Emergency Department Bed Research Articles

How Super Is Supertrack? Expediting Care of Fast-track Patients through a Pediatric Emergency Department.

Fast-track models decrease patient crowding in emergency departments (EDs) by redirecting low-acuity patients to an expedited care pathway. In 2016, this institution's pediatric ED created a fast-track pathway for patients evaluated in a rapid assessment triage area who needed further management in the primary ED. This "Supertrack" designation was intended for patients requiring up to 1 hour of additional care, though means of ensuring these patients were discharged within their anticipated timeframe were lacking. We aimed to increase the percentage of Supertrack patients discharged within 1 hour of their ED bed assignment from 17% to 50%. Interventions included the creation of objective Supertrack criteria, departmental-wide progress reports, personalized reminders, intake huddles, and documentation prompts. We visualized data from Plan, Do, Study, Act (PDSA) cycles with statistical process control charts to determine special cause variation. The percentage of Supertrack patients discharged within their anticipated timeframe increased from 17% to 27% without an increase in return ED visits. The average time Supertrack patients spent in the ED decreased from 121 to 103 minutes. Personalized reminders demonstrated a significant but short-lived improvement. We improved the flow of Supertrack patients by decreasing their length of stay and increasing how many were discharged within their anticipated timeframe. Limitations included an unexpected surge in patients with respiratory complaints and staffing and structural constraints preventing the designation of a discrete Supertrack assessment space with dedicated providers. These findings are helpful for institutions seeking to develop an effective fast-track model with limited space and resources.

Validation of the Emergency Department Work Index in a Pediatric Freestanding Community Emergency Department.

The Emergency Department Work Index (EDWIN) is a validated overcrowding score shown to correlate well with staff assessment of adult emergency department (ED) overcrowding and the potential need for diversion. It derives from the number of staffed ED beds, attending physicians on duty, patients within each triage category, and admitted patients. To date, no study has validated EDWIN in a pediatric community ED setting. We aim to determine if EDWIN correlates with established overcrowding measures and provider perception of overcrowding within a freestanding, community-based pediatric ED. In this prospective observational study at a freestanding, community-based pediatric ED, EDWIN was calculated hourly over 8 weeks throughout the year. EDWIN was compared with other objective and previously established ED metrics of overcrowding, including rates of patients who left without being seen (LWBS), average time from arrival to ED room, average length of stay (LOS), ED occupancy rates, and number of patients in the waiting room. Furthermore, EDWIN was compared with provider perception of overcrowding by surveying providers 6 times a day during the study period using novel, real-time, longitudinal, electronic health record-based survey distribution methodology. Spearman correlation coefficients were calculated to characterize the associations between EDWIN vs provider perception and EDWIN vs ED metrics. ANOVA and Tukey HSD were used to compare means of ED metrics of overcrowding across EDWIN severity categories. Five hundred eleven provider perception survey responses were collected from July 2022 through January 2023. EDWIN directly correlated with all measures of overcrowding, including provider perception of crowdedness (rho = 0.67), LWBS rates (rho = 0.44), average time from arrival to ED room (rho = 0.74), average LOS (rho = 0.70), ED occupancy rates (rho = 0.68), and number of patients in the waiting room (rho = 0.65). All findings were statistically significant (P < 0.05). Our findings suggest that EDWIN is an accurate tool to measure overcrowding in a freestanding, community-based pediatric ED.

Decreased patient discharges on weekends part 1: what do the data tell us?

We believe that hospital and emergency department (ED) crowding is exacerbated on Mondays because fewer in-patients are discharged on the weekend. In part 1 of 3 concurrent studies, we documented the number of weekend discharges and the extent of hospital and ED crowding on the days following weekends. We conducted a data analysis study at The Ottawa Hospital, a major academic health sciences center with two EDs. We created reports of the 18-month period (January 1, 2022-June 30, 2023) regarding the status of in-patients at the two campuses. We compared the total admissions, discharges, and hospital occupancy on weekends (or long weekends), the Monday following weekends (or Tuesday following long weekends), or Tuesdays-Fridays. For these three time periods, we also compared the proportion of ED beds occupied by admitted patients to all ED beds, as well as the proportion of days with > 70% admitted patients housed in the ED at 8:00am. Our data for 55,692 patients demonstrated that on weekends compared to weekdays, there were almost 50% fewer discharges with the ratio of admissions to discharges averaging 1.16 (95% CI 1.10-1.22). This was accompanied by a 2.4% absolute increase (P < 0.001) in hospital occupancy on Mondays or Tuesdays, often exceeding 100%. Both EDs are particularly crowded on these Mondays and Tuesdays with the proportion of admitted patients to regular ED beds averaging 68%. We observed serious crowding with > 70% occupancy with admitted patients on almost 50% of Mondays. We have demonstrated that there are much fewer discharges on weekends, and this is associated with significant hospital and ED crowding on Mondays. This blocks safe and timely access to beds for newly arriving patients in the ED. These results should spur Canadian hospitals to evaluate their own data and seek solutions to this important problem.

Multicenter exploration of tenecteplase transition factors: A quantitative analysis

BackgroundTenecteplase (TNK) is gaining recognition as a novel therapy for acute ischemic stroke (AIS). Despite TNK offering a longer half-life, time and cost saving benefits and comparable treatment and safety profiles to Alteplase (ALT), the adoption of TNK as a treatment for AIS presents challenges for hospital systems. ObjectiveIdentify barriers and facilitators of TNK implementation at acute care hospitals in Texas. MethodsThis prospective survey used open-ended questions and Likert statements generated from content experts and informed by qualitative research. Stroke clinicians and nurses working at 40 different hospitals in Texas were surveyed using a virtual platform. ResultsThe 40 hospitals had a median of 34 (IQR 24.5–49) emergency department beds and 42.5 (IQR 23.5–64.5) inpatient stroke beds with 506.5 (IQR 350–797.5) annual stroke admissions. Fifty percent of the hospitals were Comprehensive Stroke Centers, and 18 (45 %) were solely using ALT for treatment of eligible AIS patients. Primary facilitators to TNK transition were team buy-in and a willingness of stroke physicians, nurses, and pharmacists to adopt TNK. Leading barriers were lack of clinical evidence supporting TNK safety profile inadequate evidence supporting TNK use and a lack of American Heart Association guidelines support for TNK administration in all AIS cases. ConclusionUnderstanding common barriers and facilitators to TNK adoption can assist acute care hospitals deciding to implement TNK as a treatment for AIS. These findings will be used to design a TNK adoption Toolkit, utilizing implementation science techniques, to address identified obstacles and to leverage facilitators.

Rethinking the Healthcare Facilities: The Role of the Buffer Space.

A working group conducted a survey on the use of the principle of buffer space (BS), which in case of emergencies, could benefit healthcare settings. The aim of the preliminary investigation is to define new research lines in hospitals' functional design. The global experience of the COVID-19 pandemic highlighted challenges faced by hospitals when responding promptly to emergencies, including spatial reorganization and suspension of ordinary medical activities for ensuring adequate management of the emergency surge of patients. The group designed questionnaires to be administered to healthcare staff and healthcare designers aimed at understanding varied conceptions and features of BSs. Content across the two surveys overlapped significantly, allowing for direct comparisons of responses, while also including tailored questions in relation to the respective experience and skills of the two groups of respondents. 102 healthcare professionals and 56 designers took part to the survey. Analysis of the responses permitted for initial recommendations regarding BS typology including (a) proximity to the emergency department (ED), intensive care units (ICUs), and inpatient wards (IWs); (b) location within hospitals but separate from other medical areas; (c) need for independent access; (d) organizational and spatial features similar to ED, ICUs, and IWs; (e) existing as a fully flexible operational space; and (f) BS bed capacity to be approximately 12% of ED beds. Although the analysis is related to the Italian context, the expansion of this preliminary research to alternate healthcare facilities and geographic areas is necessary for reaching a wide consensus by different professionals on this field. It serves as a starting point for future investigations regarding the implementation of BS in hospital settings.

Computer Simulation to Assess Emergency Department Length of Stay in Pediatric Traumatic Brain Injury.

Our study aimed to identify how emergency department (ED) arrival rate, process of care, and physical layout can impact ED length of stay (LOS) in pediatric traumatic brain injury care. Process flows and value stream maps were developed for 3 level I pediatric trauma centers. Computer simulation models were also used to examine "what if" scenarios based on ED arrival rates. Differences were observed in prearrival preparation time, ED physical layouts, and time spent on processes. Shorter prearrival preparation time, trauma bed location far from diagnostic or treatment areas, and ED arrival rates that exceed 20 patients/day prolonged ED LOS. This was particularly apparent in 1 center where computer simulation showed that relocation of trauma beds can reduce ED LOS regardless of the number of patients that arrive per day. Exceeding certain threshold ED arrival rates of children with traumatic brain injury can substantially increase pediatric trauma center ED LOS but modifications to ED processes and bed location may mitigate this increase.

An economic evaluation of point-of-care ultrasound for children presenting to the emergency department with suspected septic arthritis of the hip.

Objective Diagnosing septic arthritis of the hip in children is time-sensitive, with earlier diagnosis improving outcomes. Point-of-care ultrasound (POCUS) requires specialised training and resources in emergency departments (ED) to potentially lower costs through reducing patient time in ED. We aimed to compare the costs of using POCUS for suspected septic arthritis of the hip to current practice. Methods This study is embedded in a retrospective review of 190 cases of suspected cases of septic hip joint collected over 5 years to investigate patient length of stay and time to perform ultrasound. We multiplied time use by cost per bed hour comparing current practice with POCUS. The POCUS arm included training and equipment costs. Scenario, sensitivity, and threshold analyses were conducted. Costs were calculated in Australian dollars for 2022. Results The current practice arm took 507 min from initial patient assessment to ultrasound examination, compared with 96 min for the POCUS arm. Cost per bed hour was estimated at $207 from hospital data. Total cost savings for POCUS compared to current practice were $35 821 per year assuming 38 cases of suspected arthritis of the hip per year, saving 228 bed hours per year. All scenario and sensitivity analyses were cost saving. Threshold analysis indicated that if the cost of a paediatric ED bed was higher than $51 per hour, POCUS would be cost saving. Conclusion There was significant cost saving potential for hospitals by switching to POCUS for suspected septic arthritis of the hip.

Ambulance ramping predicts poor mental health of paramedics.

Ambulance ramping involves a patient remaining under paramedic care until a hospital emergency department bed becomes available. This study examined whether negative ramping experiences (verbal abuse, physical abuse, compromised patient care, and patient fatality) contribute to relatively high levels of depression, anxiety, stress, and posttraumatic stress disorder (PTSD) in paramedics. Ninety Australian paramedics (Mage = 37.68, SD = 10.73; 52.2% male) completed an online survey. Path analysis found that negative ramping experiences were positively associated with symptoms of depression, anxiety, stress, and PTSD. Interactions indicated that negative ramping experiences predicted greater depression, stress, and PTSD among paramedics with higher, but not lower, work-related self-efficacy. All interactions with resilience were nonsignificant. These findings suggest that policymakers should aim to reduce ambulance ramping, and that future research could fruitfully investigate the mental health benefits of training programs that include strategies to minimize paramedics' feelings of powerlessness, frustration, and self-blame, during ramping. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Hospital COVID-19 preparedness: Are (were) we ready?

Terrorist attacks and natural disasters such as Hurricanes Katrina and Harvey have increased focus on disaster preparedness planning. Despite the attention on planning, many studies have found that hospitals in the United States are underprepared to manage extended disasters appropriately and the surge in patient volume it might bring. This study aims to profile and examine the availability of hospital capacity specifically related to COVID-19 patients, such as emergency department (ED) beds, intensive care unit (ICU) beds, temporary space setup, and ventilators. A cross-sectional retrospective study design was used to examine secondary data from the 2020 American Hospital Association (AHA) Annual Survey. A series of multivariate logistic analyses were conducted to investigate the strength of association between changes in ED beds, ICU beds, staffed beds, and temporary spaces setup, and the 3,655 hospitals' characteristics. Our results highlight that the odds of a change in ED beds are 44 percent lower for government hospitals and 54 percent for for-profit hospitals than not-for-profit hospitals. The odds of ED bed change for nonteaching hospitals were 34 percent lower compared to teaching hospitals. Small and medium hospitals have significantly lower odds (75 and 51 percent, respectively) than large hospitals. For ICU bed change, staffed bed change, and temporary spaces setup, the conclusions were consistently significant regarding the impact of hospital ownership, teaching status, and hospital size. However, temporary spaces setup differs by hospital location. The odds of change is significantly lower (OR = 0.71) in urban hospitals compared with rural hospitals, while for ED beds, the odds of change is considerably higher (OR = 1.57) in urban hospitals compared to rural hospitals. There is a need for policymakers to consider not only resource limitations that were created from supply line disruptions during the COVID-19 pandemic but also a more global assessment of the adequacy of funding and support for insurance coverage, hospital finance, and how hospitals meet the needs of the populations they serve.

Impact of Care Initiation Model on Emergency Department Orders and Operational Metrics: Cohort Study.

Emergency departments (ED) employ many strategies to address crowding and prolonged wait times. They include front-end Care Initiation and clinician-in-triage models that start the diagnostic and therapeutic process while the patient waits for a care space in the ED. The objective of this study was to quantify the impact of a Care Initiation model on resource utilization and operational metrics in the ED. We performed a retrospective analysis of ED visits at our institution during October 2021. Baseline characteristics were compared with Chi-square and quantile regression. We used t-tests to calculate unadjusted difference in outcome measures, including number of laboratory tests ordered and average time patients spent in the waiting room and the ED treatment room, and the time from arrival until ED disposition. We performed propensity score analysis using matching and inverse probability weighting to quantify the direct impact of Care Initiation on outcome measures. There were 2,407 ED patient encounters, 1,191 (49%) of whom arrived during the hours when Care Initiation was active. A total of 811 (68%) of these patients underwent Care Initiation, while the remainder proceeded directly to the main treatment area. Patients were more likely to undergo Care Initiation if they had lower acuity and lower risk of admission, and if the ED was busier as measured by the number of recent arrivals and percentage of occupied ED beds. After adjusting for patient-specific and department-level covariates, Care Initiation did not increase the number of diagnostic laboratory tests ordered. Care Initiation was associated with increased waiting room time by 1.8 hours and longer time from arrival until disposition by 1.3 hours, but with decreased time in the main treatment area by 0.6 hours, which represents a 15% reduction. Care Initiation was associated with a 15% reduction in time spent in the main ED treatment area but longer waiting room time and longer time until ED disposition without significantly increasing the number of laboratory studies ordered. While previous studies produced similar results with Care Initiation models accessing all diagnostic modalities including imaging, our study demonstrates that a more limited Care Initiation model can still result in operational benefits for EDs.

Access block and overcrowding at the emergency department at Tupua Tamasese Meaole Hospital in Samoa

BackgroundAccess block and overcrowding are known to adversely impact on patient outcomes, service delivery, and patient experiences within emergency departments (ED) worldwide. There are no studies on access block or overcrowding from the Pacific Islands. The aim of the present study is to provide preliminary data on access block and overcrowding in the ED of the national tertiary hospital of Samoa.MethodsMixed methods study design. Data collection was performed in March 2020. The quantitative strand calculated (1) the point prevalence of patients impacted by access block in the ED, and (2) the ED bed occupancy rate to assess for overcrowding. The qualitative strand used thematic analysis of two focus group interviews exploring access block and overcrowding with ED medical and nursing staff members.ResultsOn the day of data collection, a total of 60 patients presented through the ED triage system. Of the 20 patients who were admitted into ED, 80% were triaged as ‘see without delay’ (CAT1), ‘emergency’ (CAT2) or ‘urgent’ (CAT3). For patients requiring admission to hospital wards, 100% waited 4 + h in ED, and 100% waited 8 + h, suggesting the presence of access block. Overcrowding in the ED setting was also evident, with an ED bed occupancy rate of 0.95, and an adjusted bed occupancy rate of 1.43. The major themes emerging from the ED staff focus groups and individual in-depth interviews were (1) the adverse impacts of access block and overcrowding, i.e., violence towards ED staff members, (2) the preventable contributing factors, i.e., lack of physical beds in the ED, and (3) practical recommendations to improve patient flow through the ED, i.e., improved collaboration between ED, outpatient services, and the hospital wards.ConclusionsPreliminary evidence suggested the presence of access block and overcrowding in the ED of the national tertiary hospital of Samoa. ED staff interviews provided insight into the ED frontline challenges and offered practical recommendations for ED health service improvement.

Physician-driven early evaluation: Encounters seen in a vertical model

BackgroundAlthough many emergency departments (EDs) employ some form of vertical patient flow, there is limited published literature describing variations of vertical pathways. ObjectivesWe sought to describe our ED's emergency physician (EP)-driven vertical model and to characterize patients selected by EPs to be seen in our vertical space. MethodsWe retrospectively reviewed all vertical ED encounters in the study period, separately analyzing those who eventually received an ED bed (“ED Bed”) and those who were discharged without being roomed (“Vertical only”). We report patient demographics, ESI, vital signs, oxygen use, chief complaints, resource utilization, ED LOS, disposition, and 72 ​h return rates. ResultsPhysicians elected to perform initial evaluations of a variety of patients in the vertical space. The two most common classes of complaints evaluated in the vertical space were extremity issues (21.4%) and skin complaints (13.5%). Patients presenting with abdominal pain and chest pain initially assessed in vertical were significantly more likely to later receive an ED bed (Standardized Difference of 38% and 21.4% respectively), and patients with skin complaints were more frequently discharged from the vertical space and were less likely to receive an ED bed (Standardized Difference of 32.2%). Most (56.2%) Vertical only patients were ESI 3, although EPs also discharged ESI 2, 4, and 5 patients from Vertical. ConclusionsEP-driven patient selection for a vertical pathway allowed EPs to discharge some patients without bed placement while simultaneously functioning as their own triage physicians for higher-acuity patients who would go on to receive an ED bed.

Hospital COVID-19 preparedness: Are (were) we ready?

Terrorist attacks and natural disasters such as Hurricanes Katrina and Harvey have increased focus on disaster preparedness planning. Despite the attention on planning, many studies have found that hospitals in the United States are underprepared to manage extended disasters appropriately and the surge in patient volume it might bring. This study aims to profile and examine the availability of hospital capacity specifically related to COVID-19 patients, such as emergency department (ED) beds, intensive care unit (ICU) beds, temporary space setup, and ventilators. A cross-sectional retrospective study design was used to examine secondary data from the 2020 American Hospital Association (AHA) Annual Survey. A series of multivariate logistic analyses were conducted to investigate the strength of association between changes in ED beds, ICU beds, staffed beds, and temporary spaces setup, and the 3,655 hospitals' characteristics. Our results highlight that the odds of a change in ED beds are 44 percent lower for government hospitals and 54 percent for for-profit hospitals than not-for-profit hospitals. The odds of ED bed change for nonteaching hospitals were 34 percent lower compared to teaching hospitals. Small and medium hospitals have significantly lower odds (75 and 51 percent, respectively) than large hospitals. For ICU bed change, staffed bed change, and temporary spaces setup, the conclusions were consistently significant regarding the impact of hospital ownership, teaching status, and hospital size. However, temporary spaces setup differs by hospital location. The odds of change is significantly lower (OR = 0.71) in urban hospitals compared with rural hospitals, while for ED beds, the odds of change is considerably higher (OR = 1.57) in urban hospitals compared to rural hospitals. There is a need for policymakers to consider not only resource limitations that were created from supply line disruptions during the COVID-19 pandemic but also a more global assessment of the adequacy of funding and support for insurance coverage, hospital finance, and how hospitals meet the needs of the populations they serve.

If the machine says &amp;quot;Normal&amp;quot; on the ECG, can I put the patient in the waiting room?: a study on the reliability of ECG software classification

Background: Electrocardiograms (ECGs) are one of the most basic and fundamental screening tools used in the emergency department (ED). Previous studies have shown machine diagnosis of ECG to be unreliable, the ECG machine does provide a simpler classification of: 1- Normal, 2- Otherwise normal, 3- Borderline, and 4- Abnormal printed on the ECG. We aim to investigate if machine classification could be used reliably as a screening test for triage. Methods: This cross-sectional study was conducted from 1 to 14 June 2019 of ED at King Abdullah Medical Complex using electronic medical records. The ECGs were put into sets of 25 traces/set and then presented to 21 board-certified emergency medicine attending physicians (EMPs) to assess and decide on one of the actions: Put in the waiting area, see in triage, or admit immediately. The responses were analyzed for inter-subject correlation coefficient kappa (k). Results: Of the 3,149 patients, 452 had ECGs done from which 200 ECGs were chosen at random. The inter-subject correlation coefficient was found to be 0.315 ± 0.187 denoting a fair to moderate correlation. From the ECG traces classified as &amp;quot;Normal&amp;quot; by the ECG machine, only 46% ended up in the waiting room. While almost 15% were admitted immediately to an ED bed. In contrast, 27% of those labeled as &amp;quot;Abnormal&amp;quot; ended up in the waiting room, while 44% were admitted to an ED bed. Conclusion: The machine classification of the ECG traces unfortunately failed remarkably to predict the EMP&amp;apos;s decision. As such, the assessment of the attending EMP remains a necessary and essential part of the assessment.

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient variables, with and without hospital factors. We validated model performance and used the SHapley Additive exPlanation model interpretation. In-hospital cardiac arrest occurred in 5431 of the 1,350,693 patients (0.4%). The extreme gradient boosting model showed the best performance with area under receiver operating curve of 0.9267 when incorporating the hospital factor. Oxygen supply, age, oxygen saturation, systolic blood pressure, the number of ED beds, ED occupancy, and pulse rate were the most influential variables, in that order. ED occupancy and in-hospital cardiac arrest occurrence were positively correlated, and the impact of ED occupancy appeared greater in small hospitals. The machine-learning predictive model using the integrated information acquired in the prehospital stage effectively predicted in-hospital cardiac arrest in the ED and can contribute to the efficient operation of emergency medical systems.

Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department

Accurate and timely documentation of vital signs affects all aspects of triage, diagnosis, and management. The adequacy of current patient monitoring practices and the potential to improve on them are poorly understood. To develop measures of fit between documented and actual patient vital signs throughout the visit, as determined from continuous physiologic monitoring, and to compare the performance of actual practice with alternative patient monitoring strategies. This cross-sectional study evaluated 25 751 adult visits to continuously monitored emergency department (ED) beds between August 1, 2020, and December 31, 2021. A series of monitoring strategies for the documentation of vital signs (heart rate [HR], respiratory rate [RR], oxygen saturation by pulse oximetry [Spo2], mean arterial pressure [MAP]) was developed and the strategies' ability to capture physiologic trends and vital sign abnormalities simulated. Strategies included equal spacing of charting events, charting at variable intervals depending on the last observed values, and discrete optimization of charting events. Coverage was defined as the proportion of monitor-derived vital sign measurements (at 1-minute resolution) that fall within the bounds of nursing-charted values over the course of an ED visit (HR ± 5 beats/min, RR ± 3 breaths/min, Spo2 ± 2%, and MAP ± 6 mm Hg). Capture was defined as the documentation of a vital sign abnormality detected by bedside monitor (tachycardia [HR >100 beats/min], bradycardia [HR <60 beats/min], hypotension [MAP <65 mm Hg], and hypoxia [Spo2 <95%]). Median patient age was 60 years (IQR, 43-75 years), and 13 329 visits (51.8%) were by women. Monitored visits had a median of 4 (IQR, 2-5) vital sign charting events per visit. Compared with actual practice, a simple rule, which observes vital signs more frequently if the last observation fell outside the bounds of the previous values, and using the same number of observations as actual practice, produced relative coverage improvements of 31.5% (95% CI, 30.5%-32.5%) for HR, 31.0% (95% CI, 30.0%-32.0%) for MAP, 16.8% (95% CI, 16.0%-17.6%) for RR, and 7.8% (95% CI, 7.3%-8.3%) for Spo2. The same strategy improved capture of abnormalities by 38.9% (95% CI, 26.8%-52.2%) for tachycardia, 38.1% (95% CI, 29.0%-47.9%) for bradycardia, 39.0% (95% CI, 24.2%-55.7%) for hypotension, and 123.1% (95% CI, 110.7%-136.3%) for hypoxia. Analysis of optimal coverage suggested an additional scope for improvement through more sophisticated strategies. In this cross-sectional study, actual documentation of ED vital signs was variable and incomplete, missing important trends and abnormalities. Alternative monitoring strategies may improve on current practice without increasing the overall frequency of patient monitoring.

Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study

Background: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called “Prediction of the Patient Pathway in the Emergency Department” (3P-U) to predict patient outcomes. Materials: Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH’s ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as “likely to be discharged”, 18,815 (17.8%) were flagged as “likely to be admitted”, and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units. Discussion and conclusions: The 3P-U model’s AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.

Association of Shelter-in-Place Hotels With Health Services Use Among People Experiencing Homelessness During the COVID-19 Pandemic

Some jurisdictions used hotels to provide emergency noncongregate shelter and support services to reduce the risk of COVID-19 infection among people experiencing homelessness (PEH). A subset of these shelter-in-place (SIP) hotel guests were high users of acute health services, and the association of hotel placement with their service use remains unknown. To evaluate the association of SIP hotel placements with health services use among a subset of PEH with prior high acute health service use. This study used a matched retrospective cohort design comparing health services use between PEH with prior high service use who did and did not receive a SIP hotel placement, from April 2020 to April 2021. The setting was 25 SIP hotels in San Francisco, California, with a daily capacity of 2500 people. Participants included PEH who were among the top 10% high users of acute medical, mental health, and substance use services and who had 3 or more emergency department (ED) visits in the 9 months before the implementation of the SIP hotel program. Data analysis for this study was performed from February 2021 to May 2022. SIP hotel placement with on-site supportive services. The primary outcomes were ED visits, hospitalizations and bed days, psychiatric emergency visits, psychiatric hospitalizations, outpatient mental health and substance use visits, and outpatient medical visits. Of 2524 SIP guests with a minimum of 90-day stays, 343 (13.6%) met criteria for high service use. Of 686 participants with high service use (343 SIP group; 343 control), the median (IQR) age was 54 (43-61) years, 485 (70.7%) were male, 283 (41.3%) were Black, and 337 (49.1%) were homeless for more than 10 years. The mean number of ED visits decreased significantly in the high-user SIP group (1.84 visits [95% CI, 1.52-2.17 visits] in the 90 days before SIP placement to 0.82 visits [95% CI, 0.66-0.99 visits] in the 90 days after SIP placement) compared with high-user controls (decrease from 1.33 visits [95% CI, 1.39-1.58 visits] to 1.00 visits [95% CI, 0.80-1.20 visits]) (incidence rate ratio [IRR], 0.60; 95% CI, 0.47-0.75; P < .001). The mean number of hospitalizations decreased significantly from 0.41 (95% CI, 0.30-0.51) to 0.14 (95% CI, 0.09-0.19) for SIP guests vs 0.27 (95% CI, 0.19-0.34) to 0.22 (95% CI, 0.15-0.29) for controls (IRR, 0.41; 95% CI, 0.27-063; P < .001). Inpatient hospital days decreased significantly from a mean of 4.00 (95% CI, 2.44-5.56) to 0.81 (95% CI, 0.40-1.23) for SIP guests vs 2.27 (95% CI, 1.27-3.27) to 1.85 (95% CI, 1.06-2.65) for controls (IRR, 0.25; 95% CI, 0.12-0.54; P < .001), as did psychiatric emergency visits, from a mean of 0.03 (95% CI, 0.01-0.05) to 0.01 (95% CI, 0.00-0.01) visits for SIP guests vs no change in the control group (IRR, 0.25; 95% CI, 0.11-0.51; P < .001). These findings suggest that in a population of PEH with high use of acute health services, SIP hotel placement was associated with significantly reduced acute care use compared with high users without a placement.

The structure of rehospitalization of patients after examination and treatment on emergency department beds during the COVID‑19 pandemic

Objective . The purpose of the study is to analyze the structure of rehospitalization of patients after examination and treatment on emergency department beds during the COVID‑19 pandemic. Materials and methods . In 6365 patients after examination and treatment on emergency department beds further cases of rehospitalization were studied according to the data of the Territorial obligatory health insurance fund. Results . In rehospitalization within 30 days needed 12,1% of patients. The main reasons for rehospitalization were: a new coronavirus infection COVID‑19, worsening of chronic ischaemic heart disease, cholelithiasis, chronic pancreatitis, cirrhosis of the liver. Mortality among the rehospitalized patients was 9,9%. The main causes of deaths were: a new coronavirus infection COVID‑19, chronic ischaemic heart disease, acute cerebrovascular accident, malignant neoplasms. A scale has been developed and tested to predict the risk of death in patients after discharge from the hospital emergency department, proven to have good predictive value (AUC=0,89). Conclusions . It is advisable to continue this study in order to scientifically substantiate the risk factors for rehospitalization of patients after examination and treatment on emergency department beds.

Point-of-care ultrasound-first for the evaluation of small bowel obstruction: National cost savings, length of stay reduction, and preventable radiation exposure.

Computed tomography (CT) has long been the gold standard in diagnosing patients with suspected small bowel obstruction (SBO). Recently, point-of-care ultrasound (POCUS) has demonstrated comparable test characteristics to CT imaging for the diagnosis of SBO. Our primary objective was to estimate the annual national cost saving impact of a POCUS-first approach for the evaluation of SBO. Our secondary objectives were to estimate the reduction in radiation exposure and emergency department (ED) length of stay (LOS). We created and ran 1000 trials of a Monte Carlo simulation. The study population included all patients presenting to the ED with abdominal pain who were diagnosed with SBO. Using this simulation, we modeled the national annual cost savings in averted advanced imaging from a POCUS-first approach for SBO. The model assumes that all patients who require surgery or have non-diagnostic POCUS exams undergo CT imaging. The model also conservatively assumes that a subset of patients with diagnostic POCUS exams undergo additional confirmatory CT imaging. We used the same Monte Carlo model to estimate the reduction in radiation exposure and total ED bed hours saved. A POCUS-first approach for diagnosing SBO was estimated to save a mean (±SD) of $30.1 million (±8.9 million) by avoiding 143,000 (±31,000) CT scans. This resulted in a national cumulative decrease of 507,000 bed hours (±268,000) in ED LOS. The reduction in radiation exposure to patients could potentially prevent 195 (±56) excess annual cancer cases and 98 (±28) excess annual cancer deaths. If adopted widely and used consistently, a POCUS-first algorithm for SBO could yield substantial national cost savings by averting advanced imaging, decreasing ED LOS, and reducing unnecessary radiation exposure in patients. Clinical decision tools are needed to better identify which patients would most benefit from CT imaging for SBO in the ED.