Shortage Of Beds Research Articles

The COVID-19 Pandemic Response and Its Impact on Post-Corona Health Emergency and Disaster Risk Management in Republic of Korea

The COVID-19 pandemic significantly impacted the Republic of Korea’s Health Emergency and Disaster Risk Management (Health-EDRM). This study aims to examine the Republic of Korea’s response to the COVID-19 pandemic, focusing on Health-EDRM, especially human resources, health services, and logistics. Challenges in the Republic of Korea, including lack of medical workforce, confused risk communication, shortage of hospital beds, and inefficient distribution of medical resources, have been highlighted in this paper in terms of human resources, health service delivery, and logistics, which are components of Health-EDRM. It is essential to address the cooperation between the government and private sectors, the protection of occupational health and safety of medical staff during the pandemic, and strategies and technologies to scale up the health facilities, to respond to a future crisis like the COVID-19 pandemic.

An approximation of the inpatient distribution in hospitals with patient relocation using Markov chains

Many hospitals struggle with insufficient capacity for their inpatients. As a result, hospitals may benefit from an approach that evaluates the occupancy of inpatient wards. In this study, we approximate the occupancy distributions of inpatient wards, accounting for the cases where patients relocate due to a shortage of beds. The approximation employs a homogeneous continuous-time Markov chain to evaluate each ward as a queue containing multiple classes of patients. We avoid computational intractability by evaluating each ward separately and accommodating patients arriving from the remaining wards by interrupting the arrival processes, where the interruption times follow hyper-exponential distributions. Numerical experimentation shows that our approach is robust concerning the type of length-of-stay distribution and generally results in a minor loss of accuracy. Further validation indicates that our model reflects the occupancy distributions of inpatient wards in a Danish hospital.

NEW ZEALAND PROVINCIAL ORTHOPAEDIC TRAUMA STUDY (NZPOTS): THE EFFECTS OF INCREASING TRAUMA ON ELECTIVE ORTHOPAEDIC SURGERY IN PROVINCIAL NEW ZEALAND

Trauma and elective orthopaedic demands in New Zealand are increasing. In this study, prospective and retrospective data has been collected at Nelson Hospital and across New Zealand to identify the percentage of elective theatre time lost due to cancellation for acute patient care. Data has been collected from theatre management systems, hospital data systems and logged against secretarial case bookings, to calculate a percentage of elective theatre time lost to acute operating or insufficient bed capacity.Data was collected over a five-month period at Nelson Hospital, with a total of 215 elective and 226 acute orthopaedic procedures completed. A total of 95 primary hip or knee arthroplasties were completed during this trial while 53 were cancelled. The total number of elective operative sessions (one session is the equivalent of a half day operating theatre time) lost to acute workload was 47.9. Thirty-three percent of allocated elective theatre time was cancelled - an equivalent of approximately one-full day elective operating per week.Over a five-week period data was collected across all provincial hospitals in New Zealand, with an average of 18% of elective operating time per week lost due to acute workload. Elective cancellations were due to acute operating 40% of the time and bed shortages 60% of the time. The worst effected centre was Palmerston North which had an average of 33% of elective operating cancelled per week to accommodate acute surgery or due to bed shortages.New Zealand's provincial orthopaedic surgeons are under immense pressure from acute operating that impedes provision of elective surgery. The New Zealand government definition of an ‘acute case’ does not reflect the nature of today's orthopaedic burden. Increasing and aging populations along with staff and infrastructure shortages have financial and societal impacts beyond medicine and require better definitions, further research, and funding from governance.

Cancer Patients during COVID-19 Pandemic: A Mini-Review.

Since its emergence, coronavirus disease 2019 (COVID-19) has affected the entire world and all commerce and industries, including healthcare systems. COVID-19 adversely affects cancer patients because they are immunocompromised. Increased COVID-19 infection and shortage of medical supplies, beds and healthcare workers in hospitals affect cancer care. This paper includes a description of the existing research that shows the impact of COVID-19 on the management of cancer patients. Aged people with various chronic conditions such as cancer and comorbidities face more challenges as they have a greater risk of disease severity. COVID-19 has affected care delivery, including patient management, and has been responsible for increased mortality among cancer patients. Cancer patients with severe symptoms require regular therapies and treatment; therefore, they have a higher risk of exposure. Due to the risk of transmission, various steps were taken to combat this disease; however, they have affected the existing operational efficiency. Herein, we present the changing priorities during COVID-19, which also affected cancer care, including delayed diagnosis, treatment, and surgeries.

Estimating the impact of influenza vaccination of low-risk 50–64-year-olds on acute and ICU hospital bed usage in an influenza season under endemic COVID-19 in the UK

ABSTRACT Co-circulation of influenza and SARS-CoV-2 has the potential to place considerable strain on health-care services. We estimate the cost-effectiveness and health-care resource utilization impacts of influenza vaccination of low-risk 50–64-y-olds in the United Kingdom (UK) against a background SARS-CoV-2 circulation. A dynamic susceptible-exposed-infected-recovered model was used to simulate influenza transmission, with varying rates of vaccine coverage in the low-risk 50–64 y age-group. Four scenarios were evaluated: no vaccination (baseline), 40%, 50%, and 60% coverage. For the 50% and 60% coverage, this rate was also applied to high-risk 50–64-y-olds, whereas 48.6% was used for the baseline and 40% coverage scenarios. Cost-effectiveness was estimated in terms of humanistic outcomes and incremental cost-effectiveness ratio (ICER), with discounting applied at 3%. Overall, influenza vaccination of 50–64-y-olds resulted in reductions in GP visits, hospitalizations, and deaths, with a reduction in influenza-related mortality of 34%, 41%, and 52% for 40%, 50%, and 60% coverage, respectively. All four scenarios resulted in acute and intensive care unit (ICU) bed occupancy levels above available capacity, although vaccination of low-risk 50–64-y-olds resulted in a 35–54% and 16–25% decrease in excess acute and ICU bed requirements, respectively. Vaccination of this group against influenza was highly cost-effective from the payer perspective, with ICERs of £2,200–£2,343/quality-adjusted life year across the coverage rates evaluated. In conclusion, in the UK, vaccination of low-risk 50–64-y-olds against influenza is cost-effective and can aid in alleviating bed shortages in a situation where influenza and SARS-CoV-2 are co-circulating.

Demand-supply balance of disaster medical care in Osaka City based on damage estimation for a Nankai Trough megathrust earthquake: A geographic information system-based analysis.

Studies have not fully examined whether the medical care system would be able to manage the high number of casualties due to a Nankai Trough earthquake, whose probability of recurrence in the next 30 years is ~70%. This study assessed the demand-supply balance of the disaster medical care system in Osaka city by integrating the data on damage estimation and disaster coping hospitals using a geographic information system. We obtained data on the distribution of casualties in two cases, high and low rates of evacuation from the tsunami, and available beds in Osaka city calculated from operating data of each disaster coping hospital. We expanded these data on a geographic information system and investigated the balance of medical care. The total number of available beds in the disaster medical care facilities was 5,559, and the shortage with evacuation rates being either low or high, would be 47,631 and 1,487, respectively. With a low evacuation rate, bed shortage is a common occurrence in coastal areas. With a high evacuation rate, bed shortage decreases, and problems with medical care arise in the eastern area of Osaka city. In the case of a low evacuation rate, greater bed shortage was found along the coast of Osaka Bay where the probabilities of flooding are high. In the case of a high evacuation rate, however, the number of casualties was much lower. A shortage of medical care did not occur along the coast, but in the eastern part of Osaka city.

ОЦЕНКА ГОТОВНОСТИ СТАЦИОНАРОВ К РАБОТЕ В УСЛОВИЯХ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЙ

Significance. The COVID-19 pandemic has led to a crisis in society and seriously challenged healthcare capabilities around the world. The industry's lack of preparedness for such a large-scale and rapid spread of the virus around the world was one of the major factors in the fight against the pandemic faced by medical organizations and health services around the world. During the pandemic, the Russian Federation (hereinafter referred to as the RF) has radically restructured activities of medical organizations, re-equipped them with modern medical equipment, and intensively developed digitalization, making it possible to prevent the collapse of the industry and ultimately successfully cope with the pandemic. Analysis of the results and consequences of the restructuring the entire industry, medical organizations providing medical care of various types and in different conditions, allows us to assess emergency preparedness of hospitals and develop recommendations for the future. The purpose of the study was to assess hospital emergency preparedness in the Russian Federation based on the analysis of dynamics in bed capacity, structure of beds and utilization indicators in 2019-2022. Material and methods: comparative analysis of the number of beds, its structure and utilization indicators in the pre-COVID period, the pandemic period and the early post-COVID period based on the federal statistical observation form No. 30 “Information about medical organization” in the Russian Federation and in the constituent entities of the Russian Federation. Methods included descriptive statistics, comparative analysis and analytics. Results. A long-term trend towards reducing bed capacity and availability of beds was once interrupted in 2020, with the trend towards reducing the number of beds in rural areas remaining interrupted. In general, during the observation period, the total number beds decreased by more than 30 thousand, the population-to-bed ratio decreased by 3%, and the hospitalization rate dropped by 7%. The shortage of adult infectious disease beds at the beginning of the pandemic associated with the prior reduction of bed capacity against the background of the network restructuring was met by repurposing other beds to serve as infectious disease ones, and deploying additional infectious disease beds in and outside medical organizations to treat patients with COVID-19. The number of adult infectious disease beds increased 7.5-fold by the end of 2020. However, the number of adult infectious disease beds then started to decrease reaching a 59,4% increase by 2022 compared to 2019, but their share in rural areas decreased from 14% to 8%. The number of pediatric disease beds increased against the background of competing dynamics. Overall, during the period, the total number of pediatric and adult infectious disease beds increased by 27%. In the structure of adult infectious disease beds, the share of COVID-19 beds varied from 92% in 2020 to 34% in 2022; additionally deployed COVID-19 beds were also in operation. Over the observation period, the average bed length of stay and average bed occupancy decreased, while hospital mortality increased. Significant variability in hospital performance indicators across regions was registered. Conclusion In 2021-2022, hospital facilities in the Russian Federation were prepared to meet the increased incidence of infectious diseases. However, the renewed processes of bed capacity reduction necessitated forecasting sustainability of hospitals in emergency situations. The undertaken analysis shows that a cut in the number of round-the-clock beds should not be guided by the mere process of reduction. The decision to change the bed capacity and structure should be preceded by a thorough preliminary analysis of specialized care availability by each specialty planned to be reduced. Reducing planned hospitalizations and postponing treatment of patients with chronic non-communicable diseases turned out to be not fully justified, which was confirmed by other studies. Infectious disease beds must function in optimal numbers to cope with the increased flow of patients in early stages of an emergency. The areas where infectious disease beds are deployed must be sufficient to realize the possibility of quickly increasing the capacity of an infectious disease hospital to the required level. Planning a network of hospitals should ensure a comprehensive specialized care delivery to patients with chronic non-communicable diseases during emergencies including planned care. Scope of application: Hospital capacity and structure planning.

Outcome Analysis of Adult Distal Radius Fractures Managed during COVID-19 Pandemic.

COVID-19 pandemic created extreme pressure situation on health care services across the globe with shortage of medical staff and beds. The management of fragility fracture also affected significantly. The distal radius fractures (DRFs) are one of the common frailty fractures. There was a significant shift in the treatment of such injuries with new guidelines leaning toward more of non-operative management. A retrospective cross-sectional study of DRFs (DRF) treated during COVID pandemic for a period of 1 year in the United Kingdom. This included all radiologically confirmed cases of DRFs with isolated adult injuries excluding polytrauma, same limb other injuries, associated neurovascular injuries, and age below 18 years. All patient's data were collected from hospital records retrospectively from April 2020 to March 2021. There were a total of 179 patients with distal radius fracture, of which 141 (78.8%) were females and 38 (21.2%) males. The mean age was 68.2 years (SD 68.20 ± 15.63) and there were greater number of patients with intra-articular fracture. There were 28.5% (Grades 1 and 2, Group I) extra-articular fractures; 69.8% (Grades 3-8, Group II) were intra-articular fractures and 3 Barton's fractures (Group III) accounted to 1.7%. 137 patients were (76.5%) treated to non-operatively and 42 (23.5%) were treated surgically. 135 (75.4%) patients had good and 43 (24%) satisfactory clinical outcomes. 3 (1.6%) patients required osteotomy for malunited fractures. Fracture grade I and II is significantly associated with non-operative procedure (100.0% vs 84.3%) with P < 0.001. This study confirmed that non-operative treatment is still a good option even in intra-articular fractures. Surgical fixation should be considered in young patients and those with higher grade of fracture classification and greater angulation at presentation. A virtual physiotherapy is still a good option.

UNDERSTANDING THE PALLIATIVE CARE INFORMATION CIRCULATING ON TWITTER DURING THE CORONAVIRUS PANDEMIC

Abstract Palliative care is a growing medical specialty. While its value has become particularly evident during the pandemic, it faces various challenges including misconceptions among the general public, a lack of awareness of its benefits, and limited/sporadic access and coverage by Medicare. Given the increasing number of people sharing their health information and seeking healthcare information on social media where misinformation is widely spread, this study examined types of information on palliative care circulated on Twitter. A total of 26,495 English Tweets were collected 01/01/2020 – 12/30/2021 using the keywords “Palliative” and “Covid” or “Corona”. We manually developed a framework for coding/classifying the first 6,000 Tweets. Of those, 5,308 were unrelated Tweets (e.g., advertising palliative care seminars/conferences). Among the remaining Tweets, persistent myths were observed (e.g., palliative care is only for dying people) and were labeled accordingly. In addition, while some people mentioned negative impact of the pandemic on palliative care (e.g., shortage of beds), others found value in palliative care and reported positive changes due to the pandemic (e.g., telehealth in palliative care). Consequently, the following categories were defined for Tweets: i) Recognized benefits (203 Tweets); ii) Positive impact of the pandemic on palliative care (120 Tweets); iii) Negative impact of the pandemic on palliative care (333 Tweets); iv) Myth (63 Tweets). We will use these manually classified Tweets to train machine-learning algorithms to automatically classify the remaining tweets, aiming to obtain a comprehensive understanding of palliative care information circulating on Twitter and seek ways to promote palliative care use.

Quantitative evaluation of the role of Fangcang shelter hospitals in the control of Omicron transmission: A case study of the outbreak in Shanghai, China in 2022

Since Omicron began to spread in China, Shanghai has become one of the cities with more severe outbreaks. Under the comprehensive consideration of the vaccine coverage rate, the number of Fangcang shelter hospital beds and the number of designated hospital beds in Shanghai, this paper established a deterministic compartmental model and used the Nelder-Mead Simplex Direct Search Algorithm and chi-square values to estimate the model parameters. we calculate ℛ0 = 3.6429 when the number of beds in the Fangcang shelter hospital is relatively tight in the second stage and ℛ0 = 0.4974 in the fifth stage when there are enough beds in both Fangcang shelter hospital and designated hospital. Then we perform a sensitivity analysis on ℛ0 by using perturbation of fixed point estimation of model parameters in the fifth stage, and obtain three parameters that are more sensitive to ℛ0, which are transmission rate (β1d), proportion of the infectious (η) and the hospitalization rate of asymptomatic infected cases (δ1). Through simulation, we obtain that if the hospitalization rate of asymptomatic infections δ2 > 0.9373 or the transmission rate β1b < 0.0467, the second stage of Omicron transmission in Shanghai can be well controlled. Finally, we find the measure that converting the National Convention and Exhibition Center (NECC) into a Fangcang shelter hospital has played an important role in curbing the epidemic. Whether this temporary Fangcang shelter hospital is not built or delayed, the cumulative number of confirmed cases will both exceed 100,000, and the cumulative asymptomatic infections will both exceed 1 million. In addition, for a city of 10 million people, we obtain that if a permanent Fangcang shelter hospital with 17,784 beds is built ahead of epidemic, there will be no shortage of beds during the outbreak of Omicron. Our findings enrich the content of the impact of Fangcang shelter hospital beds on the spread of Omicron and confirm the correct policy adopted by the Chinese government.

The experience of Japan: the application of the intersectoral balance methodology to control health care costs as a factor of mobilization readiness of health care to COVID-19 pandemic

One of possible reasons for success of Japan in confronting the COVID-19 pandemic (low mortality rates, refusal of hard lock-downs and relatively low fall in economy) is seen in record high (3-4 times higher than in most other developed countries) provision of hospital beds. Its financing was supported during first 2 decades of the XXI century by the policy of relative to GDP advanced growth of public health public expenditures based on assessment of multiplier impact of these expenditures on demand, production and employment in other sectors of the economy using the intersectoral balance method based on "input-output" tables.Purpose of the study is to analyze Japan's economic policy in managing budgetary health care costs.The comprehensive statistical, comparative and retrospective analysis of available data was applied.The study results permit to suggest that high provision of the Japan population with hospital care resources and low mortality rates in 2022 prior to development of vaccines and effective treatment schemes for COVID-19 can be explained, among other things, by long-term policy of managing health care costs using assessment of their effect on production growth, demand and employment in other economy sectors using intersectoral balance method based on regular compilation of "input-output" tables.The data obtained permits to characterize as promising approach of the Japanese government to management of health care costs using assessment of their effect on production growth, demand and employment in other sectors of the economy using intersectoral balance method based on the regular compilation of "input-output" tables. This approach permitted to increase up to 1.5 times health care costs during 2005-2018 in situation of chronic stagnation of the national economy and thus to avoid world-wide trend towards reduction of hospital bed stock and after the start of pandemic severe shortage of hospital beds. The positive experience of Japan is confirmed by encouraging results of 2 pilot projects in the EU countries on applying the intersectoral balance method to assess the multiplier effect of health care costs in 2017-2018. It is considered that using the experience of Japan in managing budgetary health care expenditures through intersectoral balance method is challenging.

1067. Predicting next 7-day Discharges of Hospitalized COVID-19 Patients Using Ensemble Learning

Abstract Background COVID-19 pandemic, especially during resurgences of cases in hard-hit areas, led to significant shortage of hospital beds. Such shortages may be alleviated through timely and effective forecasting of hospital discharges. The objective of this study is to predict next 7-day discharges of hospitalized COVID-19 patients using daily-based electronic health records (EHR) data. Methods Using EHR data of hospitalized COVID-19 patients from 03/2020-08/2021, we employed ensemble learning to predict next 7-day discharges of individual patients. We used both baseline and daily inpatient features for model training, validation, and test. Baseline features include demographic and clinical characteristics, and comorbidities. The daily inpatient features were vital signs, laboratory tests, medications administered, acute physiological scores, use of ventilator, and use of intensive care unit. 1832 hospitalized patients were identified (12,397 hospital days). Samples were randomly split at patient level (7:2:1) into training set (N=1,283 patients with 8,704 hospital days), validation set (N=366 patients with 2,524 days), and test/holdout set (patient N=183, and 1,169 days). Prediction models were trained on the training set and the validation set. We conducted the model training separately on the samples of admission day and the samples of days after admission day. The predictions were based on the ensemble learning from decision tree, XGBoost, logistic regression, and multilayer perceptron, long short-term memory (LSTM), bi-directional LSTM, and convolutional neural network. The combination of ensemble learning on the test/holdout set was used for final next 7-day predictions based on ‘hard’ voting (by majority). Where there was a tie, we used ‘soft’ voting (sum of probabilities) to break the tie. Figure 1.Data Processing PipelineFigure 2.Ensemble Model Architecture Results The overall average hospital length of stay was 8.7 (SD=10.5) days. The ensemble learning accuracies for admission-day samples and after-admission-day samples were 0.781 and 0.793, and the F1-scores for were 0.761 and 0.789, respectively. Conclusion EHR data of hospitalized COVID-19 patients can be used to predict next 7-day hospital discharges. Additional inpatient features and more advanced machine learning techniques are needed for prediction accuracy improvement. Disclosures All Authors: No reported disclosures.

1002. Complex Outpatient Antimicrobial Therapy (COpAT) Program at a Rural Academic Medical Center: Evaluation of First 100 Patients

Abstract Background Literature shows early intravenous (IV) to oral (PO) antimicrobial transition for infective endocarditis (IE) and bone and joint infection (BJI) is noninferior to prolonged IV antimicrobial therapy. COVID-19 pandemic peaks resulted in critical shortages of staffed hospital beds spurring innovation. Outpatient parenteral antimicrobial therapy (OPAT), a well-established program using prolonged IV antimicrobials, faces challenges such as infusion resource needs and social circumstance limitations. Complex outpatient antimicrobial therapy (COpAT) uses PO in place of IV antimicrobials. We hypothesized rapid adoption of COpAT would decrease hospital length of stay and open beds while retaining satisfactory clinical outcomes. Methods COpAT protocols (Image 1) and guidelines by infection type and isolated organism (Image 2) were created. Hospitalized patients including persons who inject drugs (PWID) were evaluated for IV to PO antimicrobial transition by an infectious diseases (ID) physician and then followed by an ID physician-pharmacist team. Demographic, ID, and clinical outcome data for the first 100 COpAT patients between December 2020 and February 2022 were obtained by retrospective chart review. Image 1.COpAT Inpatient and Outpatient Protocols Image 2. COpAT Guidelines by Infection Type and Isolated Organism MSSA = methicillin-susceptible Staphylococcus aureus; MRSA = methicillin-resistant Staphylococcus aureus; spp. = species; TMP/SMX = trimethoprim-sulfamethoxazole; DS = double strength; SSTI = skin and soft tissue infection; CAP = community-acquired pneumonia Results PWID accounted for 78% of COpAT patients. BJI followed by mixed infection (IE and BJI) was most prevalent (Image 3) with bacteremia in 53% of cases. Staphylococcus aureus was most frequently isolated (Image 4). Oral linezolid and fluoroquinolones, often in combination, were most commonly used. IV and PO antimicrobials were taken for a median 28 and 14 days, respectively. The COpAT program saved 1425 IV antimicrobial and 1363 hospital days. Assuming daily inpatient cost of $2050, cost avoided was $2,794,150. COpAT patients participated in ID follow-up and adhered to PO antimicrobials with low 30-day readmission rates (Image 5). Image 3.Infection TypeImage 4.Isolated Organism CoNS = coagulase-negative staphylococci Image 5.Clinical Outcomes Conclusion In a sample of 100 COpAT patients including PWID, IV to PO antimicrobial transition safely saved hospital days and mitigated critical bed shortages during pandemic peaks. A successful COpAT program requires a multidisciplinary group: close ID physician-pharmacist collaboration extending to OPAT and antimicrobial stewardship teams. With a COpAT program in place, even earlier IV to PO antimicrobial transitions should be studied. Disclosures All Authors: No reported disclosures.

Development of a model for predicting hospital beds shortage and optimal policies using system dynamics approach

BackgroundPolicymakers use simulation-based models to improve system feedback and model the reality of the problems in the system. This study uses the system dynamics approach to provide a model for predicting hospital bed shortages and determine the optimal policy in Shiraz, Southern Iran.MethodsThis study was designed based on Sterman's system dynamic modeling (SDM) process. Firstly, we determined the main variables affecting bed distribution using a mixed qualitative and quantitative study which includes scoping review, expert panel, Delphi, and DANP. Then, dynamic hypotheses were designed. Subsequently, we held several expert panels for designing the causal and stock-flow models, formulating and testing a simulation model, as well as developing various scenarios and policies.ResultsDynamic modeling process resulted in four scenarios. All of the scenarios predicted a shortage of national hospital beds over a 20-year time horizon. Then, four policies were developed based on the changes in the number of beds and capacity of home care services; finally, the optimal policy was determined.ConclusionsDue to the high cost of setting up hospital beds, developing and supporting cost-effective home care services, strengthening the insurance coverage of these services, and improving the quantity and quality of community care, considering the real needs of the community could be considered as an optimal option for the future of the city.

Elective surgery scheduling under uncertainty in demand for intensive care unit and inpatient beds during epidemic outbreaks

Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.

Влияние производственных факторов на формирование психической дезадаптации у медицинских работников во время пандемии новой коронавирусной инфекции COVID-19

Summary. The aim of the study is to analyze the modern foreign and domestic scientific literature devoted to the influence of production factors on the formation of mental maladaptation in medical workers, as well as possible methods of its prevention and curing. Materials and research methods. Scientific studies of negative psychological reactions and manifestations of mental disorders in medical workers of various profiles in the COVID-19 pandemic were analyzed. The search protocol included the use of various databases, including PubMed; Russian information and analytical portal in science, technology, medicine, and education — eLIBRARY.ru; open-access statistical reporting data; and official websites of scientific journals on the subjects under study. Study results and their analysis. The results of the study showed that most medical professionals experienced stress caused by organizational factors such as a lack of personal protective equipment (PPE), fears of not being able to receive quality medical care if they were sent to work elsewhere, fears of rapidly changing context, lack of access to current information and communication, lack of special medications, shortage of ventilators and beds in intensive care units In the conditions of regular health care work, most of the professionals noted tension in the process of performing professional duties, exceeding their physical and mental capacities, which was one of the main reasons for anxiety, nervousness or stress at work.

Hospital bed capacity management during the COVID-19 outbreak using system dynamics: A case study in Amol public hospitals, Iran

ABSTRACT Nowadays proper management of hospital resources has played an important role in reducing the economic pressure on hospitals. Unprecedented challenges have arisen for healthcare systems including further resource constraints due to the Covid-19 pandemic. So different countries faced the low capacity of hospitals' resources to admit and care for patients during the crisis. Medical staff, beds and personal protective equipment are among the scarce resources in this period. In this study, the patient flow in public hospitals has been modeled using System Dynamics (SD) simulation to manage the available capacity of intensive care units (ICU) and wards during the COVID-19 period. The model has been implemented in Vensim PLE and verified for public hospitals in Amol, Iran. The results have shown that hospitals face bed shortages in the period coinciding with the growing incidence of COVID-19, being forced to cancel or delay the admission of selected patients and nonemergency surgeries. Different scenarios based on possible strategies for managing hospital bed capacities have been also evaluated. The best strategy is the one in which the allocated bed capacities to COVID-19 patients are altered based on infection rates during different stages of the pandemic, resulting the shortage of beds is postponed the most.

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region.Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals’ data warehouses and regional infection data as recorded in the Netherlands.The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

Outcome of Tracheostomy in COVID-19 Patients.

Since the start of the COVID-19 pandemic 2019, quite a few patients became critical and needed ICU admission with ventilator assistance. Tracheostomy, which was initially performed late during the course of patient on ventilator, has now been considered a procedure that can be performed relatively early as this leads to early weaning of patients and overcomes the shortage of critical beds. This study aims to focus on the outcomes of tracheotomised COVID-19 patients in terms of survival and any tracheostomy related morbidity. A prospective study was performed on COVID-19 patients undergoing tracheostomy at this tertiary care teaching hospital, which also was a dedicated centre for treating COVID-19 patients. The duration of this study was from April 2020 to September 2021. Following tracheostomy, all patients were followed up regularly and clinical changes were recorded. Points that were specifically noted were timing of the tracheostomy, change in ventilator settings, tracheostomy related complications, requirement of oxygen, days needed to wean the patient, decanulation, and, if death, the cause of death. A total of 136 surgical open tracheostomies were performed on COVID-19 patients over the study period. The mean duration of intubation (timing of tracheostomy) was 12 days. A total of 73 out of 136 (53.6%) patients survived. 51 patients (37.5%) got decannulated during the course of the hospital stay. 9 patients were decanulated during the follow up visits and 13 patients were lost to follow up. 63 out of 136 (46.3%) patients died due to COVID pneumonia. Most of the patients who died had gone into multi-organ failure. Air leak syndromes (pneumothorax and pneumomediastinum) were common findings. 10 patients already had surgical emphysema before taking up for tracheostomy and 6 developed 2-3 days after tracheostomy. The most common complication was bleeding, which was seen in 28 out of 136 patients. The Median weaning of period of patients who survived was 5 days. Performing tracheostomy early in COVID-19 patients helps in early weaning of the patient from the ventilator and makes nursing care easier and increases the availability of ICU beds. The mortality rate was 46% amongst the 136 tracheostomies done in COVID-19 patients. Local site bleeding was the most common complication and surgical emphysema was also seen more than routine tracheostomies. The online version contains supplementary material available at 10.1007/s12070-022-03248-1.

Prognostic comparison of COVID-19 outpatients and inpatients treated with Remdesivr: A retrospective cohort study.

Since the late COVID-19, many countries have faced various surges and peaks within the number of infected. Iran was one of the countries that faced many surges and peaks within these years and faced many inadequacies and shortages of resources and hospital beds. Hence the healthcare system started using in-hospital medication such as Remdesivir in outpatients to reduce the load of patients admitted to the hospital. This study aimed to evaluate and compare the reported signs, symptoms, and outcomes of COVID-infected hospitalized and out-patients receiving Remdesivir. In this retrospective cohort study, 214 patients (121 outpatient and 93 hospitalized) with moderate levels of Covid infection between October 2021 and February 2022 were studied. Both groups were treated with 200mg of Remdesivir, followed by 100 mg daily intravenous injections for five days; signs and symptoms, such as pain, shortness of breath, cough, fever and etc., of patients at the initiation and the end of treatment were recorded. Moreover, the patients' blood oxygen saturation was assessed two to three times a day, and the mean of the recorded measures was considered as the daily oxygen saturation. The outpatient group had to visit the hospital daily for treatment and assessment. At the treatment's end, mortality rates, disease signs, and symptoms alleviations were compared between the groups. The outpatient and hospitalized group's mean age was 40.30 ± 12.25 and 37.70 ± 12.00 years, and 51.2% and 55.9% were males, respectively. There was no statistical difference between baseline and clinical characteristics in the outpatients and hospitalized groups. After adjusting for oxygen saturation at baseline and gender in the multivariable Cox regression analysis, the risk of death did not statistically differ between the hospitalized and outpatient group (hazard ratio: 0.99, 95% confidence interval: 0.39-2.50)) at the end of the study. Based on the results of this study, the outcome, signs, and symptoms of inpatient and outpatient Remdesivir treatment groups did not differ significantly. Hence in COVID-19 surges where we have limitations in admitting patients, outpatient Remdesivir treatment for those without any underlying diseases can be a proper management method.