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Background Determining the optimal number of hospital beds is a complex and challenging endeavor and requires models and techniques which are sensitive to the multi-level, uncertain, and dynamic variables involved. This study identifies and characterizes extant models and methods that can be used to determine the required number of beds at hospital and regional levels, comparing their advantages and challenges. Methods A systematic search was conducted using Web of Science, Scopus, Embase and PubMed databases, with the search terms hospital bed capacity, hospital bed need, hospital, bed size, model, and method. Results Twenty-three studies met the criteria to be included in the review. Of these studies, a total of 11 models and 5 methods were identified, mainly designed to determine hospital bed capacity at the regional level. Common determinants of the required number of hospital beds in these models included demographic changes, average length of stay, admission rates, and bed occupancy rates. Conclusions There are no specific norms for the required number of beds at hospital and regional levels, but some of the identified models and methods may be used to estimate this number in different contexts. Moreover, it is important to consider alternative approaches to planning hospital capacity like care pathways to fix the limitations of “bed numbers”.

Background Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. Conclusions Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

Background Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning. Methods Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15. Results By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50. After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data. Conclusion Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.

Data on best practices for evacuating an intensive care unit (ICU) during a disaster are limited. The impact of Hurricane Sandy on New York City area hospitals provided a unique opportunity to learn from the experience of ICU providers about their preparedness, perspective, roles, and activities. We conducted a cross-sectional survey of nurses, respiratory therapists, and physicians who played direct roles during the Hurricane Sandy ICU evacuations. Sixty-eight health care professionals from 4 evacuating hospitals completed surveys (35% ICU nurses, 21% respiratory therapists, 25% physicians-in-training, and 13% attending physicians). Only 21% had participated in an ICU evacuation drill in the past 2 years and 28% had prior training or real-life experience. Processes were inconsistent for patient prioritization, tracking, transport medications, and transport care. Respondents identified communication (43%) as the key barrier to effective evacuation. The equipment considered most helpful included flashlights (24%), transport sleds (21%), and oxygen tanks and respiratory therapy supplies (19%). An evacuation wish list included walkie-talkies/phones (26%), lighting/electricity (18%), flashlights (10%), and portable ventilators and suction (16%). ICU providers who evacuated critically ill patients during Hurricane Sandy had little prior knowledge of evacuation processes or vertical evacuation experience. The weakest links in the patient evacuation process were communication and the availability of practical tools. Incorporating ICU providers into hospital evacuation planning and training, developing standard evacuation communication processes and tools, and collecting a uniform dataset among all evacuating hospitals could better inform critical care evacuation in the future.

To analyze the elements present in the missions of Brazilian hospitals accredited with an international standard of excellence. Mission elements were obtained from the websites of the Brazilian hospitals accredited with an international standard of excellence. Of eight elements proposed by Pearce II (1982), the most relevant and frequent elements for the mission of hospitals were mentioning products and services and their competition. The studies also identified elements that are required but were not found in any of the missions analyzed, namely, geographical domain, survival, growth or profitability, and concern for public image. It is possible to observe that none of the missions analyzed presented the eight elements proposed by Pearce II (1982) to become efficient. This can affect the purpose of the organization, which is defined through its mission, thus compromising its role as an essential element of strategic planning.

Objectives: Hospital surge capacity in multiple casualty events (MCE) is the core of hospital medical response, and an integral part of the total medical capacity of the community affected. To date, however, there has been no consensus regarding the definition or quantification of hospital surge capacity. The first objective of this study was to quantitatively benchmark the various components of hospital surge capacity pertaining to the care of critically and moderately injured patients in trauma-related MCE. The second objective was to illustrate the applications of those quantitative parameters in local, regional, national, and international disaster planning; in the distribution of patients to various hospitals by prehospital medical services; and in the decision-making process for ambulance diversion. Methods: A 2-step approach was adopted in the methodology of this study. First, an extensive literature search was performed, followed by mathematical modeling. Quantitative studies on hospital surge capacity for trauma injuries were used as the framework for our model. The North Atlantic Treaty Organization triage categories (T1-T4) were used in the modeling process for simplicity purposes. Results: Hospital Acute Care Surge Capacity (HACSC) was defined as the maximum number of critical (T1) and moderate (T2) casualties a hospital can adequately care for per hour, after recruiting all possible additional medical assets. HACSC was modeled to be equal to the number of emergency department beds (#EDB), divided by the emergency department time (EDT); HACSC = #EDB/EDT. In trauma-related MCE, the EDT was quantitatively benchmarked to be 2.5 (hours). Because most of the critical and moderate casualties arrive at hospitals within a 6-hour period requiring admission (by definition), the hospital bed surge capacity must match the HACSC at 6 hours to ensure coordinated care, and it was mathematically benchmarked to be 18% of the staffed hospital bed capacity. Conclusions: Defining and quantitatively benchmarking the different components of hospital surge capacity is vital to hospital preparedness in MCE. Prospective studies of our mathematical model are needed to verify its applicability, generalizability, and validity. (Disaster Med Public Health Preparedness. 2011;5:117–124)

The nursing shortage affects all levels, including the pivotal role of nurse managers, who may find themselves functioning in a complex, stressful work environment. In this increasingly difficult milieu, succession planning for nurse manager turnover is imperative. The authors describe an evidence-based, theoretically driven nurse management internship that allows staff nurses to explore the nurse manager role.

Academics at the Manchester School of Management have undertaken two detailed surveys of BM practice for NW Region 3 and the Greater Manchester Chief Executives Group, which included detailed interviews with BM staff and qualitative comparative analysis of the 12 acute hospitals in Greater Manchester, and study of the available literature on BM. The quantitative analysis is derived from forecasting projects conducted for NW Region, 4 a current project to support bed management with planning tools that are being developed and evaluated at a number of hospitals using action research methodologies, and a joint project with a Greater Manchester hospital to use operations management and operational research analysis techniques to improve capacity management.