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.

Payment Innovation in Emergency Care: A Case for Global Clinician Budgets.

The fee-for-service funding model for US emergency department (ED) clinician groups is increasingly fragile. Traditional fee-for-service payment systems offer no financial incentives to improve quality, address population health, or make value-based clinical decisions. Fee-for-service also does not support maintaining ED capacity to handle peak demand periods. In fee-for-service, clinicians rely heavily on cross-subsidization, where high reimbursement from commercial payors offsets low reimbursement from government payors and the uninsured. Although fee-for-service survived decades of steady cuts in government reimbursement rates, it is increasingly strained because of visit volatility and the effects of the No Surprises Act, which is driving down commercial reimbursement. Financial pressures on ED clinician groups and higher hospital boarding and clinical workloads are increasing workforce attrition. Here, we propose an alternative model to address some of these fundamental issues: an all-payer-funded, voluntary global budget for ED clinician services. If designed and implemented effectively, the model could support robust clinician staffing over the long term, ensure stability in clinical workload, and potentially improve equity in payments. The model could also be combined with population health programs (eg, pre-ED and post-ED telehealth, frequent ED use programs, and other innovations), offering significant payer returns and addressing quality and value. A linked program could also change hospital incentives that contribute to boarding. Strategies exist to test and refine ED clinician global budgets through existing government programs in Maryland and potentially through state-level legislation as a precursor to broader adoption.

Beyond the Four Walls: The American College of Emergency Physicians 2022 New Practice Models Task Force Report.

Emergency physicians are highly trained to deliver acute unscheduled care. The emergency physician core skillset gained during emergency medicine residency can be applied to many other roles that benefit patients and extend and diversify emergency physician careers. In 2022, the American College of Emergency Physicians (ACEP) convened the New Practice Models Task Force to describe new care models and emergency physician opportunities outside the 4 walls of the emergency department. The Task Force consisted of 21 emergency physicians with broad experience and 2 ACEP staff. Fifty-nine emergency physician roles were identified (21 established clinical roles, 16 emerging clinical roles, 9 established nonclinical roles, and 13 emerging nonclinical roles). A strength-weakness-opportunity-threat (SWOT) analysis was performed for each role. Using the analysis, the Task Force made recommendations for guiding ACEP internal actions, advocacy, education, and research opportunities. Emphasis was placed on urgent care, rural medicine, telehealth/virtual care, mobile integrated health care, home-based services, emergency psychiatry, pain medicine, addiction medicine, and palliative care as roles with high or rising demand that draw on the emergency physician skillset. Advocacy recommendations focused on removing state and federal regulatory and legislative barriers to the expansion of new and emerging roles. Educational recommendations focused on aggregating available resources, developing a centralized resource for career guidance, and new educational content for emerging roles. The Task Force also recommended promoting research on potential advantages (eg, improved outcomes, lower cost) of emergency physicians in certain roles and new care models (eg, emergency physician remote supervision in rural settings).

How artificial intelligence could transform emergency care.

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare applications, including elements of emergency department (ED) care. Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care. For example, AI-assisted symptom checkers could help direct patients to the appropriate setting, models could assist in assigning triage levels, and ambient AI systems could document clinical encounters. AI could also help provide focused summaries of charts, summarize encounters for hand-offs, and create discharge instructions with an appropriate language and reading level. Additional use cases include medical decision making for decision rules, real-time models that predict clinical deterioration or sepsis, and efficient extraction of unstructured data for coding, billing, research, and quality initiatives. We discuss the potential transformative benefits of AI, as well as the concerns regarding its use (e.g. privacy, data accuracy, and the potential for changing the doctor-patient relationship).

Development and Internal Validation of an Emergency Department Admission Intensity Measure Using Data From a National Group.

STUDY OBJECTIVE: We develop and assess variation in an emergency department (ED) admission intensity measure intended for value-based payment models. The measure includes ED diagnoses amenable to evidence-based protocols and where admission decisions vary based on physician discretion. METHODS: Measure International Classification of Diseases (ICD)-10 codes were selected by face validity by 3 emergency physicians using expertise and administrative data. Feedback was sought from a separate technical panel. Using data from a national group (2018 to 2019), we assessed measure stability at the physician and facility level by quarter using descriptive plots, multilevel linear probability models, and intraclass correlation coefficients (ICC). RESULTS: A total of 535 ICD-10 measure codes were selected from 23,590 codes. Across 127 EDs, facility-quarter admission rates averaged 26.1% (95% confidence interval [CI] 24.5 to 27.7). Between- and within-facility standard deviations were 9.2 (95% CI 8.2 to 10.5) and 2.9 (95% CI 2.7 to 3.0), respectively, with an ICC of 0.91. Most ED-quarters (749/961) fell within 2.5% of their facility's average. Among 2,398 physicians, quarterly rates averaged 29.1% (95% CI 28.6 to 29.6). The between- and within-physician standard deviation was 6.3 (95% CI 6.1 to 6.5) and 5.3 (95% CI 5.3 to 5.4), respectively, with an ICC of 0.58; 220 physicians (9.2%) had an admission rate consistently higher than average and 193 (8.0%) consistently lower. CONCLUSION: This set of ICD-10 diagnoses demonstrates face validity and stability for quarterly admission rates at the facility and physician levels. The measure may be useful to monitor facility admission rates in value-based models and reliably identify high and low admitters within facilities to manage admission variation.

Emergency Department Volume, Severity, and Crowding Since the Onset of the Coronavirus Disease 2019 Pandemic.

STUDY OBJECTIVE: We describe emergency department (ED) visit volume, illness severity, and crowding metrics from the onset of the coronavirus disease 2019 (COVID-19) pandemic through mid-2022. METHODS: We tabulated monthly data from 14 million ED visits on ED volumes and measures of illness severity and crowding from March 2020 through August 2022 compared with the same months in 2019 in 111 EDs staffed by a national ED practice group in 18 states. RESULTS: Average monthly ED volumes fell in the early pandemic, partially recovered in 2022, but remained below 2019 levels (915 per ED in 2019 to 826.6 in 2022 for admitted patients; 3,026.9 to 2,478.5 for discharged patients). The proportion of visits assessed as critical care increased from 7.9% in 2019 to 11.0% in 2022, whereas the number of visits decreased (318,802 to 264,350). Visits billed as 99285 (the highest-acuity Evaluation and Management code for noncritical care visits) increased from 35.4% of visits in 2019 to 40.0% in 2022, whereas the number of visits decreased (1,434,454 to 952,422). Median and median of 90th percentile length of stay for admitted patients rose 32% (5.2 to 6.9 hours) and 47% (11.7 to 17.4 hours) in 2022 versus 2019. Patients leaving without treatment rose 86% (2.9% to 5.4%). For admitted psychiatric patients, the 90th percentile length of stay increased from 20 hours to more than 1 day. CONCLUSION: ED visit volumes fell early in the pandemic and have only partly recovered. Despite lower volumes, ED crowding has increased. This issue is magnified in psychiatric patients.

The Cost Shifting Economics of United States Emergency Department Professional Services (2016-2019).

STUDY OBJECTIVE: We estimate the economics of US emergency department (ED) professional services, which is increasingly under strain given the longstanding effect of unreimbursed care, and falling Medicare and commercial payments. METHODS: We used data from the Nationwide Emergency Department Sample (NEDS), Medicare, Medicaid, Health Care Cost Institute, and surveys to estimate national ED clinician revenue and costs from 2016 to 2019. We compare annual revenue and cost for each payor and calculate foregone revenue, the amount clinicians may have collected had uninsured patients had either Medicaid or commercial insurance. RESULTS: In 576.5 million ED visits (2016 to 2019), 12% were uninsured, 24% were Medicare-insured, 32% Medicaid-insured, 28% were commercially insured, and 4% had another insurance source. Annual ED clinician revenue averaged $23.5 billion versus costs of $22.5 billion. In 2019, ED visits covered by commercial insurance generated $14.3 billion in revenues and cost $6.5 billion. Medicare visits generated $5.3 billion and cost $5.7 billion; Medicaid visits generated $3.3 billion and cost $7 billion. Uninsured ED visits generated $0.5 billion and cost $2.9 billion. The average annual foregone revenue for ED clinicians to treat the uninsured was $2.7 billion. CONCLUSION: Large cost-shifting from commercial insurance cross-subsidizes ED professional services for other patients. This includes the Medicaid-insured, Medicare-insured, and uninsured, all of whom incur ED professional service costs that substantially exceed their revenue. Foregone revenue for treating the uninsured relative to what may have been collected if patients had health insurance is substantial.

Opioid Prescription Reduction After Implementation of a Feedback Program in a National Emergency Department Group.

STUDY OBJECTIVE: Reducing excessive opioid prescribing in emergency departments (ED) may prevent opioid addiction. We evaluated the largest personalized feedback and peer comparison intervention to date on emergency clinician opioid prescription rates in a national emergency clinician group. METHODS: This interrupted time series analysis of a quality improvement intervention included data from adults discharged from 102 EDs in 17 states from January 1, 2019, to July 31, 2021. From June 16, 2020, to November 30, 2020, site-level ED directors received emails on local opioid prescription rates. From December 1, 2020, to July 31, 2021, all clinicians were granted electronic dashboard access, which showed prescription rates compared with peers, and national ED leaders sent emails to high-prescribing clinicians and engaged in one-on-one conversations. The primary outcome was opioid prescriptions per 100 discharges. RESULTS: The study included 5,328,288 ED discharges from 924 physicians and 472 advanced practice providers. Opioid prescription rates did not change meaningfully in the site-level director feedback period (mean difference = -0.3, 95% confidence interval [CI] -0.6 to -0.1). During the direct clinician feedback period, opioid prescription rates declined from 10.4 per 100 discharges to 8.4 per 100 discharges (mean difference = -2.0, 95% CI -2.4 to -1.5), a 19% relative reduction. Among prescribers in the highest initial quintile, opioid prescribing reduced by 35% among physicians and 41% among advanced practice providers in the direct feedback period. CONCLUSION: We demonstrated a large, sustained reduction in opioid prescribing by emergency clinicians using direct, personalized feedback to clinicians and an electronic dashboard for peer comparison.

Defining Incidental Versus Non-incidental COVID-19 Hospitalizations.

Background Rates of COVID-19 hospitalization are an important measure of the health system burden of severe COVID-19 disease and have been closely followed throughout the pandemic. The highly transmittable, but often less severe, Omicron COVID-19 variant has led to an increase in hospitalizations with incidental COVID-19 diagnoses where COVID-19 is not the primary reason for admission. There is a strong public health need for a measure that is implementable at low cost with standard electronic health record (EHR) datasets that can separate these incidental hospitalizations from non-incidental hospitalizations where COVID-19 is the primary cause or an important contributor. Two crude metrics are in common use. The first uses in-hospital administration of dexamethasone as a marker of non-incidental COVID-19 hospitalizations. The second, used by the United States (US) CDC, relies on a limited set of COVID-19-related diagnoses (i.e., respiratory failure, pneumonia). Both measures likely undercount non-incidental COVID-19 hospitalizations. We therefore developed an improved EHR-based measure that is better able to capture the full range of COVID-19 hospitalizations. Methods We conducted a retrospective study of ED visit data from a national emergency medicine group from April 2020 to August 2023. We assessed the CDC approach, the dexamethasone-based measure, and alternative approaches that rely on co-diagnoses likely to be related to COVID-19, to determine the proportion of non-incidental COVID-19 hospitalizations. Results Of the 153,325 patients diagnosed with COVID-19 at 112 general EDs in 17 US states, and admitted or transferred, our preferred measure classified 108,243 (70.6%) as non-incidental, compared to 71,066 (46.3%) using the dexamethasone measure and 77,399 (50.5%) using the CDC measure. Conclusions Identifying non-incidental COVID-19 hospitalizations using ED administration of dexamethasone or the CDC measure provides substantially lower estimates than our preferred measure.

Educational Impact of a Structured Radiation Oncology Clerkship Curriculum: An Interinstitutional Comparison.

PURPOSE: Many medical school clerkships have structured curricula; however, most radiation oncology clerkships do not. The Radiation Oncology Education Collaborative Study Group (ROECSG) implemented a curriculum for fourth-year radiation oncology clerkships at 14 institutions. We hypothesized that students completing clerkships with the curriculum would report greater subjective knowledge and comfort to function as a radiation oncology resident compared with students completing clerkships without the curriculum. METHODS: The ROECSG curriculum included three 1-hour lectures and a 1-hour hands-on radiation treatment planning workshop. Applicants to a single radiation oncology residency program in the 2014-2015 academic year were sent an anonymous, validated clerkship experience survey. Students indicated if clerkships were completed at a curriculum site. Likert-type data (1 = not at all, 5 = extremely) are reported as median (interquartile range). RESULTS: Respondents described 276 clerkship experiences, of which 64 (23.2%) were completed at a curriculum site. Students whose first clerkship was at a curriculum site perceived greater postclerkship confidence in knowledge of radiation biology (3 [3-4] versus 2 [2-3], P < .01), treatment setup/positioning (3 [2-3] versus 2 [2-3], P < .05), treatment planning (3 [2-3] versus 2 [2-3], P < .01), and ability to integrate evidence-based medicine into treatment (4 [2-4] versus 3 [2-4], P < .05). Students who completed any clerkship with the curriculum had greater postclerkship confidence to function as a radiation oncology resident (3 [3-4] versus 3 [2-3], P < .05). CONCLUSIONS: These results support the curriculum's ability to increase student knowledge in radiation oncology, especially in the students' first clerkship. Further, these findings suggest that expanded implementation of such curricula may ensure a rewarding educational experience during radiation oncology clerkships.

Imaging of tumor clones with differential liver colonization.

We present a model of hepatic colorectal metastases which represents monoclonal cell lines double-labeled by luciferase and tdTomato. These cells form liver metastasis in varying numbers and patterns similar to those observed in patients. Using in vivo and ex vivo luminescent and fluorescent imaging we determine the growth kinetics and clonogenic frequency of tumor cells colonizing liver. Molecular profiling detected stable expressional differences between clones consistent with their phenotypes. The data indicate that clinically relevant phenotypes of liver metastases can be modeled in vivo.