Key dimensions for the prevention and control of communicable diseases in institutional settings. a scoping review to guide the development of a tool to strengthen preparedness at migrant holding centres in the EU/EEA

Migrant centres, as other institutions hosting closed or semi-open communities, may face specific challenges in preventing and controlling communicable disease transmission, particularly during times of large sudden influx. However, there is dearth of evidence on how to prioritise investments in aspects such as human resources, medicines and vaccines, sanitation and disinfection, and physical infrastructures to prevent/control communicable disease outbreaks. We analysed frequent drivers of communicable disease transmission/issues for outbreak management in institutions hosting closed or semi-open communities, including migrant centres, and reviewed existing assessment tools to guide the development of a European Centre for Disease Prevention and Control (ECDC) checklist tool to strengthen preparedness against communicable disease outbreaks in migrant centres. Among articles/reports focusing specifically on migrant centres, outbreaks through multiple types of disease transmission were described as possible/occurred. Human resources and physical infrastructure were the dimensions most frequently identified as crucial for preventing and mitigating outbreaks. This review also recognised a lack of common agreed standards to guide and assess preparedness activities in migrant centres, thereby underscoring the need for a capacity-oriented ECDC preparedness checklist tool

Early Warning Software for Emergency Department Crowding

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters' seasonal methods. We showed that the software could predict next hour crowding with a nominal AUC of 0.98 and 24 hour crowding with an AUC of 0.79 using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84.Comment: 15 pages, 6 figure

Large Scale Organizational Intervention to Improve Emergency Department Throughput in a Community Hospital

Abstract BACKGROUND: Emergency Department boarding is a well-documented systemic problem across the country. ED-2b, the time from decision to admit a patient to Emergency Department departure, is specified by the Joint Commission as a quality measure for Emergency Department boarding. ED-2b metrics have been a longstanding challenge at this community hospital outside the nation’s capital. The aim of this study was to reduce median ED-2b times by 10% compared to fiscal year 2020 (FY20). To accomplish the reduction in time, a multidisciplinary throughput committee was developed with subsequent action plans designed to improve Emergency Department throughput. METHODS: The Plan Do Study Act method of quality improvement was used for this project. Several tactics were developed to address a variety of known throughput challenges. Baseline assessment included a review of FY20 ED-2b metrics. These times were used as the comparative pre-intervention data. Literature review queries were conducted to identify tactics to improve hospital throughput. INTERVENTION: A multidisciplinary hospital throughput committee was developed along with a Plan Do Study Act action plan at the beginning of FY21. Improvement tactics included the standardization of workflows for care transitions, compliance with a telemetry discontinuation protocol, implementation of an early warning predictive model for Emergency Department overcrowding, and an inpatient discharge team. In addition, data was collected during the project period comparing bed request to bed assignment, bed assignment to unit arrival, and inpatient discharge order to depart times. Perceptions of the implications associated with Emergency Department boarding were assessed pre and post intervention. RESULTS: Eight months after implementing various tactics, ED-2b metrics were reviewed to assess effectiveness. Comparative data revealed a statistically significant improvement in ED-2b median times. In addition, implementing a discharge team demonstrated a 21% improvement in inpatient discharge departures by 1700. CONCLUSION: Implementing a multidisciplinary throughput committee with engaged participants and leaders, creates a forum for process improvement. By implementing several tactics with key stakeholder, the reduction of Emergency Department boarding time is achievable. Accomplishing frontline engagement supports the success of tactics, improvement of patient satisfaction, and aligns with organizational goal achievement

CARE-PACT: a new paradigm of care for acutely unwell residents in aged care facilities

Describes the Comprehensive Aged Residents Emergency and Partners in Assessment, Care and Treatment (CARE-PACT) program: a hospital substitutive care and demand management project that aims to improve, in a fiscally efficient manner, the quality of care received by residents of aged care facilities. Background Ageing population trends create a strong imperative for healthcare systems to develop models of care that reduce dependence on hospital services. People living in residential aged care facilities (RACFs) currently have high rates of presentation to emergency departments. The care provided in these environments may not optimally satisfy the needs of frail older persons from RACFs.   Objective To describe the Comprehensive Aged Residents Emergency and Partners in Assessment, Care and Treatment (CARE-PACT) program: a hospital substitutive care and demand management project that aims to improve, in a fiscally efficient manner, the quality of care received by residents of aged care facilities when their acute healthcare needs exceed the scope of the aged care facility staff and general practitioners to manage independently of the hospital system.   Discussion The project delivers high-quality gerontic nursing and emergency specialist assessment, collaborative care planning, skills sharing across the care continuum and an individualised, resident-focused approach

Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review

Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes. Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning. Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023. Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects. Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery

Forecasting the Potential for Emergency Department Overcrowding

This research study used the Dixon Forecasting Model (DFM), a Bed Ratio (BR), and the National Emergency Department Overcrowding Scale (NEDOCS) to establish a reliable two-hour overcrowding forecasting tool within the Emergency Department. The DFM and BR were used together to predict severe overcrowding based on current census data. This two hour prediction was then be validated by the real-time NEDOCS and real-time Bed Ratio scores. Data analysis indicates that the two-hour predicted BR is moderately correlated with a real-time NEDOCS (correlation coefficient 0.508) and real-time BR (correlation coefficient 0.492) at the forecasted time. Further data analysis revealed a strong correlation between the real-time NEDOCS and the real-time BR, as evidenced by a correlation coefficient of 0.949. The results of this study suggest that the DFM can be used with additional data to calculate a two hour forecasted BR and that using either BR or NEDOCS in real-time to determine overcrowding is effective

Predicting Emergency Department Volume Using Forecasting Methods to Create a “Surge Response” for Noncrisis Events

Objectives:  This study investigated whether emergency department (ED) variables could be used in mathematical models to predict a future surge in ED volume based on recent levels of use of physician capacity. The models may be used to guide decisions related to on‐call staffing in non–crisis‐related surges of patient volume. Methods:  A retrospective analysis was conducted using information spanning July 2009 through June 2010 from a large urban teaching hospital with a Level I trauma center. A comparison of significance was used to assess the impact of multiple patient‐specific variables on the state of the ED. Physician capacity was modeled based on historical physician treatment capacity and productivity. Binary logistic regression analysis was used to determine the probability that the available physician capacity would be sufficient to treat all patients forecasted to arrive in the next time period. The prediction horizons used were 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Five consecutive months of patient data from July 2010 through November 2010, similar to the data used to generate the models, was used to validate the models. Positive predictive values, Type I and Type II errors, and real‐time accuracy in predicting noncrisis surge events were used to evaluate the forecast accuracy of the models. Results:  The ratio of new patients requiring treatment over total physician capacity (termed the care utilization ratio [CUR]) was deemed a robust predictor of the state of the ED (with a CUR greater than 1 indicating that the physician capacity would not be sufficient to treat all patients forecasted to arrive). Prediction intervals of 30 minutes, 8 hours, and 12 hours performed best of all models analyzed, with deviances of 1.000, 0.951, and 0.864, respectively. A 95% significance was used to validate the models against the July 2010 through November 2010 data set. Positive predictive values ranged from 0.738 to 0.872, true positives ranged from 74% to 94%, and true negatives ranged from 70% to 90% depending on the threshold used to determine the state of the ED with the 30‐minute prediction model. Conclusions:  The CUR is a new and robust indicator of an ED system’s performance. The study was able to model the tradeoff of longer time to response versus shorter but more accurate predictions, by investigating different prediction intervals. Current practice would have been improved by using the proposed models and would have identified the surge in patient volume earlier on noncrisis days.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92015/1/j.1553-2712.2012.01359.x.pd

Ambulance crew-initiated non-conveyance in the Helsinki EMS system-A retrospective cohort study

Background Ambulance patients are usually transported to the hospital in the emergency medical service (EMS) system. The aim of this study was to describe the non-conveyance practice in the Helsinki EMS system and to report mortality following non-conveyance decisions. Methods All prehospital patients >= 16 years attended by the EMS but not transported to a hospital during 2013-2017 were included in the study. EMS mission- and patient-related factors were collected and examined in relation to patient death within 30 days of the EMS non-conveyance decision. Results The EMS performed 324,207 missions with a patient during the study period. The patient was not transported in 95,909 (29.6%) missions; 72,233 missions met the study criteria. The patient mean age (standard deviation) was 59.5 (22.5) years; 55.5% of patients were female. The most common dispatch codes were malaise (15.0%), suspected decline in vital signs (14.0%), and falling over (12.9%). A total of 960 (1.3%) patients died within 30 days after the non-conveyance decision. Multivariate logistic regression analysis revealed that mortality was associated with the patient's inability to walk (odds ratio 3.19, 95% confidence interval 2.67-3.80), ambulance dispatch due to shortness of breath (2.73, 2.27-3.27), decreased level of consciousness (2.72, 1.75-4.10), decreased blood oxygen saturation (2.64, 2.27-3.06), and abnormal systolic blood pressure (2.48, 1.79-3.37). Conclusion One-third of EMS missions did not result in patient transport to the hospital. Thirty-day mortality was 1.3%. Abnormalities in multiple respiratory-related vital signs were associated with an increased likelihood of death within 30 days.Peer reviewe

Forecasting Emergency Department Overcrowding

Objective: To assess the development, use, and implementation of a predictive two-hour forecasting tool for Emergency Department overcrowding. Validate a forecasted Bed Ratio with the National Emergency Department Overcrowding Scale (NEDOCS) and Bed Ratio (BR) to determine accuracy and benefit of use. Methods: This research study utilized tools that identified overcrowding to establish a reliable two-hour forecasting tool within the Emergency Department. It included the use of the Dixon Forecasting Model (DFM), BR, and the NEDOCS. A combination of two tools, the DFM and BR, was utilized to forecast overcrowding based on current census. This two-hour forecast was validated by the NEDOCS and BR, which have been acknowledged in the identification of real-time overcrowding (Jones, 2006). Data Analysis: The two-hour forecasted BR is moderately correlated with the NEDOCS and BR at the forecasted time. This is evidenced by a correlation coefficient of 0.508 with the NEDOCS and a correlation coefficient of 0.492 with the BR. Further data analysis revealed a strong correlation between the NEDOCS and the BR, as evidenced by a correlation coefficient of 0.949. Conclusion: Results of this study suggest that the DFM can be used in combination with the BR to calculate a two-hour forecasted BR. This data would also indicate that using either BR or NEDOCS in real-time to determine overcrowding is effective. One limitation of the study involves criteria set forth for predicted departures in two hours. Creating an automated forecasting tool for Skinner/Forecasting Emergency Department Overcrowding 97 departures, similar to the DFM’s forecasting of arrivals, could prove beneficial