Hospital Resource Management -A Survey

Healthcare is a limited resource that is constantly in high demand because everyone requires it. When demand exceeds supply, resources become relatively scarce, making the overall resource allocation in healthcare even more difficult, as we have seen at the time of COVID-19. Effective resource allocation faces obstacles such as a lack of trained human resources, inefficient resource use, a lack of focus on improvement, and inefficient resource reallocation. This paper will outline a study of the numerous approaches to resource allocation in healthcare, outlining the methods employed, the outcomes, and benefits and drawbacks of each approach. In order to address any kind of emergency situation that may arise in the future, it was our goal to pinpoint the research gap between the work that had already been done and the solution to this problem through the survey analysis. In order to boost hospital resource management, the paper identifies a variety of potential solutions which can be categorized further into subcategories which can be seen through different perspectives and a range of approaches that can be implemented during COVID-19 or in any other emergency condition. © 2023 IEEE.

COVID-19-associated hospitalizations among children less than 12 years of age in the United States.

OBJECTIVES: To describe the characteristics, healthcare resource use and costs associated with initial hospitalization and readmissions among pediatric patients with COVID-19 in the US. METHODS: Hospitalized pediatric patients, 0-11 years of age, with a primary or secondary discharge diagnosis code for COVID-19 (ICD-10 code U07.1) were selected from 1 April 2020 to 30 September 2021 in the US Premier Healthcare Database Special Release (PHD SR). Patient characteristics, hospital length of stay (LOS), in-hospital mortality, hospital costs, hospital charges, and COVID-19-associated readmission outcomes were evaluated and stratified by age groups (0-4, 5-11), four COVID-19 disease progression states based on intensive care unit (ICU) and invasive mechanical ventilation (IMV) usage, and three sequential calendar periods. Sensitivity analyses were performed using the US HealthVerity claims database and restricting the analyses to the primary discharge code. RESULTS: Among 4,573 hospitalized pediatric patients aged 0-11 years, 68.0% were 0-4 years and 32.0% were 5-11 years, with a mean (median) age of 3.2 (1) years; 56.0% were male, and 67.2% were covered by Medicaid. Among the overall study population, 25.7% had immunocompromised condition(s), 23.1% were admitted to the ICU and 7.3% received IMV. The mean (median) hospital LOS was 4.3 (2) days, hospital costs and charges were $14,760 ($6,164) and $58,418 ($21,622), respectively; in-hospital mortality was 0.5%. LOS, costs, charges, and in-hospital mortality increased with ICU admission and/or IMV usage. In total, 2.1% had a COVID-19-associated readmission. Study outcomes appeared relatively more frequent and/or higher among those 5-11 than those 0-4. Results using the HealthVerity data source were generally consistent with main analyses. LIMITATIONS: This retrospective administrative database analysis relied on coding accuracy and inpatient admissions with validated hospital costs. CONCLUSIONS: These findings underscore that children aged 0-11 years can experience severe COVID-19 illness requiring hospitalization and substantial hospital resource use, further supporting recommendations for COVID-19 vaccination.

The Impact of COVID-19 on Acute Surgical Admissions at the Sunshine Coast University Hospital.

Purpose It has been noted in international literature that acute surgical admissions and number of operations reduced as a result of coronavirus disease2019 (COVID-19). This study assesses the impact of the COVID-19 pandemic on the number of acute surgical admissions, operations, and length of stay (LoS) at the Sunshine Coast University Hospital (SCUH), Queensland, Australia. Methodology A retrospective study was conducted on patients admitted to the Acute Surgical Unit (ASU) during March and April for the years 2018, 2019, and 2020. Admission data for ASU patients in 2018 and 2019 were combined (pre-COVID) and compared with 2020 (COVID) to determine impact of the pandemic on presentations and procedures. Results ASU admissions reduced in 2020 (461 patients) compared with pre-COVID years (mean: 545 patients per year). There was an increase in the number (%) of operations performed in 2020, 175 patients (38%) compared with pre-COVID years, mean 158 patients (29%), p = 0.001. There was a significant decrease in the number (%) of functional presentations in 2020, 29 patients (6.3%) compared with pre-COVID years, mean 105 patients (9.6%), p = 0.04. LoS was not significantly different (52 hours vs. 54 hours, p = 0.11). Conclusion COVID-19 has reduced the absolute number of acute surgical admissions at SCUH. This effectively reduced triage workload. Contrary to the literature, this study did not demonstrate a reduction in the number of operations or change in LoS. These data could be used by health administrators to help with resource allocation during future pandemics.

Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic

Hospitals and health-care institutions need to plan the resources required for handling the increased load, i.e., beds and ventilators during the COVID-19 pandemic. BaBSim.Hospital, an open-source tool for capacity planning based on discrete event simulation, was developed over the last year to support doctors, administrations, health authorities, and crisis teams in Germany. To obtain reliable results, 29 simulation parameters such as durations and probabilities must be specified. While reasonable default values were obtained in detailed discussions with medical professionals, the parameters have to be regularly and automatically optimized based on current data. We investigate how a set of parameters that is tailored to the German health system can be transferred to other regions. Therefore, we use data from the UK. Our study demonstrates the flexibility of the discrete event simulation approach. However, transferring the optimal German parameter settings to the UK situation does not work-parameter ranges must be modified. The adaptation has been shown to reduce simulation error by nearly 70%. The simulation-via-optimization approach is not restricted to health-care institutions, it is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods. © 2021 European Union

Comparing hospital-resource utilization by an enhanced pneumonia surveillance programme for COVID-19 with pre-pandemic pneumonia admissions - a Singaporean hospital's experience.

Introduction. During the early days of coronavirus disease 2019 (COVID-19) in Singapore, Tan Tock Seng Hospital implemented an enhanced pneumonia surveillance (EPS) programme enrolling all patients who were admitted from the Emergency Department (ED) with a diagnosis of pneumonia but not meeting the prevalent COVID-19 suspect case definition.Hypothesis/Gap Statement. There is a paucity of data supporting the implementation of such a programme.Aims. To compare and contrast our hospital-resource utilization of an EPS programme for COVID-19 infection detection with a suitable comparison group.Methodology. We enrolled all patients admitted under the EPS programme from TTSH's ED from 7 February 2020 (date of EPS implementation) to 20 March 2020 (date of study ethics application) inclusive. We designated a comparison cohort over a similar duration the preceding year. Relevant demographic and clinical data were extracted from the electronic medical records.Results. There was a 3.2 times higher incidence of patients with an admitting diagnosis of pneumonia from the ED in the EPS cohort compared to the comparison cohort (P<0.001). However, there was no significant difference in the median length of stay of 7 days (P=0.160). Within the EPS cohort, stroke and fluid overload occur more frequently as alternative primary diagnoses.Conclusions. Our study successfully evaluated our hospital-resource utilization demanded by our EPS programme in relation to an appropriate comparison group. This helps to inform strategic use of hospital resources to meet the needs of both COVID-19 related services and essential 'peace-time' healthcare services concurrently.

The association between various indicators of hospital capacity, age category, and the number of screening tests performed with case fatality rate and recovery rate during the COVID-19 disease pandemic.

BACKGROUND: The COVID-19-related deaths are growing rapidly around the world, especially in Europe and the United States. PURPOSE: In this study we attempt to measure the association of these variables with case fatality rate (CFR) and recovery rate (RR) using up-to-date data from around the world. METHODS: Data were collected from eight global databases. According to the raw data of countries, the CFR and RR and their relationship with different predictors was compared for countries with 1,000 or more cases of COVID-19 confirmed cases. RESULTS: There were no significant correlation between the CFR and number of hospital beds per 1,000 people, proportion of population aged 65 and older ages, and the number of computed tomography per one million inhabitants. Furthermore, based on the continents-based subgroup univariate regression analysis, the population (R2 = 0.37, P = 0.047), GPD (R2 = 0.80, P < 0.001), number of ICU Beds per 100,000 people (R2 = 0.93, P = 0.04), and number of CT per one million inhabitants (R2 = 0.78, P = 0.04) were significantly correlated with CFR in America. Moreover, the income-based subgroups analysis showed that the gross domestic product (R2 = 0.30, P = 0.001), number of ICU Beds per 100,000 people (R2 = 0.23, P = 0.008), and the number of ventilator (R2 = 0.46, P = 0.01) had significant correlation with CFR in high-income countries. CONCLUSIONS: The level of country's preparedness, testing capacity, and health care system capacities also are among the important predictors of both COVID-19 associated mortality and recovery. Thus, providing up-to-date information on the main predictors of COVID-19 associated mortality and recovery will hopefully improve various countries hospital resource allocation, testing capacities, and level of preparedness.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

Anaesthesia and Critical Care Department at a Major Academic Centre's Adaptation to Face the COVID-19 Pandemic.

The new novel coronavirus is having a major impact on healthcare systems internationally. Hospitals are struggling to manage the sudden influx of critical patients. Anaesthesiologists and critical care physicians are front liners in the fight against COVID-19 and carry the highest risk of getting infected. Due to the rapid response of the Saudi government to the WHO's early warning, we were fortunate at our hospital to see a slower rise in COVID-19 cases allowing us some time to prepare. We had to make room for the expected rise in highly infectious and critical patients, while at the same time protecting non-COVID-19 patients, staff and trainees. Additionally, the team continued to provide essential and specialized care to all patients in the hospital and maintain its academic and non-clinical services within the university. This review presents the different protocols, challenges and lessons learned during the development of a COVID-19 anaesthesia and critical care department plan in a major teaching hospital in Jeddah, Saudi Arabia. Our ultimate aim is to share our experience with other similar institutions.

A case study in model failure? COVID-19 daily deaths and ICU bed utilisation predictions in New York state.

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.