Novel indicator for the assessment of hospital bed occupancy

2011 ◽  
Vol 152 (20) ◽  
pp. 797-801 ◽  
Author(s):  
Miklós Gresz
Keyword(s):  
Patient Needs ◽  
Bed Occupancy ◽  
The Past ◽  
Hospital Beds ◽  
Hospital Bed ◽  
Bed Capacity ◽  
Statistical Indicators

In the past decades the bed occupancy of hospitals in Hungary has been calculated from the average of in-patient days and the number of beds during a given period of time. This is the only measure being currently looked at when evaluating the performance of hospitals and changing their bed capacity. The author outlines how limited is the use of this indicator and what other statistical indicators may characterize the occupancy of hospital beds. Since adjustment of capacity to patient needs becomes increasingly important, it is essential to find indicator(s) that can be easily applied in practice and can assist medical personal and funders who do not work with statistics. Author recommends the use of daily bed occupancy as a base for all these statistical indicators. Orv. Hetil., 2011, 152, 797–801.

BED FUND OF THE RUSSIAN FEDERATION: DYNAMICS OF ITS MAIN INDICATORS

2020 ◽  
Vol 5 (4) ◽  
pp. 50-56
Author(s):  
M. Bant'eva ◽  
E. Manoshkina ◽  
Yuriy Mel'nikov
Keyword(s):  
Russian Federation ◽  
Day Care ◽  
Treatment Duration ◽  
Extreme Values ◽  
Bed Occupancy ◽  
Functional Optimization ◽  
Average Treatment ◽  
Hospital Beds ◽  
Hospital Bed ◽  
The Russian Federation

Despite the fact that the process of structural and functional optimization of the hospital bed fund is currently underway, the basis for the provision of medical care remains the assistance provided in around the clock and day time hospitals, which is the most costly because it requires the constant involvement of a large amount of staff, material, technical, financial and other types of resources. The main indicators of the bed fund in around the clock and daily stay hospitals in the Russian Federation, Federal District and regions in dynamics for 2010-2018, as well as mortality in around the clock hospitals, are analyzed using descriptive statistics. In the Russian Federation from 2010 to 2018 the absolute number of hospitals decreased from 5705 to 4323 (by 24.2%), both due to the reduction in the number of hospital beds and in connection with the unification of medical organizations. At the same time, the number of round-the-clock beds decreased from 1250120 to 1044875 (by 16.4%); provision with hospital beds decreased (from 87.5 to 71.1 per 10,000 population - by 18.7%), the average treatment duration (from 12.6 days to 10.7 - by 15.1%) and, unfortunately, average bed occupancy per year (from 325 to 313 days - by 3.7%). In 2018, the extreme values of the indicator of hospitalization rate for 24-hour hospital beds in the regions of the Russian Federation differ 1.8 times, provision with hospital beds - 2.9 times, average bed occupancy per year - 1.2 times, average treatment duration - 1 8 times. The established differences may indicate an imbalance in the ongoing structural transformations. The overall mortality rate in the Russian Federation increased: from 1.5% in 2010 to 1.9% in 2018 (by 28.4%), a similar trend was observed in all regions. The provision of beds in day care hospitals increased from 15.4 per 10,000 in 2010 to 17.0 in 2018 (10.7%). Multidirectional tendencies are noted, both towards increasing and decreasing the number of places in day hospitals, both in the Federal Districts as a whole and in individual regions. The extreme values of the indicator of the provision of places for day care hospitals in the regions of the Russian Federation differ by 30 times, what reflects the disproportionate organization of a network of day care hospitals in the country's regions. During the observation period in the Russian Federation, the level of hospitalization in round-the-clock hospitals decreased from 222.0 to 203.5 per 1000 population (by 8.3%), while the level of hospitalizations in day care hospitals steadily increased from 26.4 to 35.0 per 1,000 (32.8%), what indicates the implementation of the expected hospital-replacing function of day care in the country as a whole. The issue of further structural and functional optimization of the hospital bed fund of the country remains relevant.

scholarly journals Patients with acute stroke are less likely to be admitted directly to a stroke unit when hospital beds are scarce: A Swedish multicenter register study

2017 ◽  
Vol 2 (2) ◽  
pp. 178-186 ◽  
Author(s):  
David Darehed ◽  
Bo Norrving ◽  
Birgitta Stegmayr ◽  
Karin Zingmark ◽  
Mathias C. Blom
Keyword(s):  
Emergency Department ◽  
Confidence Interval ◽  
Acute Stroke ◽  
Odds Ratio ◽  
Stroke Unit ◽  
Register Study ◽  
Bed Occupancy ◽  
Hospital Beds ◽  
Hospital Bed ◽  
Direct Admission

Introduction It is well established that managing patients with acute stroke in dedicated stroke units is associated with improved functioning and survival. The objectives of this study are to investigate whether patients with acute stroke are less likely to be directly admitted to a stroke unit from the Emergency Department when hospital beds are scarce and to measure variation across hospitals in terms of this outcome. Patients and methods This register study comprised data on patients with acute stroke admitted to 14 out of 72 Swedish hospitals in 2011–2014. Data from the Swedish stroke register were linked to administrative daily data on hospital bed occupancy (measured at 6 a.m.). Logistic regression analysis was used to analyse the association between bed occupancy and direct stroke unit admission. Results A total of 13,955 hospital admissions were included; 79.6% were directly admitted to a stroke unit from the Emergency Department. Each percentage increase in hospital bed occupancy was associated with a 1.5% decrease in odds of direct admission to a stroke unit (odds ratio = 0.985, 95% confidence interval = 0.978–0.992). The best-performing hospital exhibited an odds ratio of 3.8 (95% confidence interval = 2.6–5.5) for direct admission to a stroke unit versus the reference hospital. Discussion and conclusion We found an association between hospital crowding and reduced quality of care in acute stroke, portrayed by a lower likelihood of patients being directly admitted to a stroke unit from the Emergency Department. The magnitude of the effect varied considerably across hospitals.

scholarly journals ANALYSIS OF STRUCTURE AND FUNCTIONING OF HOSPITAL BEDS OF EMERGENCY DEPARTMENT IN THE RUSSIAN FEDERATION

2019 ◽  
Vol 20 (3) ◽  
pp. 4-9
Author(s):  
S. F. Bagnenko ◽  
A. G. Miroshnichenko ◽  
R. R. Alimov ◽  
N. V. Razumnyj ◽  
I. A. Turov
Keyword(s):  
Emergency Department ◽  
Russian Federation ◽  
Occupancy Rate ◽  
Short Stay ◽  
Bed Occupancy ◽  
Hospital Beds ◽  
Discharged Patients ◽  
Bed Capacity ◽  
The Russian Federation ◽  
Population Ratio

29 emergency departments (ED) with hospital beds were functioning in 2018 in the Russian Federation (RF). Within the period 2014–2018 the bed capacity has been increased from 415 to 737, which portion is increased from 0,036% to 0,071% (in 2018 the portion of daily bed was 27,3%, the portion of the bed of short stay department was 72,7%). The number of discharged patients has been increased from 94545 to 306757. Therewith the portion of patients referred from ED to specialized units has been decreased from 19,0% to 11,1%. The average annual bed occupancy rate is 276,6 and 274,1. The bed turnover has been increased from 262,6 to 447,2. The lethal index is decreased from 0,18% to 0,13%. Bed population ratio keeps at the low level (5.0 bed per 1 million people which is rated as 4,5% from recommended values). Additional introduction of hospital departments of emergency medicine in 56 federal subjects of the RF al­lows quicker to achieve the goals of the National Project «Health care».  

Cancellation of Scheduled Procedures as a Mechanism to Generate Hospital Bed Surge Capacity—A Pilot Study

2011 ◽  
Vol 26 (3) ◽  
pp. 224-229 ◽  
Author(s):  
Olan A. Soremekun ◽  
Richard D. Zane ◽  
Andrew Walls ◽  
Matthew B. Allen ◽  
Kimberly J. Seefeld ◽  
...  
Keyword(s):  
Pilot Study ◽  
Future Research ◽  
Surge Capacity ◽  
Rater Reliability ◽  
Hospital Bed Capacity ◽  
Hospital Beds ◽  
Hospital Bed ◽  
Bed Capacity ◽  
Categorization Scheme ◽  
The Impact

AbstractBackground: The ability to generate hospital beds in response to a mass-casualty incident is an essential component of public health preparedness. Although many acute care hospitals' emergency response plans include some provision for delaying or canceling elective procedures in the event of an inpatient surge, no standardized method for implementing and quantifying the impact of this strategy exists in the literature. The aim of this study was to develop a methodology to prospectively emergency plan for implementing a strategy of delaying procedures and quantifying the potential impact of this strategy on creating hospital bed capacity.Methods: This is a pilot study. A categorization methodology was devised and applied retrospectively to all scheduled procedures during four one-week periods chosen by convenience. The categorization scheme grouped procedures into four categories: (A) procedures with no impact on inpatient capacity; (B) procedures that could be delayed indefinitely; (C) procedures that could be delayed by one week; and (D) procedures that could not be delayed. The categorization scheme was applied by two research assistants and an emergency medicine resident. All three raters categorized the first 100 cases to allow for calculation of inter-rater reliability. Maximal hospital bed capacity was defined as the 95th percentile weekday occupancy, as this is more representative of functional bed capacity than is the number of licensed beds. The main outcome was the number of hospital beds that could be created by postponing procedures in categories B and C.Results: Maximal hospital bed capacity was 816 beds. Mean occupancy during weekdays was 759 versus 694 on weekends. By postponing Group B and C procedures, a mean of 60 beds (51 general medical/surgical and nine intensive care unit (ICU)) could be created on weekdays, and four beds (three general medical/surgical and one ICU) on weekends. This represents 7.3% and 0.49% of maximal hospital bed capacity and ICU capacity, respectively. In the event that sustained surge is needed, delaying all category B and C procedures for one week would lead to the generation of 1,235 hospital-bed days. Inter-rater reliability was high (kappa = 0.74) indicating good agreement between all three raters.Conclusions: For the institution studied, the strategy of delaying scheduled procedures could generate inpatient capacity with maximal impact during weekdays and little impact on weekends. Future research is needed to validate the categorization scheme and increase the ability to predict inpatient surge capacity across various hospital types and sizes.

scholarly journals Would the United States Have Had Too Few Beds for Universal Emergency Care in the Event of a More Widespread Covid-19 Epidemic?

2020 ◽  
Vol 17 (14) ◽  
pp. 5210 ◽  
Author(s):  
Rodney P Jones
Keyword(s):  
Population Density ◽  
The United States ◽  
Universal Health Care ◽  
Us States ◽  
Hospital Capacity ◽  
Hospital Beds ◽  
Hospital Bed ◽  
Bed Capacity ◽  
Universal Health ◽  
Bed Numbers

(1) Background: To evaluate the level of hospital bed numbers in U.S. states relative to other countries using a new method for evaluating bed numbers, and to determine if this is sufficient for universal health care during a major Covid-19 epidemic in all states (2) Methods: Hospital bed numbers in each state were compared using a new international comparison methodology. Covid-19 deaths per 100 hospital beds were used as a proxy for bed capacity pressures. (3) Results: Hospital bed numbers show large variation between U.S. states and half of the states have equivalent beds to those in developing countries. Relatively low population density in over half of US states appeared to have limited the spread of Covid-19 thus averting a potential major hospital capacity crisis. (4) Conclusions: Many U.S. states had too few beds to cope with a major Covid-19 epidemic, but this was averted by low population density in many states, which seemed to limit the spread of the virus.

scholarly journals Estimating critical care capacity needs and gaps in Africa during the COVID-19 pandemic

2020 ◽  
Author(s):  
Jessica Craig ◽  
Erta Kalanxhi ◽  
Gilbert Osena ◽  
Isabel Frost
Keyword(s):  
Critical Care ◽  
Health Services ◽  
Compartment Model ◽  
African Countries ◽  
Number Of Patients ◽  
Hospital Beds ◽  
Hospital Bed ◽  
Severe Infections ◽  
Bed Capacity ◽  
Care Capacity

AbstractObjectiveThe purpose of this analysis was to describe national critical care capacity shortages for 52 African countries and to outline needs for each country to adequately respond to the COVID-19 pandemic.MethodsA modified SECIR compartment model was used to estimate the number of severe COVID-19 cases at the peak of the outbreak. Projections of the number of hospital beds, ICU beds, and ventilators needed at outbreak peak were generated for four scenarios – if 30, 50, 70, or 100% of patients with severe COVID-19 symptoms seek health services—assuming that all people with severe infections would require hospitalization, that 4.72% would require ICU admission, and that 2.3% would require mechanical ventilation.FindingsAcross the 52 countries included in this analysis, the average number of severe COVID-19 cases projected at outbreak peak was 138 per 100,000 (SD: 9.6). Comparing current national capacities to estimated needs at outbreak peak, we found that 31of 50 countries (62%) do not have a sufficient number of hospital beds per 100,000 people if 100% of patients with severe infections seek out health services and assuming that all hospital beds are empty and available for use by patients with COVID-19. If only 30% of patients seek out health services then 10 of 50 countries (20%) do not have sufficient hospital bed capacity. The average number of ICU beds needed at outbreak peak across the 52 included countries ranged from 2 per 100,000 people (SD: 0.1) when 30% of people with severe COVID-19 infections access health services to 6.5 per 100,000 (SD: 0.5) assuming 100% of people seek out health services. Even if only 30% of severely infected patients seek health services at outbreak peak, then 34 of 48 countries (71%) do not have a sufficient number of ICU beds per 100,000 people to handle projected need. Only four countries (Cabo Verde, Egypt, Gabon, and South Africa) have a sufficient number of ventilators to meet projected national needs if 100% of severely infected individuals seek health services assuming all ventilators are functioning and available for COVID-19 patients, while 35 other countries require two or more additional ventilators per 100,000 people.ConclusionThe majority of countries lack sufficient ICU bed and ventilator capacity to care for the projected number of patients with severe COVID-19 infections at outbreak peak even if only 30% of severely infected patients seek health services.This analysis reveals there is an urgent need to allocate resources and increase critical care capacity in these countries.

HEALTH IN EUROPE

2021 ◽  
Vol 14(63) (1) ◽  
pp. 65-70
Author(s):  
Sanda Constantin
Keyword(s):  
European Union ◽  
Life Expectancy ◽  
Health Facility ◽  
The European Union ◽  
Healthy Life ◽  
The Past ◽  
European Union Level ◽  
Life Expectancy At Birth ◽  
Hospital Beds ◽  
Statistical Indicators

The paper present some aspects about health in Europe taking into consideration the new pandemic context. Some indicators linked with the topic were chosen. The indicators refer to life expectancy at birth, healthy life expectancy at birth and hospital beds per hundred thousand inhabitants as health facility. The information was analysed with statistical indicators. The result shows that the first two analysed indicators have increased in the past years, year by year, except for the last one, which showed a decreasing tendency at the European Union level

Parameters of bed occupancy of intensive care units in Hungary

2011 ◽  
Vol 152 (24) ◽  
pp. 946-950 ◽  
Author(s):  
Miklós Gresz
Keyword(s):  
Health Care ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Health Insurance ◽  
Intensive Care Units ◽  
Emergency Care ◽  
Present Author ◽  
Bed Occupancy ◽  
National Network ◽  
Bed Capacity

According to the Semmelweis Plan for Saving Health Care, ”the capacity of the national network of intensive care units in Hungary is one but not the only bottleneck of emergency care at present”. Author shows on the basis of data reported to the health insurance that not on a single calendar day more than 75% of beds in intensive care units were occupied. There were about 15 to 20 thousand sick days which could be considered unnecessary because patients occupying these beds were discharged to their homes directly from the intensive care unit. The data indicate that on the whole bed capacity is not low, only in some institutions insufficient. Thus, in order to improve emergency care in Hungary, the rearrangement of existing beds, rather than an increase of bed capacity is needed. Orv. Hetil., 2011, 152, 946–950.

An automated model for forecasting non-elective hospital bed demand in the entire English National Health System: retrospective process assessment study (Preprint)

2020 ◽  
Author(s):  
Kanan Shah ◽  
Akarsh Sharma ◽  
Chris Moulton ◽  
Simon Swift ◽  
Clifford Mann ◽  
...  
Keyword(s):  
National Health ◽  
Mean Squared Error ◽  
Winter Season ◽  
National Health System ◽  
Forecast Accuracy ◽  
Occupancy Models ◽  
Bed Occupancy ◽  
Median Error ◽  
Hospital Bed ◽  
Forecasting Techniques

BACKGROUND From 2006/2007 to 2017/2018, there was a 26% increase in emergency department (ED) attendances and 32% increase in total admissions in the National Health Service in England (NHS). Growing demand puts severe strain on hospitals, resulting in bed, nursing, clinical and equipment shortages. Nevertheless, scheduling issues can still result in significant under-utilization of beds. It is imperative to optimize the allocation of existing healthcare resources, including hospital beds. More accurate and reliable long-term hospital bed occupancy rate prediction would help managers plan ahead for their population’s hospital requirements, ultimately resulting in greater efficiencies and better patient care. OBJECTIVE This study aimed to compare widely used automated time series forecasting techniques to predict short-term daily non-elective bed occupancy at all trusts in the NHS. METHODS Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily non-elective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: Exponential Smoothing (ES); Seasonal Autoregressive Integrated Moving Average (SARIMA); Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS). The NHS Modernization Agency’s recommended forecasting method prior to 2020, was also replicated. A comparative analysis of forecast accuracy was conducted by comparing forecasted daily non-elective occupancy with actual non-elective occupancy in the out-of-sample dataset for each week forecasted. Percentage root mean squared error (RMSE) was reported. RESULTS The accuracy of the models varied based on the season during which occupancy was forecasted. For the summer season, percent RMSE values for each model remained relatively stable across six forecasted weeks. However, only the TBATS model (median error 2.45% for six weeks) outperformed the NHS Modernization Agency’s recommended method (median error 2.63% for six weeks). In contrast, during the winter season, percent RMSE values increased as we forecasted further into the future. ES generated the most accurate forecasts (median error 4.91% over four weeks), but all models outperformed the NHS Modernization Agency’s recommended method prior to 2020 (median 8.5% error over four weeks). CONCLUSIONS It is possible to create automated models, similar to those recently published by the NHS, that can be used at a hospital level for a large, national healthcare system in order to predict non-elective bed admissions and thus schedule elective procedures. CLINICALTRIAL N/A

scholarly journals Optimal Control for a COVID-19 Model Accounting for Symptomatic and Asymptomatic

2020 ◽  
Vol 8 (1) ◽  
pp. 168-179
Author(s):  
Jead M. Macalisang ◽  
Mark L. Caay ◽  
Jayrold P. Arcede ◽  
Randy L. Caga-anan
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Medical Care ◽  
Optimal Control Theory ◽  
Comparable Result ◽  
Type Model ◽  
Hospital Beds ◽  
Transmission Reduction ◽  
Bed Capacity ◽  
The Government

AbstractBuilding on an SEIR-type model of COVID-19 where the infecteds are further divided into symptomatic and asymptomatic, a system incorporating the various possible interventions is formulated. Interventions, also referred to as controls, include transmission reduction (e.g., lockdown, social distancing, barrier gestures); testing/isolation on the exposed, symptomatic and asymptomatic compartments; and medical controls such as enhancing patients’ medical care and increasing bed capacity. By considering the government’s capacity, the best strategies for implementing the controls were obtained using optimal control theory. Results show that, if all the controls are to be used, the more able the government is, the more it should implement transmission reduction, testing, and enhancing patients’ medical care without increasing hospital beds. However, if the government finds it very difficult to implement the controls for economic reasons, the best approach is to increase the hospital beds. Moreover, among the testing/isolation controls, testing/isolation in the exposed compartment is the least needed when there is significant transmission reduction control. Surprisingly, when there is no transmission reduction control, testing/isolation in the exposed should be optimal. Testing/isolation in the exposed could seemingly replace the transmission reduction control to yield a comparable result to that when the transmission reduction control is being implemented.

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