scholarly journals Variation in Laboratory Utilization and Correlation with Hospital Bed Utilization: Experience of a Trauma-Care Hospital during the COVID-19 Pandemic

2021 ◽  
Author(s):  
Tapasyapreeti Mukhopadhyay ◽  
Narinder Kumar ◽  
Shivam Pandey ◽  
Arulselvi Subramanian ◽  
Nirupam Madaan ◽  
...  
Keyword(s):  
Laboratory Tests ◽  
P Value ◽  
Occupancy Rate ◽  
Bed Occupancy ◽  
Monthly Average ◽  
Hospital Bed ◽  
Laboratory Utilization ◽  
The Absolute ◽  
Relative Differences ◽  
Test Ordering

Abstract Objectives The present study was planned with the following objectives: (i) to calculate the difference in frequency of laboratory test ordered and use of consumables between the prepandemic and pandemic phases, (ii) to determine and compare the monthly average number of tests ordered per patient between the prepandemic and pandemic phases, and (iii) to correlate the monthly test ordering frequency with the monthly bed occupancy rate in both phases. Materials and Methods Records of laboratory tests ordered and use of consumables were collected for the prepandemic phase (1.8.2019 to 31.3.2020) and the pandemic phase (1.4.2020 to 31.10.2020). The absolute and relative differences were calculated. Monthly average number of tests ordered per patient and bed occupancy rate between prepandemic and pandemic phases was determined, compared, and correlated. Statistical Analysis The absolute and the relative differences between the two periods were calculated. The continuous variables were analyzed between groups using Mann–Whitney U test. Spearman correlation was used to correlate the monthly test ordering frequency with the monthly bed occupancy rate in both phases. Results A total of 946,421 tests were ordered, of which 370,270 (39%) tests were ordered during the pandemic period. There was a decrease in the number of the overall laboratory tests ordered (12%), and in the use of blood collection tubes (34%), and an increase in the consumption of sanitizers (18%), disinfectants (3%), masks (1633%), and gloves (7011%) during the pandemic period. Also, the monthly average number of tests ordered per patients significantly reduced (p-value < 0.001). Test ordering frequency had strong positive correlation with bed occupancy rate during pandemic (Spearman co-efficient = 0.73, p-value = 0.03). Conclusions An overall decline in laboratory utilization during pandemic period was observed. Understanding and correlating the trends with hospital bed utilization can maximize the productivity of the laboratory and help in better preparedness for the challenges imposed during similar exigencies.

scholarly journals Analysis of the Relationship of the Quality of Health Services Review Interest and Bed Occupancy Rate

2021 ◽  
Vol 2 (1) ◽  
pp. 35-42
Author(s):  
Rima Khalila ◽  
Pairi A ◽  
Ginting M
Keyword(s):  
Multivariate Analysis ◽  
Health Services ◽  
Dominant Factor ◽  
P Value ◽  
Occupancy Rate ◽  
Bed Occupancy ◽  
Physical Evidence ◽  
Quality Of Health Services ◽  
The Relationship

The purpose of this study was to determine the relationship between the quality of health services and the interest in repeat visits and to analyze the relationship with the value of the Bed Occupancy Rate (BOR) in the VIP room of the Datu Beru Takengon Regional General Hospital in 2020. The research design used an analytic survey with a cross sectional design. The population of patients treated in the VIP Room of the Datu Beru Takengon Hospital was 105 people. The sampling technique used accidental sampling. The number of samples to be studied was 77 people. Data analysis was performed by univariate, bivariate and multivariate analysis. Base on the results of the research on the reliability variable obtained p-value = 0.001, responsiveness = 0.002, assurance = 0.017, empathy = 0.003, physical evidence = 0.002 <0.05, meaning that there is an influence between reliability, responsiveness, assurance, empathy, physical evidence of interest. revisit and it is related to the Bed Occupancy Rate (BOR). The results of multivariate analysis showed that the most influential variable in this study was the variable of empathy with an Exp (B) value of 12.048. The conclusion is that there is a relationship, reliability, responsiveness, assurance, empathy for re-visit interest and the value of the Bed Occupancy Rate BOR, the results of the multivariate analysis show that the most dominant factor is the variable of empathy.

scholarly journals PHP6 ACUT CARE HOSPITAL BED OCCUPANCY RATE IN HUNGARY BETWEEN 2000 AND 2008

2009 ◽  
Vol 12 (7) ◽  
pp. A238
Author(s):  
M Gresz ◽  
S Varga ◽  
I Kriszbacher ◽  
I Boncz
Keyword(s):  
Care Hospital ◽  
Occupancy Rate ◽  
Bed Occupancy ◽  
Hospital Bed

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.

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

Does the ageing population correctly predict the need for medical beds?: Part one: fundamental principles

2021 ◽  
Vol 27 (8) ◽  
pp. 1-10
Author(s):  
Rodney P Jones
Keyword(s):  
World War Ii ◽  
Capacity Planning ◽  
Baby Boom ◽  
National Average ◽  
Ageing Population ◽  
Bed Occupancy ◽  
World War ◽  
Funding Formula ◽  
Hospital Bed ◽  
The World

The World War II baby boom, coupled with increasing life expectancy, will lead to increasing numbers of deaths for the next 40 years. The last year of life represents a large proportion (55%) of lifetime hospital bed occupancy. This is called the nearness to death effect. However, the nearness to death effect has not been factored into NHS capacity planning, which largely relies on age-based forecasting, often called the ageing population. In certain locations, deaths are predicted to rise far more rapidly than the national average of 1% per annual growth. These locations are highly susceptible to capacity pressures emanating from the nearness to death effect, which is not compatible with recent policies that aim to build smaller hospitals. This article is the first of a two-part series discussing these trends in deaths and bed demand, as well as the likely impact on NHS capacity and the implications for the NHS funding formula.

scholarly journals Quantifying the contribution of established risk factors to cardiovascular mortality differences between Russia and Norway

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sergi Trias-Llimós ◽  
Lisa Pennells ◽  
Aage Tverdal ◽  
Alexander V. Kudryavtsev ◽  
Sofia Malyutina ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Cvd Risk ◽  
Risk Factor Profile ◽  
Emerging Risk ◽  
The Absolute ◽  
Absolute Age ◽  
Relative Differences ◽  
Mortality Rate Ratio ◽  
Cvd Mortality

AbstractSurprisingly few attempts have been made to quantify the simultaneous contribution of well-established risk factors to CVD mortality differences between countries. We aimed to develop and critically appraise an approach to doing so, applying it to the substantial CVD mortality gap between Russia and Norway using survey data in three cities and mortality risks from the Emerging Risk Factor Collaboration. We estimated the absolute and relative differences in CVD mortality at ages 40–69 years between countries attributable to the risk factors, under the counterfactual that the age- and sex-specific risk factor profile in Russia was as in Norway, and vice-versa. Under the counterfactual that Russia had the Norwegian risk factor profile, the absolute age-standardized CVD mortality gap would decline by 33.3% (95% CI 25.1–40.1) among men and 22.1% (10.4–31.3) among women. In relative terms, the mortality rate ratio (Russia/Norway) would decline from 9–10 to 7–8. Under the counterfactual that Norway had the Russian risk factor profile, the mortality gap reduced less. Well-established CVD risk factors account for a third of the male and around a quarter of the female CVD mortality gap between Russia and Norway. However, these estimates are based on widely held epidemiological assumptions that deserve further scrutiny.

Safety and feasibility of a nurse-led ambulatory abdominal paracentesis service.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 63-63
Author(s):  
Kim Hua Lee
Keyword(s):  
Length Of Stay ◽  
Adverse Events ◽  
Average Length ◽  
Symptom Relief ◽  
Bleeding Complications ◽  
Inpatient Setting ◽  
Bed Occupancy ◽  
Abdominal Drain ◽  
Hospital Bed ◽  
Abdominal Paracentesis

63 Background: Ascites is a common complication of cancer. Symptomatic ascites contributes to cancer-related morbidity and is distressing for patients (pts). Therapeutic abdominal paracentesis (TAP) provides symptom relief but requires specialized procedural knowledge and is usually performed in the inpatient setting with several days of hospitalization. Additionally, high hospital bed occupancy during the COVID-19 pandemic prevented timely admission for TAP. An Advanced Practice Nurse (APN)-led ambulatory TAP service was introduced at our center, with the aim of improving access to TAP and reducing hospital bed occupancy. Methods: A multidisciplinary team developed workflows and safety guidelines for TAP to enable right-siting of pts in a cancer day care unit. Pts were scheduled for radiologically guided insertion of abdominal drains in the morning before 10am to allow adequate time for drainage. Pre-procedure clinical examination and safety checks were performed by APNs in the day unit. Following the procedure, abdominal fluid was drained with concurrent administration of 20% IV albumin. Drains were removed by the APN and pts were examined before discharge on the same day. Data for all cancer pts requiring TAP in the day unit and hospital from 1 Jan to 30 Nov 2020 were extracted from the electronic medical record system. The primary outcome was length-of-stay (LOS). The primary safety outcome was adverse events in the day unit. Continuous data were compared using the t-test. Data analysis was done in SPSS version 22. Results: The number of TAPs performed in the day unit and general ward requiring hospitalization were 102 and 133, respectively. There was a significant reduction in average LOS with TAPs performed in the day unit vs. hospitalization (1.48 vs. 5.82 days, p<0.001) (Table). The mean difference was 4.34 (95% confidence interval 3.33 - 5.34) days saved per pt, or a saving of 443 inpatient bed days. The TAP day unit service encountered 10 adverse events (AEs) requiring admission to the ward for continued drainage. AEs were borderline baseline blood pressure, pt frailty and inability to care for an indwelling catheter. There were no infective or bleeding complications. The majority of TAPs (86.8%) were performed in one day, with the remainder over 2-days with the abdominal drain left in-situ and reattendance at the day unit the next day for further drainage. Differences in average length-of-stay with TAP in the hospital vs. day unit. Conclusions: An APN-led ambulatory abdominal paracentesis service is a safe alternative to inpatient paracentesis. Optimal utilization of a day unit enabled reduced LOS for pts with advanced cancer. This reduction in LOS was critical during a pandemic where bed demand was high. This was possible from advanced scheduling and control over the day unit capacity.[Table: see text]

Accuracy of Send-Out Test Ordering: A College of American Pathologists Q-Probes Study of Ordering Accuracy in 97 Clinical Laboratories

2008 ◽  
Vol 132 (2) ◽  
pp. 206-210
Author(s):  
Paul N. Valenstein ◽  
Molly K. Walsh ◽  
Ana K. Stankovic
Keyword(s):  
Laboratory Tests ◽  
Error Rates ◽  
Reference Laboratory ◽  
Patient Evaluation ◽  
Order Entry ◽  
Order Accuracy ◽  
Specific Test ◽  
Clinical Laboratories ◽  
Test Order ◽  
Test Ordering

Abstract Context.—Errors entering orders for send-out laboratory tests into computer systems waste health care resources and can delay patient evaluation and management. Objectives.—To determine (1) the accuracy of send-out test order entry under “real world” conditions and (2) whether any of several practices are associated with improved order accuracy. Design.—Representatives from 97 clinical laboratories provided information about the processes they use to send tests to reference facilities and their order entry and specimen routing error rates. Results.—In aggregate, 98% of send-out tests were correctly ordered and 99.4% of send-out tests were routed to the proper reference laboratory. There was wide variation among laboratories in the rate of send-out test order entry errors. In the bottom fourth of laboratories, more than 5% of send-out tests were ordered incorrectly, while in the top fourth of laboratories fewer than 0.3% of tests were ordered incorrectly. Order entry errors were less frequent when a miscellaneous test code was used than when a specific test code was used (3.9% vs 5.6%; P = .003). Conclusions.—Computer order entry errors for send-out tests occur approximately twice as frequently as order entry errors for other types of tests. Filing more specific test codes in a referring institution's information system is unlikely to reduce order entry errors and may make error rates worse.

Sign in / Sign up

Export Citation Format

Share Document