then tabulated for each physician and compared across sites. RESULTS: In 51,807 ED encounters seen by 16 physicians the average admission rate was 20.0%, and unadjusted admission rates differed between sites by 2.9% (range 0-8.4%) for the same physician. The adjusted admission rate was 19.3% and differed between sites by 2.1% (range 0.4%-6.2%). CONCLUSION: In this sample, some ED physicians made similar admission decisions in different settings while others increased or decreased their admission rates up to 25% when practicing in a different ED.