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

Oskvarek, Jonathan J.; Zocchi, Mark S.; Cai, Angela; Venkat, Arvind; Janke, Alexander T.; Venkatesh, Arjun K.; Pines, Jesse M.

doi:10.1016/j.annemergmed.2022.12.011

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…NIRS use in the ED will continue to increase, especially as visits and ED length of stay continue to climb postpandemic. 3 ED management of respiratory failure carries momentum affecting the subsequent hospital course and outcomes for patients with acute respiratory failure. [115][116][117][118] Consequently, it is important for the emergency physician to understand how each NIRS modality works, the goals in each form of respiratory failure, and even more critically how to monitor for, and act on failure.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive respiratory support in the emergency department: Controversies and state‐of‐the‐art recommendations

Mosier,

Tidswell,

Wang

2024

JACEP Open

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Acute respiratory failure is a common reason for emergency department visits and hospital admissions. Diverse underlying physiologic abnormalities lead to unique aspects about the most common causes of acute respiratory failure: acute decompensated heart failure, acute exacerbation of chronic obstructive pulmonary disease, and acute de novo hypoxemic respiratory failure. Noninvasive respiratory support strategies are increasingly used methods to support work of breathing and improve gas exchange abnormalities to improve outcomes relative to conventional oxygen therapy or invasive mechanical ventilation. Noninvasive respiratory support includes noninvasive positive pressure ventilation and nasal high flow, each with unique physiologic mechanisms. This paper will review the physiology of respiratory failure and noninvasive respiratory support modalities and offer data and guideline‐driven recommendations in the context of key clinical controversies.

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Section: Discussionmentioning

confidence: 99%

Noninvasive respiratory support in the emergency department: Controversies and state‐of‐the‐art recommendations

Mosier,

Tidswell,

Wang

2024

JACEP Open

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“…For example, during the initial months following the declaration of the public health emergency, ED census across the nation fell markedly, including in California. 5 , 6 Similarly, COVID had a major impact on SNF and other long‐term care facilities that adopted efforts to reduce disease spread within their facilities such as limiting access and admissions. 7 …”

Section: Discussionmentioning

confidence: 99%

Impact of skilled nursing facility (SNF) 3‐day hospitalization requirement waiver during the COVID‐19 pandemic on emergency department and inpatient SNF discharges in California

Chan,

Brennan,

Castillo

2024

JACEP Open

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ObjectiveWe sought to study the impact of the Centers for Medicare & Medicaid services (CMS) waiver of the 3‐day hospitalization requirement for skilled nursing facility (SNF) care implemented as part of the Federal COVID‐19 response on emergency department (ED) and inpatient hospital SNF discharges.MethodsWe conducted a multicenter retrospective cohort study of hospital ED and inpatient visits in California during 18 months before (prewaiver, September 2018–February 2020) and 18 months after (waiver, March 2020–August 2021) waiver implementation. Data were collected from all adult ED and admitted patients utilizing California Department of Health Care Access and Information datasets from all acute care hospitals licensed in the state. Prewaiver and waiver periods were compared for SNF discharge/disposition rates stratified by patient demographic and hospital data with differences in the proportion and 95% confidence interval [CI] reported (SPSS).ResultsSNF discharges decreased from the prewaiver to waiver periods from the ED (−7.4% [CI −8.1%, −6.6%]), along with larger declines occurring from the inpatient hospital setting (−18.1% [CI −18.4%, −17.9%]). For Medicare beneficiaries, there was a smaller decrease in ED SNF rates (−3.8% [CI −4.7%, −2.9%]), and there was no significant change for SNF discharge rates for inpatient admissions with a length of stay (LOS) <3 days (+1.0% [CI 0.0%, 2.1%]).ConclusionIn California, the CMS waiver did not result in an increase, but an actual decrease rate of SNF discharges from the ED and inpatient setting, though with smaller declines for the ED, Medicare patients, and those with a LOS <3 days.

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“…Oskvarek et al [18] assessed and developed variation in an emergency department (ED) admission intensity measure proposed for value-based payment methods. The measure involves ED diagnosis amenable to evidence-related protocol and where admission decisions change related to physician discretion.…”

Section: Related Workmentioning

confidence: 99%

Artificial Rabbits Optimizer With Machine Learning Based Emergency Department Monitoring and Medical Data Classification at KSA Hospitals

Alharbi

2023

IEEE Access

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The Emergency Departments (EDs) in health centres located in the main areas of Saudi Arabia have heavy patient inflow because of the pandemic, viral infections, and even on some special occasions like Umrah or Hajj, where pilgrims who travel from one place to another with serious disorders. Other than the EDs, it was important to observe the patient's activities from ED to other wards in the region or the hospital to track the spread of viral diseases. In this case, deep learning (DL) and machine learning (ML) methods have been used to track the target audience and classify the data into many classes. With this motivation, this study develops an artificial rabbit optimization with a machine learning-based healthcare data classification (AROML-HDC) technique for EDs. The AROML-HDC technique monitors and tracks the patient visit data, treatment given, and length of stay (LOS). In addition, the AROML-HDC technique designs an effective ARO algorithm for the optimal selection of feature subsets. Next, the class-specific cost regulation extreme learning machine (CSCR-ELM) classifier is applied for effective medical data classification. Finally, the grasshopper optimization algorithm (GOA) was used to adjust the parameters related to the CSCR-ELM classifier. The experimental outcome of the AROML-HDC approach is tested on the benchmark Cleveland dataset and the Statlog dataset comprising 297 and 270 samples, respectively. The simulation results signify the improved performance of the AROML-HDC technique over other recent methods with maximum accuracy of 93.22% and 94.05% Cleveland dataset and the Statlog dataset, respectively.

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

Cited by 4 publications

References 18 publications

Noninvasive respiratory support in the emergency department: Controversies and state‐of‐the‐art recommendations

Noninvasive respiratory support in the emergency department: Controversies and state‐of‐the‐art recommendations

Impact of skilled nursing facility (SNF) 3‐day hospitalization requirement waiver during the COVID‐19 pandemic on emergency department and inpatient SNF discharges in California

Artificial Rabbits Optimizer With Machine Learning Based Emergency Department Monitoring and Medical Data Classification at KSA Hospitals

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