A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy

We present a scalable forecasting framework with a Monte Carlo simulation to forecast the short-term bed occupancy of patients with confirmed and suspected COVID-19 in intensive care units and regular wards. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care from May 2020 to March 2021.

Remdesivir and dexamethasone as tools to relieve hospital care systems stressed by COVID-19: A modelling study on bed resources and budget impact

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.

The Economics of a Bed Shortage: Certificate-of-Need Regulation and Hospital Bed Utilization during the COVID-19 Pandemic

Certificate-of-need (CON) laws are intended to restrain health care spending by limiting the acquisition of duplicative capital and the initiation of unnecessary services. Critics contend that need is difficult to objectively assess, especially considering the risks and uncertainty inherent in health care. We compare statewide bed utilization rates and hospital-level bed utilization rates in bed CON and non-bed CON states during the COVID-19 pandemic. Controlling for other possibly confounding factors, we find that states with bed CONs had 12 percent higher bed utilization rates and 58 percent more days in which more than 70 percent of their beds were used. Individual hospitals in bed CON states were 27 percent more likely to utilize all of their beds. States that relaxed CON requirements to make it easier for hospitals to meet the surge in demand did not experience any statistically significant decreases in bed utilization or number of days above 70 percent of capacity. Nor were hospitals in states that relaxed their CON requirements any less likely to use all their beds. Certificate-of-need laws seem to have exacerbated the risk of running out of beds during the COVID-19 pandemic. State efforts to relax these rules had little immediate effect on reducing this risk.

I Will Never See a Full Moon the Same

I will never see a full moon the same Since the night I stepped out In the dark, looked up to the moon and Heard the cries of a mother who just lost her son. The African moon, so full and so proud, seemed too bright for such a somber night. And my empty hands, which this son once held, Sought to grasp the thought of A young, lifeless body Left lying on that hospital bed. Swaddled by the night’s rich darkness, Full of chants, cries, and pains, I am reminded that Only what’s done for Christ remains. Tonight, as my gaze meets again this African moon, from half a world away, I remember The cries, the lost, this life, The strange peace and the hope that We will meet again. What an oddly beautiful night it was to die. So, take courage, dear heart Don’t fear the night, don’t fear the pain, Rest in His unchanging grace. Go, and be the hands of the only Son who saves.

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

Development of a Motorized Hospital Bed with Swerve Drive Modules for Holonomic Mobility

In hospitals; transferring patients using hospital beds is time consuming and inefficient. Additionally; the task of frequently pushing and pulling beds poses physical injury risks to nurses and caregivers. Motorized hospital beds with holonomic mobility have been previously proposed. However; most such beds come with complex drivetrain which makes them costly and hinders larger-scale adoption in hospitals. In this study; a motorized hospital bed that utilizes a swerve drive mechanism is proposed. The design takes into account simplicity which would allow for minimum modification of the existing beds. Two DC motors for steering and propulsion are used for a single swerve drive module. The control of the propulsion motor is achieved by a combination of trajectory planning based on quintic polynomials and PID control. Further; the control performance of the proposed bed was evaluated; and the holonomic mobility of its prototype was successfully demonstrated. An average error of less than 3% was obtained for motion with a constant velocity; however; larger values in the range of 15% were observed for other conditions, such as accelerating and decelerating.

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

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.