The effects of emergency department crowding on triage and hospital admission decisions

2020 ◽  
Vol 38 (4) ◽  
pp. 774-779 ◽  
Author(s):  
Wanyi Chen ◽  
Benjamin Linthicum ◽  
Nilay Tanik Argon ◽  
Thomas Bohrmann ◽  
Kenneth Lopiano ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Emergency Department Crowding ◽  
Admission Decisions

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

scholarly journals Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ryan P. Strum ◽  
Fabrice I. Mowbray ◽  
Andrew Worster ◽  
Walter Tavares ◽  
Matthew S. Leyenaar ◽  
...  
Keyword(s):  
Emergency Department ◽  
Cohort Study ◽  
Hospital Admission ◽  
Binary Logistic Regression ◽  
Population Based ◽  
Urban Setting ◽  
Bed Management ◽  
Admission Decisions ◽  
Ed Visits ◽  
Mode Of Transportation

Abstract Background Increasing hospitalization rates present unique challenges to manage limited inpatient bed capacity and services. Transport by paramedics to the emergency department (ED) may influence hospital admission decisions independent of patient need/acuity, though this relationship has not been established. We examined whether mode of transportation to the ED was independently associated with hospital admission. Methods We conducted a retrospective cohort study using the National Ambulatory Care Reporting System (NACRS) from April 1, 2015 to March 31, 2020 in Ontario, Canada. We included all adult patients (≥18 years) who received a triage score in the ED and presented via paramedic transport or self-referral (walk-in). Multivariable binary logistic regression was used to determine the association of mode of transportation between hospital admission, after adjusting for important patient and visit characteristics. Results During the study period, 21,764,640 ED visits were eligible for study inclusion. Approximately one-fifth (18.5%) of all ED visits were transported by paramedics. All-cause hospital admission incidence was greater when transported by paramedics (35.0% vs. 7.5%) and with each decreasing Canadian Triage and Acuity Scale level. Paramedic transport was independently associated with hospital admission (OR = 3.76; 95%CI = 3.74–3.77), in addition to higher medical acuity, older age, male sex, greater than two comorbidities, treatment in an urban setting and discharge diagnoses specific to the circulatory or digestive systems. Conclusions Transport by paramedics to an ED was independently associated with hospital admission as the disposition outcome, when compared against self-referred visits. Our findings highlight patient and visit characteristics associated with hospital admission, and can be used to inform proactive healthcare strategizing for in-patient bed management.

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS (Preprint)

2021 ◽  
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

BACKGROUND Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. OBJECTIVE The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. METHODS We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. RESULTS Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. CONCLUSIONS There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

scholarly journals Emergency Department Visits During Outpatient Parenteral Antimicrobial Therapy: A Cohort Study

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S333-S334
Author(s):  
So Lim Kim ◽  
Angela Everett ◽  
Susan J Rehm ◽  
Steven Gordon ◽  
Nabin Shrestha
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Hospital Admission ◽  
Vascular Access ◽  
Antimicrobial Therapy ◽  
Cleveland Clinic ◽  
Emergency Department Visits ◽  
Outpatient Parenteral Antimicrobial Therapy ◽  
Ed Visits ◽  
Access Complications

Abstract Background Outpatient parenteral antimicrobial therapy (OPAT) carries risk of vascular access complications, antimicrobial adverse effects, and worsening of infection. Both OPAT-related and unrelated events may lead to emergency department (ED) visits. The purpose of this study was to describe adverse events that result in ED visits and risk factors associated with ED visits during OPAT. Methods OPAT courses between January 1, 2013 and December 31, 2016 at Cleveland Clinic were identified from the institution’s OPAT registry. ED visits within 30 days of OPAT initiation were reviewed. Reasons and potential risk factors for ED visits were sought in the medical record. Results Among 11,440 OPAT courses during the study period, 603 (5%) were associated with 1 or more ED visits within 30 days of OPAT initiation. Mean patient age was 58 years and 57% were males. 379 ED visits (49%) were OPAT-related; the most common visit reason was vascular access complication, which occurred in 211 (56%) of OPAT-related ED visits. The most common vascular access complications were occlusion and dislodgement, which occurred in 99 and 34 patients (47% and 16% of vascular access complications, respectively). In a multivariable logistic regression model, at least one prior ED visit in the preceding year (prior ED visit) was most strongly associated with one or more ED visits during an OPAT course (OR 2.96, 95% CI 2.38 – 3.71, p-value < 0.001). Other significant factors were younger age (p 0.01), female sex (p 0.01), home county residence (P < 0.001), and having a PICC (p 0.05). 549 ED visits (71%) resulted in discharge from the ED within 24 hours, 18 (2%) left against medical advice, 46 (6%) were observed up to 24 hours, and 150 ED visits (20%) led to hospital admission. Prior ED visit was not associated with hospital admission among patients who visited the ED during OPAT. Conclusion OPAT-related ED visits are most often due to vascular access complications, especially line occlusions. Patients with a prior ED visit in the preceding year have a 3-fold higher odds of at least one ED visit during OPAT compared with patients without a prior ED visit. A strategy of managing occlusions at home and a focus on patients with prior ED visits could potentially prevent a substantial proportion of OPAT-related ED visits. Disclosures All authors: No reported disclosures.

scholarly journals Correction to: Validation of the modified Skåne emergency department assessment of patient load (mSEAL) model for emergency department crowding and comparison with international models; an observational study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jens Wretborn ◽  
Håkan Starkenberg ◽  
Thoralph Ruge ◽  
Daniel B. Wilhelms ◽  
Ulf Ekelund
Keyword(s):  
Emergency Department ◽  
Observational Study ◽  
Emergency Department Crowding ◽  
Patient Load

An amendment to this paper has been published and can be accessed via the original article.

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