Bed Management Research Articles

Investigating Challenges of Bed Management in Referral Teaching Hospitals in Mashhad

Background: Access to services is one of the main intermediate outcomes of health system which directly affects people's health. Increasing demand for services, on the one hand, and insufficiency of existing beds according to the relevant standards in many hospitals in the country, on the other hand, necessitate the optimal use of existing beds. The present study aims to investigate the challenges of bed management in the referral educational hospitals of Mashhad.
 Methods: This was a qualitative study using content analysis method. The study population consisted of managers working in Imam Reza and Ghaem hospitals, as referral hospitals in Mashhad, and staff managers. Authors continued data collection until saturation was reached. Researchers used in-depth and semi-structured interviews to collect data. For data analysis, the authors transcribed and coded interviews, and at the same time, collected data using MaxQDA 11 software.
 Results: Data were continuously compared in terms of similarities and differences to similar codes. They can be integrated together, leading to the creation of 7 main concepts. They included challenges regarding admission, bed allocation, sustainability, discharge, process, structure, and human resources.
 Conclusion: Attention to the challenges of bed management in this study can greatly assist the correct emergency organization and patient flow in the hospital. Authorities need to recognize the problems arising from bed management and movie towards solving them. By doing so, the health system will enjoy higher productivity, and the patients ' access to health care will be enhanced without the need to increase the cost.

Improving service efficiency and throughput of cardiac surgery patients using Monte Carlo simulation: a queueing setting.

Bed occupancy rate (BOR) is important for healthcare policymakers. Studies showed the necessity of using simulation approach when encountering complex real-world problems to plan the optimal use of resources and improve the quality of services. So, the aim of the present study is to estimate average length of stay (LOS), BOR, bed blocking probability (BBP), and throughput of patients in a cardiac surgery department (CSD) using simulation models. We studied the behavior of a CSD as a complex queueing system at the Farshchian Hospital. In the queueing model, customers were patients and servers were beds in intensive care unit (ICU) and post-operative ward (POW). A computer program based on the Monte Carlo simulation, using Python software, was developed to evaluate the behavior of the system under different number of beds in ICU and POW. The queueing simulation study showed that, for a fixed number of beds in ICU, BOR in POW decreases as the number of beds in POW increases and LOS in ICU increases as the number of beds in POW decreases. Also, based on the available data, the throughput of patients in the CSD during 800days was 1999 patients. Whereas, the simulation results showed that, 2839 patients can be operated in the same period. The results of the simulation study clearly demonstrated the behavior of the CSD; so, it must be mentioned, hospital administrators should design an efficient plan to increase BOR and throughput of patients in the future.

Quality and safety in home care with the Vidaza cooperation protocol

In a context of tense bed management in hospitals, the implementation of cooperation protocols between hospital services and home care providers has become necessary in recent years. The Albi hospital in the Tarn region has set itself the objective of developing care pathways in conjunction with the city in order to better structure the supply of care in the region and also to encourage exchanges between healthcare professionals. The "Vidaza (Azacitidine®) cooperation protocol for day hospital - home hospitalization" illustrates this approach in the field of oncology.

Machine-learning prediction for hospital length of stay using a French medico-administrative database

ABSTRACT Introduction: Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.Methods: Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC).Results: Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values <0.0001). The performance of the RF, GB and NN models (AUC ranged from 0.808 to 0.810) was superior to that of the LR model (AUC = 0.795); all p-values <0.0001. In contrast, LR was superior to CART (AUC = 0.786), p < 0.0001. The variable most predictive of the PLOS was the destination of the patient after hospitalization to other institutions. The typical clinical profile of these patients (17.5% of the sample) was the elderly patient, admitted in emergency, for a trauma, a neurological or a cardiovascular pathology, more often institutionalized, with more comorbidities notably mental health problems, dementia and hemiplegia.Discussion: The integration of ML, particularly the GB algorithm, may be useful for health-care professionals and bed managers to better identify patients at risk of PLOS. These findings underscore the need to strengthen hospitals through targeted allocation to meet the needs of an aging population.

Evaluation of citrus grove floor management strategies for water use efficiency and conservation

Perennial crops require water year-round, straining water resources, particularly in arid and semi-arid regions throught the world. Of those regions, the Lower Rio Grande Valley of Texas, USA, depends on flood irrigation to meet water demands of crops. Approximately 60–70% of citrus producers use flood irrigation to irrigate their groves, which is less efficient than other irrigation methods such as micro-jet or drip irrigation. Flood irrigation is to reduce cost, equipment, and energy required, and no land needs to be taken out of production to be used for holding ponds. Therefore, conservation practices that are adapted for flood irrigation are necessary in these areas where infrastructure is less conducive to other practices. Water use efficiency and potential water savings in different grove-floor management strategies, citrus tree growth, soil moisture, and irrigation volume were measured over the course of 3 years (from establishment to first harvest) in four different grove floor management strategies. These strategies, flat-bed no plastic mesh groundcover (traditional), flat-bed with plastic mesh groundcover, raised bed no groundcover, and raised bed with groundcover showed varying impacts on young citrus tree growth, soil moisture, and water savings over this study period. By the end of the study, 2.2% water savings (44,403 L over 4 years) were found in the flat bed with groundcover treatment compared to traditional grove floor management. This treatment also resulted in higher water use efficiency and greater initial growth of young citrus. Overall, the use of ground cover showed much potential for conserving water compared to traditional, uncovered flat bed management methods. These grove floor management practices can improve tree growth and establishment in young citrus trees in addition to improving water use efficiency. However, it is yet to see how these benefits will translate to more mature trees.

Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion

In healthcare, Intensive Care Unit (ICU) bed management is a necessary task because of the limited budget and resources. Predicting the remaining Length of Stay (LoS) in ICU and mortality can assist clinicians in managing ICU beds efficiently. This study proposes a deep learning method based on several successive Temporal Dilated Separable Convolution with Context-Aware Feature Fusion (TDSC-CAFF) modules, and a multi-view and multi-scale feature fusion for predicting the remaining LoS and mortality risk for ICU patients. In each TDSC-CAFF module, temporal dilated separable convolution is used to encode each feature separately, and context-aware feature fusion is proposed to capture comprehensive and context-aware feature representations from the input time-series features, static demographics, and the output of the last TDSC-CAFF module. The CAFF outputs of each module are accumulated to achieve multi-scale representations with different receptive fields. The outputs of TDSC and CAFF are concatenated with skip connection from the output of the last module and the original time-series input. The concatenated features are processed by the proposed Point-Wise convolution-based Attention (PWAtt) that captures the inter-feature context to generate the final temporal features. Finally, the final temporal features, the accumulated multi-scale features, the encoded diagnosis, and static demographic features are fused and then processed by fully connected layers to obtain prediction results. We evaluate our proposed method on two publicly available datasets: eICU and MIMIC-IV v1.0 for LoS and mortality prediction tasks. Experimental results demonstrate that our proposed method achieves a mean squared log error of 0.07 and 0.08 for LoS prediction, and an Area Under the Receiver Operating Characteristic Curve of 0.909 and 0.926 for mortality prediction, on eICU and MIMIC-IV v1.0 datasets, respectively, which outperforms several state-of-the-art methods.

Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses

Poultry farming plays a key role in agricultural air emissions. Particulate matter (PM) level tends to be high in broiler and cage-free layer houses, that may impair health and welfare of animals and their caretakers. To protect public health and welfare, the occupational exposure limit for PM10 and PM2.5 (i.e., PM diameters that are generally ≤10 and 2.5 μm, respectively) are suggested not to exceed 150 µg m−3 and 35 µg m−3, respectively, based on 24-h concentrations thresholds as suggested by US. EPA. However, the levels of PM10 and PM2.5 in poultry houses could be 100 times higher than that limit. For instance, PM10 and PM2.5 levels in cage-free henhouses are higher than 15,000 µg/m3 and 3500 µg/m3 in wintertime. Therefore, it is critical to identify the primary factors affecting PM generation in poultry houses and apply corresponding mitigation strategies. This review paper summarizes PM emission factors, mitigating strategies, and impacts on birds’ and caretakers’ health, and welfare. Generally, PM emissions are affected by various factors, including housing types, seasonal and diurnal variation, manure management, bedding materials, ventilation rates, and birds’ activities. High PM concentrations in poultry houses impair birds’ and caretakers’ liver, kidneys, and respiratory systems. Thus, different mitigating strategies are discussed in this study for addressing those issues. Effective mitigation strategies include frequent house cleaning, optimum light intensity, liquid spraying, bedding management, and air filtration systems. However, mitigation strategies can be cost-prohibitive and have side effects. Therefore, poultry farms should select mitigation strategies based on farm location, climate conditions, environmental policies, and available resources (government assistance programs).

Phase-Type Survival Trees to Model a Delayed Discharge and Its Effect in a Stroke Care Unit

The problem of hospital patients’ delayed discharge or ‘bed blocking’ has long been a challenge for healthcare managers and policymakers. It negatively affects the hospital performance metrics and has other severe consequences for the healthcare system, such as affecting patients’ health. In our previous work, we proposed the phase-type survival tree (PHTST)-based analysis to cluster patients into clinically meaningful patient groups and an extension of this approach to examine the relationship between the length of stay in hospitals and the destination on discharge. This paper describes how PHTST-based clustering can be used for modelling delayed discharge and its effects in a stroke care unit, especially the extra beds required, additional cost, and bed blocking. The PHTST length of stay distribution of each group of patients (each PHTST node) is modelled separately as a finite state continuous-time Markov chain using Coxian-phase-type distributions. Delayed discharge patients waiting for discharge are modelled as the Markov chain, called the ‘blocking state’ in a special state. We can use the model to recognise the association between demographic factors and discharge delays and their effects and identify groups of patients who require attention to resolve the most common delays and prevent them from happening again. The approach is illustrated using five years of retrospective data of patients admitted to the Belfast City Hospital with a stroke diagnosis.

Management of hospital beds and ventilators in the Gauteng province, South Africa, during the COVID-19 pandemic.

We conducted an observational retrospective study on patients hospitalized with COVID-19, during March 05, 2020, to October 28, 2021, and developed an agent-based model to evaluate effectiveness of recommended healthcare resources (hospital beds and ventilators) management strategies during the COVID-19 pandemic in Gauteng, South Africa. We measured the effectiveness of these strategies by calculating the number of deaths prevented by implementing them. We observed differ ences between the epidemic waves. The length of hospital stay (LOS) during the third wave was lower than the first two waves. The median of the LOS was 6.73 days, 6.63 days and 6.78 days for the first, second and third wave, respectively. A combination of public and private sector provided hospital care to COVID-19 patients requiring ward and Intensive Care Units (ICU) beds. The private sector provided 88.4% of High care (HC)/ICU beds and 49.4% of ward beds, 73.9% and 51.4%, 71.8% and 58.3% during the first, second and third wave, respectively. Our simulation results showed that with a high maximum capacity, i.e., 10,000 general and isolation ward beds, 4,000 high care and ICU beds and 1,200 ventilators, increasing the resource capacity allocated to COVID- 19 patients by 25% was enough to maintain bed availability throughout the epidemic waves. With a medium resource capacity (8,500 general and isolation ward beds, 3,000 high care and ICU beds and 1,000 ventilators) a combination of resource management strategies and their timing and criteria were very effective in maintaining bed availability and therefore preventing excess deaths. With a low number of maximum available resources (7,000 general and isolation ward beds, 2,000 high care and ICU beds and 800 ventilators) and a severe epidemic wave, these strategies were effective in maintaining the bed availability and minimizing the number of excess deaths throughout the epidemic wave.

Associations of calf management practices with antimicrobial use in Canadian dairy calves.

The objective of this cross-sectional study was to investigate the relationship between management practices and antimicrobial use in heifer calves on Canadian dairy farms. Questionnaires on calf management practices, herd characteristics, and calf treatment records were administered on 147 dairy farms in 5 provinces during annual farm visits in a multiyear, nationwide research project (Canadian Dairy Network for Antimicrobial Stewardship and Resistance: CaDNetASR). Questions focused on the calf caregiver, calving pen, colostrum management, milk feeding, grouping, bedding management, and age when male calves were sold. Antimicrobial treatment records were collected on each farm from either an electronic herd management system or paper-based records. Newborn heifers born in the last 12 mo were identified retrospectively and followed to 60 d of age, with antimicrobial treatments and dates of sale or death extracted for further analysis. A multivariable linear regression model was developed with the natural log of the number of antimicrobial treatments per calf-year as the dependent variable, and categorized calf management practices and farm characteristics as the independent variables. A complete data set of records on 7,817 calves was retrieved from 74 farms based on completeness of calf records. A total of 2,310 calves were treated at least once with an antimicrobial, and 7,307 individual antimicrobial treatments were recorded. Among the reasons for antimicrobial use, respiratory disease (54%) was most common, followed by diarrhea (20%), presence of a fever (3%), and umbilical disease (2%). Florfenicol (33% of recorded treatments), penicillin (23%), and trimethoprim-sulfamethoxazole (18%) were commonly used, whereas fluoroquinolones (4%), and ceftiofur (1%) were used less commonly. Farms (31%) commonly had 0-1.0 antimicrobial treatments/calf-year (median: 2.2 treatments/calf-year; interquartile range: 0.64-6.43 treatments/calf-year). Defined daily dose (DDD) per calf-year was calculated based on the Canadian bovine standards. Among the 74 farms, florfenicol (1.35 DDD/calf-year) and macrolides (0.73 DDD/calf-year) were used most, whereas ceftiofur (0.008 DDD/calf-year) was the lowest. The final multivariable linear regression model indicated that farms that fed transition milk had fewer than half the number of antimicrobial treatments per calf-year than those who did not feed transition milk. The number of antimicrobial treatments per calf-year in preweaning calves was low on many farms, and there was low use of highly important drugs for human medicine. The effect of feeding transition milk should be investigated regarding potential effects on antimicrobial use and disease prevention.

Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments

Having an increasing number of patients in the emergency department constitutes an obstacle to the admissions process and hinders the emergency department (ED)’s ability to deal with the continuously arriving demand for new admissions. In addition, forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. Therefore, this paper aims to develop a precise prediction model for admissions in the Integral Healthcare System for Public Use in Catalonia. These models assist in reducing overcrowding in emergency rooms and improve the quality of care offered to patients. Data from 60 EDs were analyzed to determine the likelihood of hospital admission based on information readily available at the time of arrival in the ED. The first part of the study targeted the obtention of models with high accuracy and area under the curve (AUC), while the second part targeted the obtention of models with a sensitivity higher than 0.975 and analyzed the possible benefits that could come from the application of such models. From the 3,189,204 ED visits included in the study, 11.02% ended in admission to the hospital. The gradient boosting machine method was used to predict a binary outcome of either admission or discharge.

Innovative Health Technologies to Improve Emergency Department Performance

Abstract Background Emergency Departments (EDs) are increasingly pivotal, with a constant increment in their use, despite stable or declining fundings. Crowding can lead to disruptions and the COVID-19 epidemic has further burdened ED services. However, the pandemic has seen an increased use of telemedicine and digital health tools, which may be notably beneficial for EDs. This study offers a review of the latest available digital health technologies and their effectiveness to improve ED performance. Methods We performed a narrative review to identify digital and technological innovations in EDs. The themes of interest were defined in 4 areas: Patient Assessment, Patient Experience, Resource Allocation, and Discharge. Data was analyzed by 5 independent reviewers who focused on different macro-areas. Disagreement on data was discussed with 2 independent tiebreakers. Results Our search yielded 25 articles addressing 4 topics: Patient Assessment, Resource Allocation, Patient Experience, Discharge. We found that digital tools and Artificial Intelligence are powerful tools to detect, collect, and process data from patients, to improve healthcare delivery in EDs. The Resource Allocation category showed to be key in optimizing services already in place. New technologies showed effective to improve Patient Experience by curbing pain and anxiety. Innovative technologies demonstrated efficacy after Discharge when patients need guidance from clinicians for follow-up care. Conclusions Our review shows evidence of increasing effectiveness of innovative tools in reducing wait time and improving performance and patient experience in EDs. Technology applied to resource allocation appeared to be the most effective category. Prediction algorithms could be used to improve workforce allocation and bed management. Critical care systems must meet the challenge of innovative technologies which can lead to a new era in healthcare delivery with improvements for patients and healthcare professionals. Key messages • Digital innovation will have a significant impact on several dimensions of healthcare in the near future. • Healthcare systems and EDs must meet the challenge of innovative technologies which can lead to a new era in healthcare delivery with improvements for both patients and healthcare professionals.

Modelling fast pyrolysis of biomass in a fluidized bed reactor

AbstractA compartmental one‐dimensional model of a fluidized bed pyrolytic converter of biomass is presented. Reference conditions are those of non‐catalytic fast pyrolysis of biomass in a shallow fluidized bed with external regeneration of the bed material. The fate of biomass and of the resulting char has been modelled by considering elutriation of biomass and char particles, char attrition as well as bed drain/regeneration. The course of primary and secondary pyrolitic reactions is modelled according to a semi‐lumped reaction network using well‐established kinetic parameters taken from the literature. A specific focus of the present study is the role of the heterogeneous volatile–char secondary reactions, whose rate has been modelled by borrowing a kinetic expression from the neighbouring area of tar adsorption/decomposition over char. The results of computations highlight the relevance of heterogeneous volatile–char secondary reactions and of the closely associated control of char loading in the bed. The sensitivity of the reactor performance to char elutriation and attrition, to proper management of bed drain/regeneration, and to control of gas phase backmixing is demonstrated. Model results provide useful guidelines for optimal design and control of fluidized bed pyrolyzers and pinpoint future research priorities.

Comparative evaluation of different bedding materials on growth performance of Murrah buffalo calves

Ideal bedding management is essential for optimal health, welfare, and growth performance of dairy calves. The study was conducted in order to compare the effect of different bedding materials on growth performance of Murrah buffalo calves. Murrah buffalo calves (n=20) of less than 4 months of age were selected and divided into four groups (5 animals/group), viz. T1: Concrete floor, T2: Concrete floor bedded with sand, T3: Concrete floor bedded with wheat straw and T4: Concrete floor bedded with rubber mat. Body weight and body measurement (body length, height of wither and heart girth) were taken at fortnightly and monthly intervals, respectively. There was no significant difference in body weight between treatment groups. However, average weight gain (kg) and ADG was significantly high in calves reared on T3 as compared to other treatment groups, though no significant difference was observed between T1, T2 and T4. Body length was significantly lower in T2 as compared to T3, however, difference in body length among T1, T2 and T4 groups was relatively non-significant. Moreover, body height and heart girth were significantly higher in calves reared on wheat straw floor as compared to concrete and sand bedded floor. Estimation of net profit per calf after reducing the bedding cost revealed that it was highest for wheat straw bedding followed by rubber mat, concrete and least for sand bedding. It can be concluded that wheat straw followed by rubber mat could be used effectively as bedding over concrete floor during winter season for rearing of calves.

Implementation of Pre-Admission Caregiver Testing for COVID-19.

Caregivers are often at the bedside of hospitalized children posing an additional risk for coronavirus disease 2019 (COVID-19) transmission. We describe the implementation of caregiver COVID-19 testing before inpatient pediatric admissions and the effect on patient cohorting and bed capacity. We implemented an ordering pathway to facilitate COVID-19 testing of caregivers of patients admitted to the inpatient units from the pediatric emergency department, elective procedural admissions, or direct admissions at a tertiary children's hospital in the Northeastern United States in August 2021. Testing was expedited by the clinical laboratory, and caregiver results were used to inform cohorting, infection prevention, and bed management decisions. From August 2021 to January 2022, 2558 caregiver tests were ordered through this pathway, and 83 (3.2%) were positive. Of the positive tests, 72 (86.7%) occurred after December 18, 2021, coinciding with the local Omicron variant wave. Among positives, 67 caregiver or child pairs were identified, and 36 positive caregivers had a COVID-19 negative child leading to use of isolation precautions. Reintroduction of patient cohorting increased overall bed capacity from 74% to 100% of available beds. The overall incidence of COVID-19 among caregivers before admission correlated well with rates of COVID-19 positivity among asymptomatic adults in the community during the study period. Implementation of caregiver testing increased bed capacity by reintroducing cohorting of patients and identified patients needing isolation that would have been missed by patient testing alone. More research is necessary to determine the extent that routine caregiver testing mitigates the risk of nosocomial severe acute respiratory syndrome coronavirus 2 transmission.

Burn Wound Bed Management.

Critical to the success of modern burn care is the management of the burn wound. Timely and complete removal of nonviable tissue is complicated by the irreplaceable nature of the tissue lost either through the burn injury or as "collateral damage" as part of the treatment. Challenges in distinguishing between viable and nonviable tissue and "replacing the irreplaceable" are discussed alongside potential disruptive technologies which could fundamentally change how burn care is delivered. Advances in burn wound bed management forms the foundation on which the goal of zero preventable death and disability after burn injury can be achieved.

EP-604 The Golden Patient Pathway - Improving Efficiency of Emergency General Surgery Theatres and Bed Management

Abstract Aim This closed loop audit aimed at improving efficiency of time management with good utilisation of the first hour of the day in emergency theatres. This was reflected on bed management and patient service. Methods Patients who required simple emergency operations were the target population. Retrospective data of patients’ demographics, time of attendance to surgical admission unit, time waiting for an operation, anaesthetic start times, surgical start times and postoperative inpatient stay were collected. Then the Golden patient pathway was implemented via a list of agreed criteria for the Golden patient (simple procedure, non-septic, non-diabetic, etc.). The chosen patient was labelled as Golden on the booking form. A precise time scale for anaesthetic review and theatre sending time was agreed. This pathway was tried for a month followed by data collection to compare outcomes. Results After starting the Golden patient pathway in our emergency surgery service, the mean anaesthetic start time (+/- standard deviation) improved from 09:12 +/- 0.11 to 08:32 +/- 0.01. Consequently, the mean operation start time improved from 09:45 +/- 0.10 to 08:53 +/- 0.01. The percentage of patients with same day discharge after simple procedures increased from 57% to 100% thus providing better use of hospital beds. Conclusion The Golden patient pathway provides a good utilisation of the first hour of the day in emergency theatres. It has a positive impact on patient service and bed management via reducing the waiting times. The Golden patient pathway is an asset to the emergency general surgery service.

UPAYA PARTISIPASI MASYARAKAT SEBAGAI KEMANDIRIAN EKONOMI DESA MENGHADAPI COVID 19 DESA JUNREJO KECAMATAN JUNREJO KOTA BATU

The COVID-19 pandemic has caused unplanned social changes as social changes that occur erratically and whose presence is not desired by the community. Village independence efforts and village community participation must be needed and have the initiative to create an economically independent community in the face of a pandemic situation. Covid 19 is to be faced by rural communities. The purpose of this research to find out the efforts of community participation as village economic independence in Covid 19 in Junrejo village, Junrejo district, Batu city and to find out what are the supporting and inhibiting factors in community participation efforts as an effort to achieve village economic independence in Covid 19 in Junrejo Village, Junrejo District, Batu City. This study uses the determination of informants, research instruments, snowball sampling, data collection techniques, and technical data analysis, namely data reduction, data presentation, and conclusion drawing. The results showed that the efforts of the community to ensure the independence of the village faced Covid-19 in the villager of Junrejo, namely: the emancipation of the community has changed for the better, especially when viewed from the point of view of community skills and creativity in the management of flower beds, which made it a source of income, adequate public institutions. Such as providing free internet in the form of village wifi to access information, selling agricultural products online, and village cooperatives to create an improved and better standard of living for the people of Junrejo

Cost of stay and characteristics of patients with stroke and delayed discharge for non-clinical reasons

Delayed discharge for non-clinical reasons (bed-blocking) is characteristic of pathologies associated with ageing, loss of functional capacity and dependence such as stroke. The aims of this study were to describe the costs and characteristics of cases of patients with stroke and delayed discharge for non-clinical reasons (bed-blocking) compared with cases of bed-blocking (BB) for other reasons and to assess the relationship between the length of total stay (LOS) with patient characteristics and the context of care. A descriptive cross-sectional study was conducted at a high complexity public hospital in Northern Spain (2007–2015). 443 stroke patients presented with BB. Delayed discharge increased LOS by approximately one week. The median age was 79.7 years, significantly higher than in cases of BB for other reasons. Patients with stroke and BB are usually older patients, however, when younger patients are affected, their length of stay is longer in relation to the sudden onset of the problem and the lack of adequate functional recovery resources or residential facilities for intermediate care.

Optimising the balance of acute and intermediate care capacity for the complex discharge pathway: Computer modelling study during COVID-19 recovery in England.

ObjectivesWhile there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for ‘step down’ intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions.MethodsDemand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays.ResultsIncreased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute ‘bed blocking’ is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway.ConclusionsComputer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code.