Allocation Of Hospital Beds Research Articles

Optimizing hospital bed allocation for coordinated medical efficiency and quality improvement

Optimizing hospital bed allocation for coordinated medical efficiency and quality improvement

The allocation and utilization efficiency of hospital beds in Sichuan Province, China.

To analyze the allocation and utilization efficiency of hospital beds in Sichuan Province, China, and to provide a scientific basis for improving the rational allocation and efficient utilization. The supply and demand balance method, health resource agglomeration degree (HRAD), bed efficiency index and bed utilization model were used to evaluate the allocation and utilization efficiency of hospital beds in Sichuan Province from 2017 to 2021. The number of hospital beds per 1000 population in Sichuan Province increased from 4.97 in 2017 to 5.94 in 2021. The overall supply and demand ratio of hospital beds in Sichuan Province is between 0.85 and 1.01, and the supply and demand situation is a basically balanced situation. The HRAD of hospital beds in Ya'an, Aba, Ganzi and Liangshan is <1, indicating that the equity of hospital beds by geography in these regions is low. The difference between HRAD and population agglomeration degree (PAD) in 9 regions, including Deyang, Aba, Ganzi and Liangshan, is <0, indicating that there are insufficient hospital beds in these areas relative to the agglomerated population. The bed efficiency index of hospital beds in 17 regions, including Chengdu, Zigong, Aba and Ganzi, are all <1, which means that hospital beds are operating with low efficiency. The bed utilization model of Panzhihua is efficiency type, that of Zigong, Deyang and Ziyang is pressure bed type, and that of Nanchong and Ya'an is idle type. The hospital bed allocation in Sichuan Province is relatively good, and the supply and demand situation is in a basically balanced situation. The hospital bed allocation in Aba, Ganzi and Liangshan is insufficient by geography and population. The overall operational efficiency of hospital beds is low, and there are more idle and pressure bed utilization models.

Usefulness of dalbavancin in early discharge and nonhospitalization. It's time to throw your heart over the obstacle?

Dalbavancin is a semisynthetic lipoglycopeptide long-acting antibiotic approved for the treatment of acute bacterial skin and skin structure infections (ABSSSIs). Its features can be useful in the current healthcare scenario characterized by the shortage of available hospital beds. We implemented several actions in order to optimize the use of dalbavancin allowing an improvement strategy both from the healthcare system and the patient's perspective in two hospital settings. In the Emergency Department we hospitalized only patients who met the clinical criteria and not the logistic criteria (i.e., the need for antibiotic therapy infusion). During the years 2017-2023, this strategy was applied in 40 cases, thus avoiding 40 hospitalizations for a total saving of 280 days of hospitalization.In the Internal Medicine ward and surgery department when there was no longer any need for hospitalization, we discharged the patient as early as possible. During the years 2017-2023, this strategy was applied in 189 cases, saving at least 1,134 days of hospitalization. The outcome of the treated patients was favorable in 228 out of 229 patients (99.5%). Our experience using dalbavancin in ABSSSI has been very satisfactory overall. The efficacy was close to 100%. Minor adverse events of slight severity occurred rarely. At the same time, this strategy allowed a more efficient allocation of hospital beds. Dalbavancin presents an ideal pharmacodynamic/pharmacokinetic profile for the management of ABSSSI especially in settings where shortage of hospital beds is critical.

A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for interval-valued pythagorean fuzzy hypersoft set

The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study.

Safety and cost of performing laparoscopic sleeve gastrectomy with same day discharge at a large academic hospital.

Laparoscopic sleeve gastrectomy (LSG) is the most common surgical treatment for morbid obesity. While certain specialized ambulatory surgery centers offer LSG on an outpatient basis, patients undergoing LSG at most academic centers are admitted to hospital for initial postoperative convalescence and monitoring. Our institution has begun to offer LSG with same-day discharge (SDD) in select patients. We aimed to compare the perioperative outcomes and costs for patients undergoing LSG with inpatient admission versus SDD. All patients enrolled in the SDD program from December 2020 through July 2022 were identified from a prospectively maintained database. Patients enrolled in this pathway were analyzed on an intention-to-treat basis even if ultimately admitted postoperatively. Propensity scoring was used to match these patients 1:1 to those with planned inpatient recovery based on age, BMI, and ASA classification. Seventy-five patients were enrolled in the LSG with SDD program during the study period. Among these, 62 patients (82.7%) had successful immediate postoperative discharge. Reasons for cancelation of planned SDD included anxiety (n = 5), pain (n = 3), nausea (n = 2), and one patient each with hypotension, urinary retention, and bleeding. After matching, there were no differences in age, BMI, or ASA classification in a comparison group of patients with planned inpatient recovery. There were no differences in perioperative complications. There were no readmissions or requirements for outpatient intravenous fluids among patients with SDD, compared to n = 3 (4.0%) and n = 2 (2.7%) in the inpatient cohort, respectively. The total perioperative cost for patients undergoing LSG with planned SDD was 6.8% less than those with inpatient recovery. With appropriate protocols, LSG with same-day discharge can safely be performed at large academic surgery centers without increased morbidity or need for additional services in the perioperative period. SDD may be associated with decreased costs and allows for more efficient hospital bed allocation.

Disparity in hospital beds’ allocation at the county level in China: an analysis based on a Health Resource Density Index (HRDI) model

BackgroundAs approximately 3/4 of the population lives in county-level divisions in China, the allocation of health resources at the county level will affect the realization of health equity. This study aims to evaluate the disparity in hospital beds at the county level in China, analyze its causes, and discuss measures to optimize the allocation.MethodsData were drawn from the Chinese County/City Statistical Yearbook (2001–2020). The health resource density index (HRDI) was applied to mediate between the influence of demographic and geographical factors on the allocation of hospital beds. The trends of HRDI allocation were evaluated through the growth incidence curve and the probability density function. The regional disparity in the HRDI was examined through the Lorenz curve, and Dagum Gini coefficient. The contribution of the Gini coefficient and its change were assessed by using the Dagum Gini decomposition method.ResultsFrom 2000 to 2019, the number of hospital beds per thousand people at the county level in China increased dramatically by 1.49 times. From the aspect of the HRDI, there were large regional disparities at the national level, with a Gini coefficient of 0.367 in 2019 and in the three subregions. In 2019, the Gini coefficient of the HRDI exhibited regional variations, with the highest value observed in the western region, followed by the central region and the eastern region. Decomposition reveals that the contribution of interregional disparity changed from the dominant factor to the least important factor, accounting for 29.79% of the overall disparity and the contribution of trans-variation intensity increased from 29.19% to 39.75%, whereas the intraregional disparity remained stable at approximately 31% and became the second most important factor.ConclusionThe regional disparity in hospital beds allocation at the county level in China was large and has not improved substantially. Trans-variation intensity was the main reason for the overall disparity and changes, and the intraregional disparity was more important than the interregional disparity for the overall disparity.

Hospital Bed Allocation using Three-Stage Weighted Optimization Method for Government Hospital in Pulau Pinang

Ineffective bed allocation across hospital departments leads to the imbalance between patients’ needs and resource capacity. This study aims to simulate patients’ arrivals, to measure the departments’ importance and to design a mathematical model for allocating beds which will be realized by the proposed three-stage weighted optimization model. The stages consist of data simulation in ARENA software, weight evaluation based on bed occupancy rate patients’ arrival rates, bed occupancy rate (BOR), patients’ average length of stay (ALOS) and bed operation cost (BOC), and weighted optimization using goal programming (GP) model. The goals to be achieved in this study consists of minimizing the idle beds in departments and the hospital and minimizing the total BOC. The result of the study shows that obstetrics and orthopaedics wards obtained the biggest number of beds while dengue and paediatrics surgery wards obtained the least number. The statistical analysis made to the results shows that patients’ arrival rate is the most influential factor in allocating the beds as its Pearson correlation value to the bed numbers is 0.789, which indicates strong correlation.

Allocation of hospital beds on the emergence of new infectious disease: A mathematical model.

This paper is concerned to a mathematical model for the management of hospital beds when a new infection emerges in the population with the existing infections. The study of this joint dynamics presents formidable mathematical challenges due to a limited number of hospital beds. We have derived the invasion reproduction number, which investigates the potential of a newly emerged infectious disease to persist when some infectious diseases are already invaded the host population. We have shown that the proposed system exhibits transcritical, saddle-node, Hopf, and Bogdanov-Takens bifurcations under certain conditions. We have also shown that the total number of infected individuals may increase if the fraction of the total number of hospital beds is not properly allotted to the existing and a newly emerged infectious disease. The analytically obtained results are verified with the help of numerical simulations.

RV4JaCa—Towards Runtime Verification of Multi-Agent Systems and Robotic Applications

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This is achieved keeping in mind possible safety-critical uses of the MAS, such as robotic applications. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. In this paper, we mainly focus on MAS when used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. The latter may be a problem and undermine the development of the software agents. In this paper, we tackle this problem by proposing and demonstrating the implementation of a framework that aims to control the dialogue flow in a MAS; especially when the MAS communicates with the user through natural language to aid decision-making in a hospital bed allocation scenario.

Adding Resilience to Hospital Bed Allocation, using FRAM Ralph MacKinnon, Jacob Barnaby, Nomoto Hideki, Rees Hill and David Slater

Modelling complex sociotechnical systems to better understand and optimise their operation has proved a challenge and many methodologies have been put forward to use in what has been called model-based system engineering (MBSE). The appropriateness and availability of the Functional Resonance Analysis Method as a suitable candidate has been tested here, in the context of developing a methodology that can be applied currently, and in an actual real system requiring taking life and death decisions on an informed understanding of the factors and risks involved. The allocation of scarce critical care beds in hospitals is a clear example of where a better understanding of the system and a way of feeding the decision makers with crucial information on numbers and trends could help optimise very challenging responsibilities. By building on recent developments by Hill of Hollnagel’s paradigm shifting approach, this paper has laid out how FRAM system modelling can provide not only the prediction and correction mechanism provided by the new machine learning FRAM facility demonstrated by Nomoto but can add resilience to the critical decision making involved. It puts forward the case that it is certainly worth pursuing and developing further.

Identification and prioritization of indicators of hospital bed allocation in Iran

AbstractThe distribution of hospital beds can be used as an indicator to assess the distribution of health services. This study aimed to determine and prioritize indicators of hospital bed allocation in Iran. In the first phase of the study, we used a scoping review to identify criteria affecting hospital bed allocation. Then, we applied an expert panel and the Delphi method to localize the criteria. Finally, we carried out Analytic Network Process method based on Decision Making Trial and Evaluation Laboratory technique (DANP), to determine the weight and importance of indicators and to evaluate the causal relationship between them. Based on the scoping review, we identified 75 criteria that influence bed allocation. In 2 Delphi cycles, 28 criteria were selected and included in the DANP phase. The population, the presence of a reference hospital in the region, the type of hospital, the common diseases in the region, and the presence of home care services were the variables that had the most interaction with other variables. The variable of home care services was at the highest level of the causal chain. Bed occupancy rate, average length of stay, waiting list, as well as type and level of the hospital had the highest priority in the allocation of hospital beds. Furthermore, given the importance of home care services, policies should be adopted to expand these services nationwide. The indicators identified in this study will be useful for the development of policies aimed at an equitable allocation of hospital beds.

RV4JaCa – Runtime Verification for Multi-Agent Systems

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. MAS have been used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. We demonstrate the implementation of a monitor that aims to control this dialogue flow in a MAS that communicates with the user through natural language to aid decision-making in hospital bed allocation.

A study of the evacuation and allocation of hospital beds during the Covid-19 epidemic: a case study in Iran

BackgroundShortage of resources, such as hospital beds, needed for health care especially in times of crisis can be a serious challenge for many countries. Currently, there is no suitable model for optimal allocation of beds in different hospital wards. The Data Envelopment Analysis method (DEA) has been used in the present study to examine the evacuation and allocation of hospital beds during the covid-19 pandemic in order to contribute to effective planning for fighting the spread the covid-19 virus.MethodsThe present study was conducted in two stages in hospitals affiliated with Urmia University of Medical Sciences (UUMS) in 2021. First, the number of excess beds was determined by calculating the technical efficiency using the DEA method and Deap2.1 software. To reallocate excess beds to covid-19 patients, the types of hospital wards were considered. As a result of this analysis, the inefficient hospitals with excess beds in different wards, which could be used for covid-19 patients with more serious symptoms, were identified.ResultsThe results of the study show that the average technical efficiency of the studied hospitals was 0.603. These hospitals did not operate efficiently in 2021 and their current output can be produced with less than 61% of the used input. Also, the potential of these hospitals, over a period of 1 year, for the evacuation of beds and reallocation of them to covid-19 patients was calculated to be 1781 beds, 450 of which belonged to general wards and 1331 belonged to specialized wards.ConclusionsThe DEA method can be used in the allocation of resources in hospitals. Depending on the type of hospital wards and the health condition of patients, this method can help policy-makers identify hospitals with the best potential but less emergency services for the purpose of reallocation of resources, which can help reduce the severe effects of crises on health resources.

Healthcare accessibility in preconflict Syria: a comparative spatial analysis

IntroductionThere is scarce information about the allocation of health resources in Syria. Pre-existing inequalities were further intensified after the 2011 conflict which displaced over 50% of the population. This study...

Validation of the Cepheid Xpert® Xpress SARS-CoV-2 using upper and lower respiratory tract specimens.

This study validated the performance of the reverse transcriptase‐polymerase chain reaction (rRT-PCR) based Cepheid Xpert® Xpress SARS-CoV-2 assay against the TIB MOLBIOL E-gene/EAV, a standard laboratory rRT-PCR SARS-CoV-2 assay. Upper and lower respiratory tract samples (nasopharyngeal and nasal swabs, bronchoalveolar lavage, and tracheal aspirate) were obtained from patients suspected to have contracted COVID-19. Results from the Xpert® Xpress and standard rRT-PCR assays were compared for positive and negative agreement and analyzed for precision, reproducibility, 95% confidence intervals, and coefficients of variation. The Xpert® Xpress assay demonstrated 100% agreement with the standard lab rRT-PCR for both upper and lower respiratory tract samples. Both the Xpert® Xpress and lab rRT-CPR identified weakly positive (Ct values 35–39) sample replicates with 100% reproducibility and showed 100% precision in identifying triplicates of upper respiratory tract samples. The single-cartridge Xpert® Xpress system has a short turnaround time and can be employed to improve patient management and hospital bed allocation. Further verification of the system is required before implementation and consideration must be paid to its higher cost and impracticality for high-throughput use.

Token Economy-Based Hospital Bed Allocation to Mitigate Information Asymmetry: Proof-of-Concept Study Through Simulation Implementation.

BackgroundHospital bed management is an important resource allocation task in hospital management, but currently, it is a challenging task. However, acquiring an optimal solution is also difficult because intraorganizational information asymmetry exists. Signaling, as defined in the fields of economics, can be used to mitigate this problem.ObjectiveWe aimed to develop an assignment process that is based on a token economy as signaling intermediary.MethodsWe implemented a game-like simulation, representing token economy–based bed assignments, in which 3 players act as ward managers of 3 inpatient wards (1 each). As a preliminary evaluation, we recruited 9 nurse managers to play and then participate in a survey about qualitative perceptions for current and proposed methods (7-point Likert scale). We also asked them about preferred rewards for collected tokens. In addition, we quantitatively recorded participant pricing behavior.ResultsParticipants scored the token economy–method positively in staff satisfaction (3.89 points vs 2.67 points) and patient safety (4.38 points vs 3.50 points) compared to the current method, but they scored the proposed method negatively for managerial rivalry, staff employee development, and benefit for patients. The majority of participants (7 out of 9) listed human resources as the preferred reward for tokens. There were slight associations between workload information and pricing.ConclusionsSurvey results indicate that the proposed method can improve staff satisfaction and patient safety by increasing the decision-making autonomy of staff but may also increase managerial rivalry, as expected from existing criticism for decentralized decision-making. Participant behavior indicated that token-based pricing can act as a signaling intermediary. Given responses related to rewards, a token system that is designed to incorporate human resource allocation is a promising method. Based on aforementioned discussion, we concluded that a token economy–based bed allocation system has the potential to be an optimal method by mitigating information asymmetry.

Providing a Model for the Optimal Allocation of Hospital Beds Based on Markov Chain Approach (Case Study: Shiraz-Shahid-Faghihi Hospital)

Background: As was observed in the corona crisis, in situations, such as war or natural disasters or epidemic diseases, the intensity of the applicants for medical services causes congestion problems. In this situation, due to the limited capacity of the system, queuing phenomenon for service applicants and in some cases, rejection of clients occur. Reducing the length of hospital stays by improving performance productivity can compensate for the shortage of hospital beds. In order to increase the productivity of personnel and equipment, it is necessary to eliminate unemployment and improve service scheduling. One of the ways to achieve these goals is to optimize the distribution of beds between wards. In the present study, in the form of Markov chain approach, according to the referral rate and service rate, the existing beds were allocated to different wards of the hospital to maximize service and minimize rejection of patients. Methods: The present study is an applied study conducted in 2019 for the optimal distribution of beds between the 3 wards of Shahid Faghihi Hospital in Shiraz. The research problem was modeled in the form of Markov chain approach and assuming the referral of clients according to the continuous-time Markov chain, the model parameters value was identified. The obtained mathematical model was solved by GAMS 24.1.3 software. Results: The proposed model led to an improvement in ward performance in terms of reducing patient waiting time and increasing the number of admitted patients. The proposed model reduced patient rejection by 8.6 %. According to the patients' referral rate to the wards and the service rate of each ward, based on sensitivity analysis, the number of beds allocated to each of the 3 wards was determined. Conclusion: Queuing theory can be applied as a tool to analyze the phenomena of the treatment system and determine the features of the waiting time, queue length, and capacity of the system. Appropriate allocation of hospital beds results in improving the efficiency and decreasing the patient rejection. Therefore, it could be useful in crisis, congestion in patients, and when increasing facilities is required.

Um olhar crítico à descentralização de serviços públicos de saúde em atenção hospitalar

Este trabalho objetiva identificar o panorama de contratação do setor privado sem fins lucrativos pelo Sistema Único de Saúde para realização de serviços de saúde de média e alta complexidade em atenção hospitalar, e a relação desse panorama com a diretriz de descentralização do sistema, que deve garantir atendimentos dos grandes centros urbanos às zonas onde os serviços públicos são mais escassos. Questiona-se se a contratação expandida de serviços hospitalares privados representa um afastamento das direções nacional e estadual da execução dos serviços de saúde a partir da municipalização generalizada. A metodologia de pesquisa é exploratória e descritiva, com base bibliográfica na literatura dos direitos Sanitário e Administrativo. A partir da análise dos gastos públicos com serviços hospitalares,nos últimos cinco anos, identifica-se que as entidades sem fins lucrativos tiveram mais investimento em produção hospitalar do que as esferas da administração pública direta e indireta de todos os entes. A título de contribuição para a busca de soluções para o problema, conclui-se que a menor participação da gestão nacional do SUS na prestação de serviços de saúde acarreta maior compra de serviços da iniciativa privada pelos entes, descumprindo a determinação constitucional de complementariedade da participação do setor privado no SUS. Ademais, a prestação de serviços hospitalares nesses moldes promove a falta de equilíbrio na alocação de leitos hospitalares, comprometendo a igualdade de condições no acesso às ações e aos serviços de saúde, objetivo da descentralização, e o adequado funcionamento do sistema.

Filling the gaps in the characterization of the clinical management of COVID-19: 30-day hospital admission and fatality rates in a cohort of 118 150 cases diagnosed in outpatient settings in Spain.

BackgroundCurrently, there is a missing link in the natural history of COVID-19, from first (usually milder) symptoms to hospitalization and/or death. To fill in this gap, we characterized COVID-19 patients at the time at which they were diagnosed in outpatient settings and estimated 30-day hospital admission and fatality rates.MethodsThis was a population-based cohort study. Data were obtained from Information System for Research in Primary Care (SIDIAP)—a primary-care records database covering >6 million people (>80% of the population of Catalonia), linked to COVID-19 reverse transcriptase polymerase chain reaction (RT-PCR) tests and hospital emergency, inpatient and mortality registers. We included all patients in the database who were ≥15 years old and diagnosed with COVID-19 in outpatient settings between 15 March and 24 April 2020 (10 April for outcome studies). Baseline characteristics included socio-demographics, co-morbidity and previous drug use at the time of diagnosis, and polymerase chain reaction (PCR) testing and results. Study outcomes included 30-day hospitalization for COVID-19 and all-cause fatality.ResultsWe identified 118 150 and 95 467 COVID-19 patients for characterization and outcome studies, respectively. Most were women (58.7%) and young-to-middle-aged (e.g. 21.1% were 45–54 years old). Of the 44 575 who were tested with PCR, 32 723 (73.4%) tested positive. In the month after diagnosis, 14.8% (14.6–15.0) were hospitalized, with a greater proportion of men and older people, peaking at age 75–84 years. Thirty-day fatality was 3.5% (95% confidence interval: 3.4% to 3.6%), higher in men, increasing with age and highest in those residing in nursing homes [24.5% (23.4% to 25.6%)].ConclusionCOVID-19 infections were widespread in the community, including all age–sex strata. However, severe forms of the disease clustered in older men and nursing-home residents. Although initially managed in outpatient settings, 15% of cases required hospitalization and 4% died within a month of first symptoms. These data are instrumental for designing deconfinement strategies and will inform healthcare planning and hospital-bed allocation in current and future COVID-19 outbreaks.

Ethical considerations for allocation of scarce resources and alterations in surgical care during a pandemic.

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in modern history. Its effects on social behavior and health care delivery have been dramatic. The resultant burden of disease and critical illness has outpaced the diagnostic, therapeutic, and health care professional resources of many clinics and hospitals. It continues to do so globally. The allocation of hospital beds and ventilators, personal protective equipment, investigational therapeutics, and other scarce resources has required difficult decisions. Clinical and surgical practices which are standard in normal times may not be standard or safe during the COVID-19 crisis. How can we best adapt as physicians and surgeons? What foundational ethical principles and systems of principle application can help guide our decision-making? Fortunately, a large body of work in medical ethics addresses these questions. Unfortunately, many surgeons and other health care professionals are probably not as familiar with these concepts. This brief communication is intended to provide a concise explanation of ethical considerations which readers may find helpful when addressing allocation of scarce resources and alterations in surgical care brought on by the current pandemic.