Are testers also admitters? Comparing emergency physician resource utilization and admitting practices

Emergency physician resource utilization varies by years of experience

Objectives

One of the most pivotal decisions an emergency physician (EP) makes is whether to admit or discharge a patient. The emergency department (ED) work-up leading to this decision involves several resource-intensive tests. Previous studies have demonstrated significant differences in EP resource utilization, measured by lab tests, advanced imaging (magnetic resonance imaging [MRI], computed tomography [CT], ultrasound), consultations, and propensity to admit a patient. However, how an EP's years of experience may impact their resource utilization and propensity to admit patients has not been well characterized. This study seeks to better understand how EPs' years of experience, post-residency, relates to their use of advanced imaging and patient disposition.

Methods

Ten years of ED visits were analyzed for this study from a single, academic tertiary care center in the urban Northeast United States. The primary outcomes were utilization of advanced imaging during the visit (CT, MRI, or formal ultrasound) and whether the patient was admitted. EP years of experience was categorized into 0-2 years, 3-5 years, 6-8 years, 9-11 years, and 12 or more years. Patient age, sex, Emergency Severity Index (ESI), and the attending EP's years of experience were collected. The relationship between EP years of experience and each outcome was assessed with a linear mixed model with a random effect for provider and patient age, sex, and ESI as covariates.

Results

A total of 460,937 visits seen by 65 EPs were included in the study. Over one-third (37.6%) of visits had an advanced imaging study ordered and nearly half (49.5%) resulted in admission. Compared to visits with EPs with 0-2 years of experience, visits with EPs with 3-5 or 6-8 years of experience had significantly lower odds of advanced imaging occurring. Visits seen by EPs with more than 2 years of experience had lower odds of admission than visits by EPs with 0-2 years of experience.

Conclusion

More junior EPs tend to order more advanced imaging studies and have a higher propensity to admit patients. This may be due to less comfort in decision-making without advanced imaging or a lower risk tolerance. Conversely, the additional clinical experience of the most senior EPs, with greater than 9 years of experience, likely impacts their resource utilization patterns such that their use of advanced imaging does not significantly differ from the most junior EPs.

Practice as you Teach: Comparing Ordering Practices Between Shared and Physician-Only Visits in Academically Affiliated Community Emergency Departments

BACKGROUND

Considerable variability exists in emergency physicians' (EPs) rates of resource utilization, which may cluster in distinct patterns. However, previous studies have focused on academic and tertiary care centers, and it is unclear whether similar patterns exist in community practice.

OBJECTIVE

Our aim was to examine whether EPs practicing in community emergency departments (EDs) have practice patterns similar to those of academic EDs. Secondarily, we sought to investigate the effects of shared visits with advanced practice professionals and residents.

METHODS

This was a retrospective study of two community EDs affiliated with an academic network. There were 62,860 visits among 50 EPs analyzed from October 1, 2018 through January 31, 2020 for rates of advanced imaging, admission, and shared visits. To classify practice patterns, we used a Gaussian Mixture Model (GMM), with groups and covariance determined by Bayesian Information Criteria.

RESULTS

Our GMM revealed three groups. The largest had homogeneous patterns of resource use (n = 28; 50% were female; years of experience: 7; interquartile range [IQR] 2-11; advanced imaging: 28%; admission: 19%; shared: 34%), a small group with lower resource use (n = 4; 0% were female; years of experience: 6; IQR 4-10; advanced imaging: 28%; admission: 16%; shared: 8%), and a modest high-resource group (n = 18; 28% female; years of experience: 5; IQR 2-16; advanced imaging: 34%; admission: 23%; shared: 43%). Rates of shared visits had little direct correlation with imaging (r2 = 0.045) or admission (r2 = 0.093), and rates of imaging and admission were weakly correlated (r2 = 0.242).

CONCLUSIONS

Our data suggest that community EPs may have multiple patterns of resource use, similar to those in academic EDs.

Correlations among common emergency medicine physician performance measures: Mixed messages or balancing forces?

The increasing complexity of ED physician performance measures has resulted in significant challenges, including duplicative and conflicting measures that fail to account for different ED settings. We performed a cross sectional analysis of correlations between measures to characterize their relationships and determine if differences exist between academic versus non-academic ED settings. Pearson correlations were calculated for 12 measures among 220 ED physicians at 11 EDs. Higher admission rate was strongly correlated with higher CT utilization rate (R = 0.7, p < 0.01) and longer room to discharge time (R = 0.7, p < 0.01). Higher patients per hour was strongly correlated with shorter room to doctor time (R = -0.7, p < 0.01). Stronger measure correlations were found in the academic setting compared to the non-academic setting. Strong correlations between ED measures imply opportunities to reduce competing performance demands on clinicians. Differences in correlations at academic versus non-academic settings suggest that it may be inappropriate to apply the same performance standards across settings.

Does What Happens in the ED Stay in the ED? The Effects of Emergency Department Physician Workload on Post-ED Care Use

PROBLEM DEFINITION

Emergency department (ED) crowding has been a pressing concern in healthcare systems in the U.S. and other developed countries. As such, many researchers have studied its effects on outcomes within the ED. In contrast, we study the effects of ED crowding on system performance outside the ED-specifically, on post-ED care utilization. Further, we explore the mediating effects of care intensity in the ED on post-ED care use.

METHODOLOGY/RESULTS

We utilize a dataset assembled from more than four years of microdata from a large U.S. hospital and exhaustive billing data in an integrated health system. By using count models and instrumental variable analyses to answer the proposed research questions, we find that there is an increasing concave relationship between ED physician workload and post-ED care use. When ED workload increases from its 5th percentile to the median, the number of post-discharge care events (i.e., medical services) for patients who are discharged home from the ED increases by 5% and it is stable afterwards. Further, we identify physician test-ordering behavior as a mechanism for this effect: when the physician is busier, she responds by ordering more tests for less severe patients. We document that this "extra" testing generates "extra" post-ED care utilization for these patients.

MANAGERIAL IMPLICATIONS

This paper contributes new insights on how physician and patient behaviors under ED crowding impact a previously unstudied system performance measure: post-ED care utilization. Our findings suggest that prior studies estimating the cost of ED crowding underestimate the true effect, as they do not consider the "extra" post-ED care utilization.

Not All Testers are Admitters: An Analysis of Emergency Physician Resource Utilization and Consultation Rates

BACKGROUND

Variability exists in emergency physician (EP) resource utilization as measured by ordering practices, rate of consultation, and propensity to admit patients.

OBJECTIVE

To validate and expand upon previous data showing that resource utilization as measured by EP ordering patterns is positively correlated with admission rates.

METHODS

This is a retrospective study of routinely gathered operational data from the ED of an urban academic tertiary care hospital. We collected individual EP data on advanced imaging, consultation, and admission rates per patient encounter. To investigate whether there might be distinct groups of practice patterns relating these 3 resources, we used a Gaussian mixture model, a classification method used to determine the likelihood of distinct subgroups within a larger population.

RESULTS

Our Gaussian mixture model revealed 3 distinct groups of EPs based on their ordering practices. The largest group is characterized by a homogenous pattern of neither high or low resource utilization (n = 37, 27% female, median years' experience: 6 [interquartile ratio {IQR} 3-18]; rates of advanced imaging, 38.9%; consultation, 45.1%; and admission 39.3%), with a modest group of low-resource users (n = 15, 60% female, median years' experience: 6 [IQR 5-14]; rates of advanced imaging, 37%; consultation, 42.6%; and admission 37.3%), and far fewer members of a high-resource use group (n = 6, 0% female, median years' experience: 6 [IQR 4-16]; rates of advanced imaging, 42.2%; consultation, 45.8%; and admission 40.6%). This variation suggests that not "all testers are admitters," but that there exist wider practice variations among EPs.

CONCLUSIONS

At our academic tertiary center, 3 distinct subgroups of EP ordering practices exist based on consultation rates, advanced imaging use, and propensity to admit a patient. These data validate previous work showing that resource utilization and admission rates are related, while demonstrating that more nuanced patterns of EP ordering practices exist. Further investigation is needed to understand the impact of EP characteristics and behavior on throughput and quality of care. © 2022 Elsevier Inc.

Peer Discussion Decreases Practice Intensity and Increases Certainty in Clinical Decision-Making Among Internal Medicine Residents

BACKGROUND

Team-based decision-making has been shown to reduce diagnostic error, increase clinical certainty, and decrease adverse events.

OBJECTIVE

This study aimed to assess the effect of peer discussion on resident practice intensity (PI) and clinical certainty (CC).

METHODS

A vignette-based instrument was adapted to measure PI, defined as the likelihood of ordering additional diagnostic tests, consultations or empiric treatment, and CC. Internal medicine residents at 7 programs in the Philadelphia area from April 2018 to June 2019 were eligible for inclusion in the study. Participants formed groups and completed each item of the instrument individually and as a group with time for peer discussion in between individual and group responses. Predicted group PI and CC scores were compared with measured group PI and CC scores, respectively, using paired t testing.

RESULTS

Sixty-nine groups participated in the study (response rate 34%, average group size 2.88). The measured group PI score (2.29, SD = 0.23) was significantly lower than the predicted group PI score (2.33, SD = 0.22) with a mean difference of 0.04 (SD = 0.10; 95% CI 0.02-0.07; P = .0002). The measured group CC score (0.493, SD = 0.164) was significantly higher than the predicted group CC score (0.475, SD = 0.136) with a mean difference of 0.018 (SD = 0.073; 95% CI 0.0006-0.0356; P = .022).

CONCLUSIONS

In this multicenter study of resident PI, peer discussion reduced PI and increased CC more than would be expected from averaging group members' individual scores.

Risk factors associated with hospital admission in COVID-19 patients initially admitted to an observation unit

Background

No set guidelines to guide disposition decisions from the emergency department (ED) in patients with COVID-19 exist. Our goal was to determine characteristics that identify patients at high risk for adverse outcomes who may need admission to the hospital instead of an observation unit.

Methods

We retrospectively enrolled 116 adult patients with COVID-19 admitted to an ED observation unit. We included patients with bilateral infiltrates on chest imaging, COVID-19 testing performed, and/or COVID-19 suspected as the primary diagnosis. The primary outcome was hospital admission. We assessed risk factors associated with this outcome using univariate and multivariable logistic regression.

Results

Of 116 patients, 33 or 28% (95% confidence interval [CI] 20–37%) required admission from the observation unit. On multivariable logistic regression analysis, we found that hypoxia defined as room-air oxygen saturation < 95% (OR 3.11, CI 1.23–7.88) and bilateral infiltrates on chest radiography (OR 5.57, CI 1.66–18.96) were independently associated with hospital admission, after adjusting for age. Two three-factor composite predictor models, age > 48 years, bilateral infiltrates, hypoxia, and Hispanic race, bilateral infiltrates, hypoxia yield an OR for admission of 4.99 (CI 1.50–16.65) with an AUC of 0.59 (CI 0.51–0.67) and 6.78 (CI 2.11–21.85) with an AUC of 0.62 (CI 0.54–0.71), respectively.

Conclusions

Over 1/4 of suspected COVID-19 patients admitted to an ED observation unit ultimately required admission to the hospital. Risk factors associated with admission include hypoxia, bilateral infiltrates on chest radiography, or the combination of these two factors plus either age > 48 years or Hispanic race.

Emergency Department Operations: An Overview

Emergency department (ED) operations reflect the intersection of factors external and internal to the ED itself, with unique problems posed by community and academic environments. ED crowding is primarily caused by a lack of inpatient beds for patients admitted through the ED. Changes to front-end operations, such as point-of-care testing and putting physicians in triage, can yield benefits in throughput, but require individual cost analyses. Balancing physician workloads can lead to substantial improvements in throughput. Observation pathways can reduce crowding while maintaining safety. Physician and nurse well-being is an underappreciated topic within operations, and demands close attention and further research.

Patient Assignment Models in the Emergency Department

Early assignment of patients to specific treatment teams improves length of stay, rate of patients leaving without being seen, patient satisfaction, and resident education. Multiple variations of patient assignment systems exist, including provider-in-triage/team triage, fast-tracks/vertical pathways, and rotational patient assignment. The authors discuss the theory behind patient assignment systems and review potential benefits of specific models of patient assignment found in the current literature.

Resource Utilization in Non-Academic Emergency Departments with Advanced Practice Providers

INTRODUCTION

Advanced practice providers (APP), including physicians' assistants and nurse practitioners, have been increasingly incorporated into emergency department (ED) staffing over the past decade. There is scant literature examining resource utilization and the cost benefit of having APPs in the ED. The objectives of this study were to compare resource utilization in EDs that use APPs in their staffing model with those that do not and to estimate costs associated with the utilized resources.

METHODS

In this five-year retrospective secondary data analysis of the Emergency Department Benchmarking Alliance (EDBA), we compared resource utilization rates in EDs with and without APPs in non-academic EDs. Primary outcomes were hospital admission and use of computed tomography (CT), radiography, ultrasound, and magnetic resonance imaging (MRI). Costs were estimated using the 2014 physician fee schedule and inpatient payments from the Centers for Medicare and Medicaid Services. We measured outcomes as rates per 100 visits. Data were analyzed using a mixed linear model with repeated measures, adjusted for annual volume, patient acuity, and attending hours. We used the adjusted net difference to project utilization costs between the two groups per 1000 visits.

RESULTS

Of the 1054 EDs included in this study, 79% employed APPs. Relative to EDs without APPs, EDs staffing APPs had higher resource utilization rates (use per 100 visits): 3.0 more admissions (95% confidence interval [CI], 2.0-4.1), 1.7 more CTs (95% CI, 0.2-3.1), 4.5 more radiographs (95% CI, 2.2-6.9), and 1.0 more ultrasound (95% CI, 0.3-1.7) but comparable MRI use 0.1 (95% CI, -0.2-0.3). Projected costs of these differences varied among the resource utilized. Compared to EDs without APPs, EDs with APPs were estimated to have 30.4 more admissions per 1000 visits, which could accrue $414,717 in utilization costs.

CONCLUSION

EDs staffing APPs were associated with modest increases in resource utilization as measured by admissions and imaging studies.