Bed Allocation Research Articles

Preliminary Evaluation of Market Mechanism-Based Bed Allocation System.

Effective bed management is important for hospital management. Until now, bed allocation process is generally controlled by administrative staffs in centralized manner but it is not always effective. In the present study, we proposed and evaluated new method for bed allocation applying market mechanism via token. Evaluation was performed with newly-developed game-type simulation. Nurse managers as research participants played it and answered for survey. The result showed that the proposed method can be useful with appropriate operational design.

The role of palliative medicine in ICU bed allocation in COVID-19: a joint position statement of the Singapore Hospice Council and the Chapter of Palliative Medicine Physicians.

Facing the possibility of a surge of COVID-19-infected patients requiring ventilatory support in Intensive Care Units (ICU), the Singapore Hospice Council and the Chapter of Palliative Medicine Physicians forward its position on the guiding principles that ought to drive the allocation of ICU beds and its role in care of these patients and their families.

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.

Adjusting Daily Inpatient Bed Allocation to Smooth Emergency Department Occupancy Variation.

Study Objective: Overcrowding in emergency departments (ED) is an increasingly common problem in Taiwanese hospitals, and strategies to improve efficiency are in demand. We propose a bed resource allocation strategy to overcome the overcrowding problem. Method: We investigated ED occupancy using discrete-event simulation and evaluated the effects of suppressing day-to-day variations in ED occupancy by adjusting the number of empty beds per day. Administrative data recorded at the ED of Taichung Veterans General Hospital (TCVGH) in Taiwan with 1500 beds and an annual ED volume of 66,000 visits were analyzed. Key indices of ED quality in the analysis were the length of stay and the time in waiting for outward transfers to in-patient beds. The model is able to analyze and compare several scenarios for finding a feasible allocation strategy. Results: We compared several scenarios, and the results showed that by reducing the allocated beds for the ED by 20% on weekdays, the variance of daily ED occupancy was reduced by 36.25% (i.e., the percentage of reduction in standard deviation). Conclusions: This new allocation strategy was able to both reduce the average ED occupancy and maintain the ED quality indices.

Profile of ICU Bed Requests at Helen Joseph Hospital

Abstract Background: Intensive care unit (ICU) beds are a scarce resource at Helen Joseph Hospital (HJH). A limited number of beds serve a population with a large burden of disease. Medical practitioners request ICU beds for patients they deem in need of ICU management. However, the decision to admit patients to the ICU remains the responsibility of the ICU consultant on call. No formal prognostic scoring system (such as Acute Physiological and Chronic Health Evaluation or Simplified Acute Physiology Score) or admission guidelines were in place when performing this study. Aim: To compile a profile of all the ICU admission requests at HJH. Methods: A contextual, prospective, descriptive research design was followed in this study. Data was collected during one winter and one summer month in 2012 using consecutive sampling from ICU consultation forms. Results: A total of 139 patients were included in the study. The median age was 44 years. The majority of patients (79%) were under the age of 60 years. The overall admission rate was 35.25% and the most common reason for admission was mechanical ventilation. Of the patients refused ICU admission, 41% were assessed as being too ill, 30% were assessed as too well and 29% were refused admission due to a lack of resources. Patients admitted to the ICU had a 77.55% survival rate. The relationship between ICU admission and 30-day improved outcome was statistically significant, with those being admitted to ICU having a better outcome. Conclusions: Overall a relatively young population is admitted to ICU at HJH. The allocation of beds between the disciplines is fairly equal, both with good survival benefits. The lack of ICU resources is an important limitation to ICU admission. Admission to ICU demonstrated a survival benefit.

Allocation of Intensive Care Unit Beds in Periods of High Demand

Intensive care unit (ICU) beds are valuable resources that are typically in short supply and therefore their effective and efficient use is essential particularly during periods when patient demand...

Data-Driven Models for Capacity Allocation of Inpatient Beds in a Chinese Public Hospital.

Hospital beds are a critical but limited resource shared between distinct classes of elective patients. Urgent elective patients are more sensitive to delays and should be treated immediately, whereas regular patients can wait for an extended time. Public hospitals in countries like China need to maximize their revenue and at the same time equitably allocate their limited bed capacity between distinct patient classes. Consequently, hospital bed managers are under great pressure to optimally allocate the available bed capacity to all classes of patients, particularly considering random patient arrivals and the length of patient stay. To address the difficulties, we propose data-driven stochastic optimization models that can directly utilize historical observations and feature data of capacity and demand. First, we propose a single-period model assuming known capacity; since it recovers and improves the current decision-making process, it may be deployed immediately. We develop a nonparametric kernel optimization method and demonstrate that an optimal allocation can be effectively obtained with one year's data. Next, we consider the dynamic transition of system state and extend the study to a multiperiod model that allows random capacity; this further brings in substantial improvement. Sensitivity analysis also offers interesting managerial insights. For example, it is optimal to allocate more beds to urgent patients on Mondays and Thursdays than on other weekdays; this is in sharp contrast to the current myopic practice.

Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic

During the current epidemic, it is necessary to ensure the rehabilitation treatment of children with serious illness. At the same time, however, it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting. To resolve this contradiction, the rehabilitation department of Nanjing Children’s Hospital adjusted its bed allocation based on the queuing model, with reference to the regional source and classification of the children’s conditions in the rehabilitation department ward. The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from other places. A M/G/2 queuing model with priority was also applied to analyze the state of patient admissions. Moreover, patients in Nanjing were also classified into mild and severe cases. The M/M/1 queuing model with priority was used for analysis of this situation, so that severely ill children could be treated in time while patients with mild symptoms could be treated at home. This approach not only eases the bed tension in the ward, but also provides suitable conditions for controlling cross-infection.

Management of bed availability in intensive care in the context of hospitalization by court order

Objective:to identify, from the nurse perspective, situations that interfere with the availability of beds in the intensive care unit in the context of hospitalization by court order.Method:qualitative exploratory, analytical research carried out with 42 nurses working in adult intensive care. The selection took place by non-probabilistic snowball sampling. Data collected by interview and analyzed using the Discursive Textual Analysis technique.Results:three categories were analyzed, entitled deficiency of physical structure and human resources; Lack of clear policies and criteria for patient admission and inadequate discharge from the intensive care unit. In situations of hospitalization by court order, there is a change in the criteria for the allocation of intensive care beds, due to the credibility of professionals, threats of medico-legal processes by family members and judicial imposition on institutions and health professionals.Conclusion:nurses defend the needs of the patients, too, with actions that can positively impact the availability of intensive care beds and adequate care infrastructure.

Artificial Bee Colony Algorithm for Solving Fuzzy Multi-Objective Bed Allocation Model

With the improvement of the medical services frameworks rivalry, hospitals face more and more challenges. In the interim, allotment of resource has a crucial influence on performing competitive benefits in a hospitals. To choose the suitable beds number is one of the most essential tasks in hospital administration. Anyway, in true condition, bed allotment choice is a multiple-side problem with weakness and haphazardness of the information available. It is so sophisticated. Therefore, the research about bed allotment difficulty is comparatively rare under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we improve a fuzzy multi-objective bed allotment pattern for defeating doubtfulness and various sections. Fuzzy objectives, and weights are at the same time used to assist the administrators to choose the appropriate beds about various departments. The suggested pattern uses Artificial Bee Colony (ABC), which is so efficient algorithm. The research portrays a use of the pattern in a public hospital in Iraq. The outcome has shown presented an appropriate system for bed allotment and ideal usage.

Inpatient bed allocation policy under uncertain capacity

This paper concerns the problem of inpatient bed allocation for two classes of patients (scheduled and non-scheduled) when there is uncertainty about daily available capacity. In the afternoon of each day, patients from the scheduled class, also called backlogged elective admissions, are selected from a waiting list, for the admission on the next day. The non-scheduled class, also called emergent admissions, are new requests that arise randomly each day with emergent needs. The capacity of available beds for a medical specialty to provide hospitalization services is uncertain when backlogged elective patients are chosen. Admitting too many of elective patients may result in exceeding a day’s capacity, which can potentially necessitate “overflowing” or “postponing” some emergent requests that should be performed as soon as possible. Therefore, the problem faced by the medical specialty facility at the decision-making point of each day is how many of the backlogged elective patients can be admitted. We formulate this problem as a Markov decision process (MDP) and study the structural properties of the model to characterize the nature of the optimal policy. We propose easy-to-implement policies (the fixed quota policy and the best fixed quota policy), which perform well under fitted distributions. By reporting numerical analyses using real data from a Chinese public hospital, we finally compare the improvements that our proposed solutions could bring to the hospital with the existing practices under several different cost structures.

127 Predicting Patient Disposition from the Emergency Department: A Prospective Comparison of Attending Physician Gestalt vs. an Automated Computer Model

Emergency department (ED) boarding is a widespread problem associated with negative clinical outcomes and decreased patient satisfaction. Earlier identification of patients likely to be hospitalized has the potential to reduce ED boarding by facilitating an earlier start to the bed allocation process. Our group recently published a computer-based model1 that relies on information contained in the electronic medical record to predict hospitalization from the pediatric ED, as early as 30 minutes from presentation. Our model was able to identify 50.6% of the hospitalizations at the moment of arrival, 60.0% after 10 minutes, and 73.4% after 30 minutes, and is now being piloted at Boston Children’s Hospital, South Shore Hospital, and Children’s Hospital of Los Angeles. A commonly cited shortcoming of computer models is that they fail to take into account clinical gestalt. We aim to assess the validity of the computer-based model against the clinical gestalt of attending physicians to predict hospitalization. Eight pediatric ED attending physicians participate in this study, with ongoing evaluation of 200 patients in total. Attending physicians assess patients while off duty and evaluate the likelihood of admission, purpose of admission (monitor, diagnose, treat, etc.), and key factors driving the prediction (eg, clinical history, patient appearance, etc), within 60 minutes of presentation to the ED. For all included patients, we compare the predictions of ED attending physicians to those generated by our existing computer model. Of the 16 patients who have been assessed to date, 8 were hospitalized and 8 were discharged. Our computer model predicted that 11/16 patients have more than a 60% chance of admission. Of these, 8 were indeed admissions (100% sensitivity). ED attending physicians predicted that 5/16 patients have more than 60% chance of admission. Of these, 4 were indeed admissions (50% sensitivity). Additional data collection is ongoing and will be completed by August 2019. Our analysis tests clinician gestalt as an alternative means for predicting hospitalization. Our results will either strengthen our reliance on the computer model, if it out-performs clinical gestalt, or enable us to identify opportunities to improve the computer model and refine its predictive algorithms.

A Clustered Overflow Configuration of Inpatient Beds in Hospitals

Problem definition: The shortage of inpatient beds is a major cause of delays and cancellations in many hospitals. It may also lead to patients being admitted to inappropriate wards, resulting in a lower quality of care and a longer length of stay. Academic/practical relevance: Investment in additional beds is not always feasible. Instead, new and creative solutions for a more efficient use of existing resources must be sought. Methodology: We propose a new configuration of inpatient beds, which we call the clustered overflow configuration. In this configuration, patients who are denied admission to their primary wards as a result of beds being fully occupied are admitted to overflow wards, with each designated to serve overflows from a certain subset of specialties and providing the same quality of care as in primary wards. We propose two different formulations for partitioning and bed allocation in the proposed configuration: one minimizing the sum of average daily costs of turning patients away and nursing teams, and another minimizing the numbers turned away subject to nursing cost falling below a given threshold. We heuristically solve instances from both formulations. Results: Applying the models to real data shows that the configurations obtained from our models compare very well with the other configurations proposed in the literature, provided that patients’ willingness to wait is relatively short. Managerial implications: The proposed configuration provides the combined advantages of the dedicated configuration, wherein patients are only admitted to their primary wards, and the flexible configuration, in which all specialties share a single ward. On the other hand, it restricts the adverse impacts of pooling and minimizes cross-training costs through appropriate partitioning and bed allocation. As such, it serves as a viable alternative to existing inpatient configurations.

Improving the wait time to admission by reducing bed rejections

Congestion at the emergency department (ED) is associated with increased wait times, morbidity and mortality. We have identified prolonged wait time to admission as a significant contributor to ED congestion....

Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models

Problem definition: This research focuses on elderly patients who have been hospitalized and are ready to be discharged, but they must remain in the hospital until a bed in a geriatric institution becomes available; these patients “block” a hospital bed. Bed blocking has become a challenge to healthcare operators because of its economic implications and the quality-of-life effect on patients. Indeed, hospital-delayed patients who do not have access to the most appropriate treatments (e.g., rehabilitation) prevent new admissions. Moreover, bed blocking is costly, because a hospital bed is more expensive to operate than a geriatric bed. We are thus motivated to model and analyze the flow of patients between hospitals and geriatric institutions to improve their joint operation. Academic/practical relevance: Practically, our joint modeling of hospital-institution is necessary to capture blocking effects. In contrast to previous research, we address an entire time-varying network, which enables an explicit consideration of blocking costs. Theoretically, our fluid model captures blocking without the need for reflection, which simplifies the analysis as well as the convergence proof of the corresponding stochastic model. Methodology: We develop a mathematical fluid model, which accounts for blocking, mortality, and readmission—all significant features of the discussed environment. Then, for bed allocation decisions, the fluid model and especially, its offered load counterpart turn out insightful and easy to implement. Results: The comparison between our fluid model, a two-year data set from a hospital chain, and simulation results shows that our model is accurate and useful. Moreover, our analysis yields a closed form expression for bed allocation decisions, which minimizes the sum of underage and overage costs. Solving for the optimal number of geriatric beds in our system shows that significant reductions in cost and waiting list length are achievable compared with current operations. Managerial implications: Our model can support healthcare managers in allocating geriatric beds to reduce operational costs. Moreover, our model facilitates three extensions: a periodic reallocation of beds, incorporation of setup costs into bed allocation decisions, and accommodating home care (or virtual hospitals) when feasible.

Modeling multi-stage healthcare systems with service interactions under blocking for bed allocation

Balancing bed allocation is a critical but cumbersome decision-making process in hospitals due to limited capacity, fluctuations in the rate of patient arrival and service interactions among various units; typically, this will cause blockages in multi-stage healthcare services. Accurately estimating the blocking probability is an important task in order to improve the performance of healthcare systems. Early studies assumed either unlimited bed capacity or no service interaction among units. In this study, we consider the correlation between the blockage and service time of the subsequent stage and apply a multi-stage tandem-queuing model with limited bed capacity and service interactions to model healthcare systems. We develop two effective heuristics to estimate the patient-blocking probability, which are then used to develop an integrated mathematical model for bed allocation. We collect real-world data from a tertiary hospital in China to delineate the effect of service interactions while estimating the blocking probability and use non-parametric rank-sum tests to verify and compare the relative performances of the proposed model against two popular heuristics. Our comparative results illustrate that the proposed model is as accurate as simulations. We also observe that increasing the number of beds during the first stage is more effective in reducing blockage than doing so later in case of a limited number of beds.

A Data-Driven Hybrid Three-Stage Framework for Hospital Bed Allocation: A Case Study in a Large Tertiary Hospital in China.

Beds are key, scarce medical resources in hospitals. The bed occupancy rate (BOR) amongst different departments within large tertiary hospitals is very imbalanced, a situation which has led to problems between the supply of and the demand for bed resources. This study aims to balance the utilization of existing beds in a large tertiary hospital in China. We developed a data-driven hybrid three-stage framework incorporating data analysis, simulation, and mixed integer programming to minimize the gaps in BOR among different departments. The first stage is to calculate the length of stay (LOS) and BOR of each department and identify the departments that need to be allocated beds. In the second stage, we used a fitted arrival distribution and median LOS as the input to a generic simulation model. In the third stage, we built a mixed integer programming model using the results obtained in the first two stages to generate the optimal bed allocation strategy for different departments. The value of the objective function, Z, represents the severity of the imbalance in BOR. Our case study demonstrated the effectiveness of the proposed data-driven hybrid three-stage framework. The results show that Z decreases from 0.7344 to 0.0409 after re-allocation, which means that the internal imbalance has eased. Our framework provides hospital bed policy makers with a feasible solution for bed allocation.

Abstract 126: Putting Into Practice: American Heart Association 2017 Standards for Hospital Electrocardiographic Monitoring and Inappropriate Telemetry Use

Background: Initially hospital electrocardiographic monitoring (telemetry) was introduced for heart rate and rhythm monitoring in the CCU. It has expanded, but with the caveat of increased inappropriate use. In one study 1 of 236 admissions, 50% of the 745 monitoring days were not indicated by ACC/AHA guidelines. Similar studies 2 have been done, but these were done prior to the release of the 2017 AHA Updates on the Standards of Inpatient Electrocardiographic monitoring 3 . Our intent was to evaluate the power of education alone, and the ability thus to implement recent expert recommendations into current practice. Methods: A retrospective evaluation was done (chart review over 3 months) for patients admitted to telemetry units to then assess the appropriateness of telemetry usage. Intervention was through the education on the appropriate indications and duration of inpatient telemetry as per the “Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings 3 .” Education included providing the guidelines and indications to physicians/medical staff. After intervention, charts were reviewed over the next 3 months to evaluate for changes. The study was created as a cyclical/rolling study. Results: Out of the 166 charts reviewed from the pre intervention group, 61 involved patients on telemetry, and 14/61 (23%) was the percentage of inappropriate telemetry usage. In the post-intervention group, 248 charts reviewed, 94 involved patients on telemetry, and inappropriate telemetry usage was 14/94 (15%). The most common reasons for inappropriate telemetry use: hemodynamically stable GI bleed (6/28), alcohol withdrawal (4/28), and hypertensive urgency (6/28). Between the two groups there was an 8% decrease, showing more appropriate use of telemetry after intervention (statistical significance, p-value=0.2). Bed allocation however specifically improved along with several other end-points. Conclusion: The 8% decrease in the inappropriate telemetry usage created better bed allocation from the emergency department (ED) and led to decreased ED wait times and earlier transfers of patients from the intensive care unit. The overall results were in accordance with our expectations as we were aware of the limitations (length of study time, single intervention of education). However, the sole education of the AHA 2017 standards 3 led to desired trends of decreased in inappropriate telemetry use and opening several avenues to augment patient care and placement, liberation of multiple medical departments to deliver more focused care, and overall improvement in hospital work flow and cost-effectiveness. With interdepartmental efforts, use of technology, protocols and more, the current practices will become all the more likely to mirror the recommended standards. We thus plan to continue to move on to the further stages of our model to reveal the summative endpoints achieved.

Improving decision making in acute healthcare through implementation of an intensive care unit (ICU) intervention in Australia: a multimethod study

ObjectiveTo evaluate the implementation of an intensive care unit (ICU) intervention designed to establish rules for making ICU decisions about postsurgery beds.DesignPreintervention/postintervention case study using a multimethod approach, involving two...

A simulation study of bed allocation to reduce blocking probability in emergency departments: A case study in China

Improving the bed allocation policy can alleviate the blocking, a situation where admitted patients cannot be transferred from the Emergency Departments (the upstream) to inpatient departments (the downstream) due to unavailability of beds. We aim to find a bed allocation policy that reduces the blocking probability and improves bed utilisation considering the interaction between the neighboring departments, as well as the priorities of different departments. This concept and the proposed model are tested in a real-life case at a hospital in China. A two-stage bed allocation simulation model is developed to evaluate the effect of bed assignment on the blocking probability and bed utilisation. Results show that the blocking probabilities can be reduced by 1.32%–8.98% and 1.74%–4.16% under different priority assignment cases and total number of beds. Sensitivity analyses are performed to examine the effect of varying patient arrival rate on the performance of bed allocation policy.