Development of an Index to Assess COVID-19 Hospital Care Installed Capacity in the 450 Brazilian Health Regions.

OBJECTIVE: The article seeks to assess the Brazilian health system ability to respond to the challenges imposed by the coronavirus disease 2019 (COVID-19) pandemic by measuring the capacity of Brazilian hospitals to care for COVID-19 cases in the 450 Health Regions of the country during the year 2020. Hospital capacity refers to the availability of hospital beds, equipment, and human resources. METHODS: We used longitudinal data from the National Register of Health Facilities (CNES) regarding the availability of resources necessary to care for patients with COVID-19 in inpatient facilities (public or private) from January to December 2020. Among the assessed resources are health professionals (certified nursing assistants, nurses, physical therapists, and doctors), hospital beds (clinical, intermediate care, and intensive care units), and medical equipment (computed tomography scanners, defibrillators, electrocardiograph monitors, ventilators, and resuscitators). In addition to conducting a descriptive analysis of absolute and relative data (per 10,000 users), a synthetic indicator named Installed Capacity Index (ICI) was calculated using the multivariate principal component analysis technique to assess hospital capacity. The indicator was further stratified into value ranges to understand its evolution. RESULTS: There was an increase in all selected indicators between January and December 2020. It was possible to observe differences between the Northeast and North regions and the other regions of the country; most Health Regions presented low ICI. The ICI increased between the beginning and the end of 2020, but this evolution differed among Health Regions. The average increase in the ICI was more evident in the groups that already had considerably high baseline capacity in January 2020. CONCLUSIONS: It was possible to identify inequalities in the hospital capacity to care for patients affected by COVID -19 in the Health Regions of Brazil, with a concentration of low index values in the Northeast and North of the country. As the indicator increased throughout the year 2020, inequalities were also observed. The information here provided may be used by health authorities, providers, and managers in planning and adjusting for future COVID-19 care and in dimensioning the adequate supply of hospital beds, health-care professionals, and devices in Health Regions to reduce associated morbidity and mortality. We recommend that the ICI continue to be calculated in the coming months of the pandemic to monitor the capacity in the country's Health Regions.

Managing pandemics: A POM perspective and directions for future research

COVID-19 pandemic has revealed how unprepared operations and supply chain professionals are for "abnormal” conditions. Understanding how the production and operations management field can affect the trajectory and especially the remediation of pandemics is a critical, but understudied, area of research from descriptive, predictive, and prescriptive perspectives. Fourteen research articles in this special issue have attempted to fill this gap with rigor. We first summarize these articles in six categories, (1) public policies and government interventions, (2) hospital capacity, (3) propagation of pandemics, (4) humanitarian operations, (5) private partnerships, and (6) vaccine production, by drawing out the themes addressed. As we look ahead at pandemics yet to come, we note there is still much research needed and conclude by discussing emergent interest in promising themes for studying pre-pandemic, during pandemic, and post-pandemic operations. © 2023 Production and Operations Management Society.

A flatter curve affords hospitals greater time to prepare for a pandemic surge

This paper quantifies the benefits of flattening the curve (with a constant total patient load over the study period) on the risk of a hospital bed shortage in a pandemic. Using discrete-event simulation of patient care paths in hospitals, synthetic data that eliminates issues of confounding affects from the simultaneous occurrence of regional response actions and/or changes in resources, treatments or other situational circumstances, is produced for estimating hospital capacity for pandemic response. Results from systematically designed numerical experiments produced several findings. These include that the higher the acceleration in pandemic patient demand growth, the greater the impact of the intervention. Cutting this acceleration by 75% from the greatest studied rate created over four additional weeks to prepare for an 80% risk of running out of intensive care beds. Additionally, the greater the acceleration in growth, the fewer the days with a high risk of running out of beds, but the greater the total number of critical patients that could not be served with existing resources. Finally, the lower this acceleration, the fewer resources or modifications needed to cope with the surge, but the longer they are needed. The findings further show how hospitals can benefit from analytical tools that exploit digital health information to predict and plan for need levels and time to onset of these levels. These tools can be embedded within a real-time framework in which automated and early warnings can inform the selection of strategies for managing or coping with expected increases in demand for emergency hospital services. © 2022 The Author(s)

COVID-19 pandemic: the impact on Canada's intensive care units

The COVID-19 pandemic has exposed the precarious demand-capacity balance in Canadian hospitals, including critical care where there is an urgent need for trained health care professionals to dramatically increase ICU capacity. The impact of the pandemic on ICUs varied significantly across the country with provinces that implemented public health measures later and relaxed them sooner being impacted more severely. Pediatric ICUs routinely admitted adult patients. Non-ICU areas were converted to ICUs and staff were redeployed from other essential service areas. Faced with a lack of critical care capacity, triage plans for ICU admission were developed and nearly implemented in some provinces. Twenty eight percent of patients in Canadian ICUs who required mechanical ventilation died. Surviving patients have required prolonged ICU admission, hospitalization and extensive ongoing rehabilitation. Family members of patients were not permitted to visit, resulting in additional psychological stresses to patients, families, and healthcare teams. ICU professionals also experienced extreme psychological stresses from caring for such large numbers of critically ill patients, often in sub-standard conditions. This resulted in large numbers of health workers leaving their professions. This pandemic is not yet over, and it is likely that new pandemics will follow. A review and recommendations for the future are provided. © 2022 Gibney et al.

Has vaccination alleviated the strain on hospitals due to COVID-19? A combined difference-in-difference and simulation approach.

BACKGROUND: Serious measures, including mass vaccination, have been taken to ensure sufficient hospital capacity during the COVID-19 pandemic. Due to high hospitalization risk in the oldest age groups, most countries prioritized elderly for vaccines. The aim of this study is to broaden the understanding of how vaccination in younger age groups relieved the strain on hospitals during the pandemic. METHODS: To determine the impact of vaccination on hospitalization, we relied on individual level data on health care use and vaccination from the Norwegian Emergency Preparedness Register Beredt C19. Using a pre-post design, we estimated the increase in hospitalization days from before to after confirmed COVID-19 for individuals aged 18-64 who were fully vaccinated (N=2 419) or unvaccinated (N=55 168) with comparison groups of vaccinated (N=4 818) and unvaccinated (N= 97 126) individuals without COVID-19. To evaluate whether vaccination itself contributed to a strain in hospitals, we use a similar design to study hospitalization rates before and after vaccination by comparing individuals vaccinated with the first dose (N=67 687) to unvaccinated individuals (N=130 769). These estimates were incorporated into a simulation of hospitalization days with different vaccine scenarios to show how the estimated results might have mattered for the hospitals and their capacity. RESULTS: Hospitalization days increased by 0.96 percentage point each day during the first week and 1.57 percentage points during the second week after testing positive for COVID-19 for unvaccinated individuals. The corresponding increase was 0.46 and 0.32 for vaccinated individuals, i.e., a substantial difference. The increase was significantly higher for those aged 45-64 than for those aged 18-25. We find no increase in hospitalization days due to vaccination. Simulation results show that vaccination reduced hospitalization days by 25 percent, mainly driven by age 45-64. CONCLUSION: Our findings indicate that vaccination of individuals aged 18-64 did alleviate pressure on hospitals. Whereas there was a substantial relieve from vaccinating the 45-64 age group, there was no such contribution from vaccinating the 18-25 age group. Our study highlights how simulation models can be useful when evaluating alternative vaccine strategies.

An Integer Linear Programming for Hospital Bed Scheduling and Capacity Management

COVID-19 has struck the Philippines in December 2019 and has brought great panic to the country's healthcare system. In a short period of time, the number of infected increased exponentially. Hospitals are suddenly filled with patients infected by the virus to the extent that patients wait for hours to days to be admitted. Others die on the road even before finding hospitals that can accommodate them. The hospitals and the country's healthcare system must consider this increasing demand to serve patients fully. Patient planning is commonly used in other countries to maximize bed allocation. A recent study using Bernoulli Distributed Random Variable represents the binary integer program. The approach combines the queuing model and simulation to reduce the patient dismissal rate and increase hospital output. On the other hand, this paper deals with strategic hospital bed capacity optimization using linear integer programming by considering the diverse resources, such as doctors, nurses, beds, and hospital rooms. © 2022 ACM.

A flatter curve affords hospitals greater time to prepare for a pandemic surge

This paper quantifies the benefits of flattening the curve (with a constant total patient load over the study period) on the risk of a hospital bed shortage in a pandemic. Using discrete-event simulation of patient care paths in hospitals, synthetic data that eliminates issues of confounding affects from the simultaneous occurrence of regional response actions and/or changes in resources, treatments or other situational circumstances, is produced for estimating hospital capacity for pandemic response. Results from systematically designed numerical experiments produced several findings. These include that the higher the acceleration in pandemic patient demand growth, the greater the impact of the intervention. Cutting this acceleration by 75% from the greatest studied rate created over four additional weeks to prepare for an 80% risk of running out of intensive care beds. Additionally, the greater the acceleration in growth, the fewer the days with a high risk of running out of beds, but the greater the total number of critical patients that could not be served with existing resources. Finally, the lower this acceleration, the fewer resources or modifications needed to cope with the surge, but the longer they are needed. The findings further show how hospitals can benefit from analytical tools that exploit digital health information to predict and plan for need levels and time to onset of these levels. These tools can be embedded within a real-time framework in which automated and early warnings can inform the selection of strategies for managing or coping with expected increases in demand for emergency hospital services.

An Overview of Hospital Capacity Planning and Optimisation.

Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.

Collaboration Among Providers to Treat COVID-19 Patients at Home Opens Beds for Those with More Serious Illness

In March 2020, COVID-19 cases were beginning to emerge in North Florida and concern over hospital bed capacity started to rise. Baptist Health System in Jacksonville, Florida quickly formed a multidisciplinary team to explore how we could increase hospital bed capacity. Utilizing the resources within our affiliated home healthcare agency, an Enhanced Home Support Model (EHSM) with a COVID-19 protocol was developed. The protocol was implemented by home health nurses and included COVID-19 testing, blood tests, and the ability to start oxygen at home on admission. Patients were provided self-monitoring equipment and information about self-isolating and infection control within the home. After the initial visit, the home health nurse and the primary care physician shared collaborative oversight through virtual visits. This article discusses how we initially approached identification of severity and the methods we used to implement the protocol. The results section offers information about the number of patients utilizing this protocol between April and December 2020;patient and physician satisfaction;and considers strengths and weaknesses of the program. In conclusion, the EHSM protocol allowed patients to receive high quality emergent care at home and increased access to hospital emergency departments and inpatient hospital beds for more seriously ill patients. [ FROM AUTHOR] Copyright of Online Journal of Issues in Nursing is the property of American Nurses Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Modelling the effect of COVID-19 mass vaccination on acute hospital admissions.

BACKGROUND: Managing high levels of acute COVID-19 bed occupancy can affect the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible impact on future bed pressures remained subject to considerable uncertainty. OBJECTIVE: The aim of this study was to model the effect of vaccination on projections of acute and intensive care bed demand within a 1 million resident healthcare system located in South West England. METHODS: An age-structured epidemiological model of the susceptible-exposed-infectious-recovered type was fitted to local data up to the time of the study, in early March 2021. Model parameters and vaccination scenarios were calibrated through a system-wide multidisciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists and academics. Scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021. RESULTS: Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert the third wave in autumn 2021 but would produce a median peak bed requirement ∼6% (IQR: 1-24%) of that experienced during the second wave (January 2021). A 2-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11-146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns), then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19%, respectively, an amount which would seriously pressure hospital capacity. CONCLUSION: Modelling influenced decision-making among senior managers in setting COVID-19 bed capacity levels, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.

Estimating the Effects of Public Health Measures by SEIR(MH) Model of COVID-19 Epidemic in Local Geographic Areas.

The COVID-19 pandemic of 2020-21 has been a major challenge to public health systems worldwide. Mathematical models of epidemic are useful tools for assessment of the situation and for providing decision-making support for relevant authorities. We developed and implemented SEIR(MH) model that extends the conventional SEIR model with parameters that define public lockdown (the level and start of lockdown) and the medical system capacity to contain patients. Comparative modeling of four regions in Europe that have similar population sizes and age structures, but different public health systems, was performed: Baden-Württemberg, Lombardy, Belgium, and Switzerland. Modeling suggests that the most effective measure for controlling epidemic is early lockdown (exponential effect), followed by the number of available hospital beds (linear effect if the capacity is insufficient, with diminishing returns when the capacity is sufficient). Dynamic management of lockdown levels is likely to produce better outcomes than strict lockdown.

Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic.

OBJECTIVE: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. METHODS: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. RESULTS: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. CONCLUSIONS: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.

A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe.

BACKGROUND: The exponential increase in SARS-CoV-2 infections during the first wave of the pandemic created an extraordinary overload and demand on hospitals, especially intensive care units (ICUs), across Europe. European countries have implemented different measures to address the surge ICU capacity, but little is known about the extent. The aim of this paper is to compare the rates of hospitalised COVID-19 patients in acute and ICU care and the levels of national surge capacity for intensive care beds across 16 European countries and Lombardy region during the first wave of the pandemic (28 February to 31 July). METHODS: For this country level analysis, we used data on SARS-CoV-2 cases, current and/or cumulative hospitalised COVID-19 patients and current and/or cumulative COVID-19 patients in ICU care. To analyse whether capacities were exceeded, we also retrieved information on the numbers of hospital beds, and on (surge) capacity of ICU beds during the first wave of the COVID-19 pandemic from the COVID-19 Health System Response Monitor (HSRM). Treatment days and mean length of hospital stay were calculated to assess hospital utilisation. RESULTS: Hospital and ICU capacity varied widely across countries. Our results show that utilisation of acute care bed capacity by patients with COVID-19 did not exceed 38.3% in any studied country. However, the Netherlands, Sweden, and Lombardy would not have been able to treat all patients with COVID-19 requiring intensive care during the first wave without an ICU surge capacity. Indicators of hospital utilisation were not consistently related to the number of SARS-CoV-2 infections. The mean number of hospital days associated with one SARS-CoV-2 case ranged from 1.3 (Norway) to 11.8 (France). CONCLUSION: In many countries, the increase in ICU capacity was important to accommodate the high demand for intensive care during the first COVID-19 wave.

Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study.

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3-1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300-54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2-0.9) additional cases and hospitalizations peaking at 12,000 (3,700-27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.

Challenges in organizing a Belgian reference hospital to respond to the COVID-19 pandemic: A case report.

The coronavirus disease 2019 (COVID-19) pandemic resulted in an enormous influx of seriously ill patients in the hospitals worldwide. After its initial impact in Asia, Europe also suffered greatly. Caring for COVID-19 patients whist maintaining treatment for patients with other conditions was a complex planning and management challenge. A series of interventions has been implemented to increase hospital capacity in response to the pandemic. Hospital provision interventions included the purchase of equipment, the establishment of additional hospital facilities and the redeployment of staff and other resources. Ensuring safe and high quality care to both COVID-19 patients and those with other conditions was a crucial aspect of the Belgian response in this current health crisis.

Emergency department provider in triage: assessing site-specific rationale, operational feasibility, and financial impact.

Emergency department (ED) crowding is recognized as a critical threat to patient safety, while sub-optimal ED patient flow also contributes to reduced patient satisfaction and efficiency of care. Provider in triage (PIT) programs-which typically involve, at a minimum, a physician or advanced practice provider conducting an initial screening exam and potentially initiating treatment and diagnostic testing at the time of triage-are frequently endorsed as a mechanism to reduce ED length of stay (LOS) and therefore mitigate crowding, improve patient satisfaction, and improve ED operational and financial performance. However, the peer-reviewed evidence regarding the impact of PIT programs on measures including ED LOS, wait times, and costs (as variously defined) is mixed. Mechanistically, PIT programs exert their effects by initiating diagnostic work-ups earlier and, sometimes, by equipping triage providers to directly disposition patients. However, depending on local contextual factors-including the co-existence of other front-end interventions and delays in ED throughput not addressed by PIT-we demonstrate how these features may or may not ultimately translate into reduced ED LOS in different settings. Consequently, site-specific analysis of the root causes of excessive ED LOS, along with mechanistic assessment of potential countermeasures, is essential for appropriate deployment and successful design of PIT programs at individual EDs. Additional motivations for implementing PIT programs may include their potential to enhance patient safety, patient satisfaction, and team dynamics. In this conceptual article, we address a gap in the literature by demonstrating the mechanisms underlying PIT program results and providing a framework for ED decision-makers to assess the local rationale for, operational feasibility of, and financial impact of PIT programs.

Short-Stay Hospitalizations for Patients with COVID-19: A Retrospective Cohort Study.

OBJECTIVE: Patients requiring hospital care for COVID-19 may be stable for discharge soon after admission. This study sought to describe patient characteristics associated with short-stay hospitalization for COVID-19. METHODS: We performed a retrospective cohort study of patients with COVID-19 admitted to five United States hospitals from March to December 2020. We used multivariable logistic regression to identify patient characteristics associated with short hospital length-of-stay. RESULTS: Of 3103 patients, 648 (20.9%) were hospitalized for less than 48 h. These patients were significantly less likely to have an age greater than 60, diabetes, chronic kidney disease; emergency department vital sign abnormalities, or abnormal initial diagnostic testing. For patients with no significant risk factors, the adjusted probability of short-stay hospitalization was 62.4% (95% CI 58.9-69.6). CONCLUSION: Identification of candidates for early hospital discharge may allow hospitals to streamline throughput using protocols that optimize the efficiency of hospital care and coordinate post-discharge monitoring.

Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020-2021 is essential. METHODS: An integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff and ventilators under different epidemic scenarios in France, Germany and Italy across the 2020-2021 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICUs under varying levels of effectiveness is examined, using a 'dual-demand' (COVID-19 and non-COVID-19) patient model. RESULTS: Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy. CONCLUSION: Reductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020-2021.

Development of Case Numbers during the COVID-19 Pandemic in a Center of Maximum-Care for Traumatology and Orthopedic Oncology.

(1) Background: The COVID-19 pandemic has led to a significant change in the utilization of trauma surgery and tumor orthopedic hospital facilities. (2) Methods: In a monocentric retrospective analysis, the weekly numbers of cases requiring intra-clinical treatment in the first four months of 2020 were compared with those of 2019. Patients' visits to the emergency department and shock room, consultation hours, work-related accidents, case numbers in the normal and intensive care units, ventilation hours, the "Simplified Acute Physiology Score/ Therapeutic Intervention Scoring System" (SAPS/TISS), the average length of stay in hospital, the number of operations and their degree of urgency, as well as deaths, were analyzed in a study based on the data from 7606 outpatient consultations in 2019 and 6755 in 2020, as well as 993 inpatient cases in 2019 and 950 in 2020. (3) Results: There was a significant reduction in the number of treatments per week in the emergency department (261 ± 29 vs. 165 ± 25; p < 0.001) with the same number of shock room treatments and fewer consultation hour contacts (226 ± 29 vs. 119 ± 65; p = 0.012). There were fewer inpatient cases (66 ± 7 vs. 42 ± 11; p = 0.001), resulting in a fall in the days of hospitalization (492 ± 63 vs. 308 ± 78; p < 0.001) and number of operations (73 ± 7 vs. 55 ± 10; p = 0.012), especially elective procedures (20 ± 3 vs. 7 ± 7; p = 0.008). The SAPS/TISS score was lower (1351 ± 1213 vs. 399 ± 281; p = 0.023). Fewer fracture treatments and septic surgeries were performed, while the number of procedures to treat orthopedic malignancies remained constant. (4) Conclusions: During the first phase of the COVID-19 pandemic, we observed a significant reduction in the number of cases treated in orthopedics. While the number of multiple-injured patients was unchanged, fewer patients presented for primary and regular care. Treatment of acute injuries and malignant tumor diseases was not at risk. There was no effect on in-house mortality. We see a potential for the recruitment of medical staff from the outpatient department, operating room, and the ward. In the event of a future second wave, our results may allow for early planning, particularly of the all-important human resources. Reorganization by hospitals and decreased patient numbers in trauma surgery can enable the reallocation of medical staff, equipment, and beds to increase capacity for COVID-19 patients.