Optimization of the Use of Hospital Beds as an Example of Improving the Functioning of Hospitals in Poland on the Basis of the Provincial Clinical Hospital No. 1 in Rzeszow.

An efficient health care system combines maximum accessibility with high-quality treatments, as well as cost optimization of individual health care facilities throughout the entire system. In hospitals, the critical element is the number of beds within individual wards, which generates costs and, at the same time, affects the capacity to serve patients. The aim of this article is to discuss the restructuring and optimization of hospital bed occupancy in a healthcare facility in the Podkarpackie voivodeship. The analysis covers the years 1999-2018. In the indicated period, the analyzed healthcare institution restructured the number of beds based on a forecast of the demand for services, which resulted in positive cost effects, without limiting patients' access to diagnostic and therapeutic care. The analyzed facility took part in a common trend of optimizing cost-effectiveness and efficiency of hospital operations in Poland.

Artificial Intelligence Forecasting Census and Supporting Early Decisions.

Matching resources to demand is a daily challenge for hospital leadership. In interdisciplinary collaboration, nurse leaders and data scientists collaborated to develop advanced machine learning to support early proactive decisions to improve ability to accommodate demand. When hundreds or even thousands of forecasts are made, it becomes important to let machines do the hard work of mathematical pattern recognition, while efficiently using human feedback to address performance and accuracy problems. Nurse leaders and data scientists collaborated to create a usable, low-error predictive model to let machines do the hard work of pattern recognition and model evaluation, while efficiently using nurse leader domain expert feedback to address performance and accuracy problems. During the evaluation period, the overall census mean absolute percentage error was 3.7%. ALEx's predictions have become part of the team's operational norm, helping them anticipate and prepare for census fluctuations. This experience suggests that operational leaders empowered with effective predictive analytics can take decisive proactive staffing and capacity management choices. Predictive analytic information can also result in team learning and ensure safety and operational excellence is supported in all aspects of the organization.

Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method.

In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census.

A decision support simulation model for bed management in healthcare.

PURPOSE: In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions. DESIGN/METHODOLOGY/APPROACH: A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department's performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients. FINDINGS: Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds. ORIGINALITY/VALUE: This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

The self-regulating nature of occupancy in ICUs: stochastic homoeostasis.

As pressure on the health system grows, intensive care units (ICUs) are increasingly operating close to their capacity. This has led a number of authors to describe a link between admission and discharge behaviours, labelled variously as: 'bumping', 'demand-driven discharge', 'premature discharge' etc. These labels all describe the situation that arises when a patient is discharged to make room for the more acute arriving patient. This link between the admission and discharge behaviours, and other potential occupancy-management behaviours, can create a correlation between the arrival process and LOS distribution. In this paper, we demonstrate the considerable problems that this correlation structure can cause capacity models built on queueing theory, including discrete event simulation (DES) models; and provide a simple and robust solution to this modelling problem. This paper provides an indication of the scope of this problem, by showing that this correlation structure is present in most of the 37 ICUs in Australia. An indication of the size of the problem is provided using one ICU in Australia. By incorrectly assuming that the arrival process and LOS distribution are independent (i.e. that the correlation structure does not exist) for an occupancy DES model, we show that the crucial turn-away rates are markedly inaccurate, whilst the mean occupancy remains unaffected. For the scenarios tested, the turn-away rates were over-estimated by up to 46 days per year. Finally, we present simple and robust methods to: test for this correlation, and account for this correlation structure when simulating the occupancy of an ICU.

Bed management team with Kanban web-based application.

OBJECTIVE: To measure the effectiveness of the bed management process that uses a web-based application with Kanban methodology to reduce hospitalization time of hospitalized patients. DESIGN: Before-after study was performed. SETTING: The study was conducted between July 2013 and July 2017, at the Unimed Regional Hospital of Fortaleza, which has 300 beds, of which 60 are in the intensive care unit (ICU). It is accredited by International Society for Quality in Healthcare. POPULATION: Patients hospitalized in the referred period. INTERVENTION: Bed management with an application that uses color logic to signal at which stage of high flow the patients meet, in which each patient is interpreted as a card of the classical Kanban theory. It has an automatic user signaling system for process movement, and a system for monitoring and analyzing discharge forecasts. MAIN OUTCOME MEASURES: Length of hospital stay, number of customer complaints related to bed availability. RESULTS: After the intervention, the hospital's overall hospital stay time was reduced from 5.6 days to 4.9 days (P = 0.001). The units with the greatest reduction were the ICUs, with reduction from 6.0 days to 2.0 (P = 0.001). The relative percentage of complaints regarding bed availability in the hospital fell from 27% to 0%. CONCLUSION: We conclude that the use of an electronic tool based on Kanban methodology and accessed via the web by a bed management team is effective in reducing patients' hospital stay time.

Patient and family description of receiving care in acuity adaptable care model.

AIM: To explore patient and family perspectives of hospital care in an acuity adaptable care model implemented in an urban, public safety-net hospital. BACKGROUND: Specialty care units result in reactionary bed management. Changes in acuity generate costly, disruptive, intra-hospital patient transfers, which negatively affect clinical outcomes while increasing nurse workload. The acuity adaptable care model is a universal bed model structured to support patients in one room while providing staff, equipment and other resources across varying levels of acuity. METHOD: Qualitative descriptive methods were used to analyse the narratives of a purposive sample of patients and family members about receiving care in an acuity adaptable care delivery model. RESULTS: Three content areas emerged from the narratives and were categorized as feeling safe, perceiving continuity of care and valuing family, which culminated in a sense of comfort and healing while in the hospital. CONCLUSION: By bringing care services to the patient instead of taking the patient to the services, the acuity adaptable care model facilitated a perception of a healing environment for patients and family members. IMPLICATIONS FOR NURSING MANAGEMENT: The acuity adaptable care model should be considered when hospital facilities are undergoing major renovation or replacement.

Improving the Hospital Quality of Care during Winter Periods by Optimizing Budget Allocation Between Rotavirus Vaccination and Bed Expansion.

BACKGROUND: During each winter the hospital quality of care (QoC) in pediatric wards decreases due to a surge in pediatric infectious diseases leading to overcrowded units. Bed occupancy rates often surpass the good hospital bed management threshold of 85%, which can result in poor conditions in the workplace. This study explores how QoC-scores could be improved by investing in additional beds and/or better vaccination programs against vaccine-preventable infectious diseases. METHODS: The Cobb-Douglas model was selected to define the improvement in QoC (%) as a function of two strategies (rotavirus vaccination coverage [%] and addition of extra hospital beds [% of existing beds]), allowing improvement-isocurves to be produced. Subsequently, budget minimization was applied to determine the combination of the two strategies needed to reach a given QoC improvement at the lowest cost. Data from Jessa Hospital (Hasselt, Belgium) were chosen as an example. The annual population in the catchment area to be vaccinated was 7000 children; the winter period was 90 days with 34 pediatric beds available. Rotavirus vaccination cost per course was €118.26 and the daily cost of a pediatric bed was €436.53. The target QoC increase was fixed at 50%. The model was first built with baseline parameter values. RESULTS: The model predicted that a combination of 64% vaccine coverage and 39% extra hospital beds (≈ 13 extra beds) in winter would improve QoC-scores by 50% for the minimum budget allocation. CONCLUSION: The model allows determination of the most efficient allocation of the healthcare budget between rotavirus vaccination and bed expansion for improving QoC-scores during the annual epidemic winter seasons.

Sustaining critical care: using evidence-based simulation to evaluate ICU management policies.

Intensive Care Units (ICU) are costly yet critical hospital departments that should be available to care for patients needing highly specialized critical care. Shortage of ICU beds in many regions of the world and the constant fire-fighting to make these beds available through various ICU management policies motivated this study. The paper discusses the application of a generic system dynamics model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to explore the dynamics of intended and unintended consequences of such ICU management policies under a natural disaster crisis scenario. ICU management policies that can be implemented by a single hospital on short notice, namely premature transfer from ICU, boarding in ward, and general ward admission control, along with their possible combinations, are modeled and their impact on managerial and health outcome measures are investigated. The main insight out of the study is that the general ward admission control policy outperforms the rest of ICU management policies under such crisis scenarios with regards to reducing total mortality, which is counter intuitive for hospital administrators as this policy is not very effective at alleviating the symptoms of the problem, namely high ED and ICU occupancy rates that are closely monitored by hospital management particularly in times of crisis. A multivariate sensitivity analysis on parameters with diverse range of values in the literature found the superiority of the general ward admission control to hold true in every scenario.

Inside 'bed management': ethnographic insights from the vantage point of UK hospital nurses.

In the face of unprecedented financial and demographic challenges, optimising acute bed utilisation by the proactive management of patient flows is a pressing policy concern in high-income countries. Despite the growing literature on this topic, bed management has received scant sociological attention. Drawing on practice-based approaches, this article deploys ethnographic data to examine bed management from the perspective of UK hospital nurses. While the nursing contribution to bed management is recognised formally in their widespread employment in patient access and discharge liaison roles, nurses at all levels in the study site were enrolled in this organisational priority. Rather than the rational, centrally controlled processes promulgated by policymakers, bed management emerges as a predominantly distributed activity, described here as match-making. An example of micro-level rationing, for the most part, match-making was not informed by explicit criteria nor did it hinge on clearly identifiable decisions to grant or deny access. Rather it was embedded in the everyday practices and situated rationalities through which nurses accomplished the accommodations necessary to balance demand with resources.

An application of multi-attribute value theory to patient-bed assignment in hospital admission management: an empirical study.

Hospital beds are considered economically scarce and hospitalists strive to balance between utilizing beds more efficiently and complying with preference of physicians and patients when pairing patients to beds. This research is to develop preference-based decision rules for patient-bed assignment in a dynamic environment. A multi-attribute value theory (MAVT) model with additive value function is proposed to quantitatively deploy hospital policies in bed management. To elicit scaling factors and value functions for attributes, a linear programming model is constructed for all preference conditions. An empirical study was conducted with real data collected from two branches of a medical center. The simulated results using value function showed greater benefits when the patient-bed ratio was high and more flexible ward assignment was allowed. Further, a detailed analysis showed that this MAVT model was better in preference matching for both physicians/nurses and patients. At least 79 percent of patients were given beds in designated wards in accordance with their attending physicians' subspecialty, and more than 48 percent of patients' room preferences were matched in the simulated assignment for one branch.

Cross hospital bed management system.

The lack of adequate numbers of hospital beds to accommodate the injured is a main problem in public hospitals. For control of occupancy of bed, we design a dynamic system that announces status of bed when it change with admission or discharge of a patient. This system provide a wide network in country for bed management, especially for ICU and CCU beds that help us to distribute injured patient in the hospitals.

Using quality improvement to optimise paediatric discharge efficiency.

BACKGROUND: Bed capacity management is a critical issue facing hospital administrators, and inefficient discharges impact patient flow throughout the hospital. National recommendations include a focus on providing care that is timely and efficient, but a lack of standardised discharge criteria at our institution contributed to unpredictable discharge timing and lengthy delays. Our objective was to increase the percentage of Hospital Medicine patients discharged within 2 h of meeting criteria from 42% to 80%. METHODS: A multidisciplinary team collaborated to develop medically appropriate discharge criteria for 11 common inpatient diagnoses. Discharge criteria were embedded into electronic medical record (EMR) order sets at admission and could be modified throughout a patient's stay. Nurses placed an EMR time-stamp to signal when patients met all discharge goals. Strategies to improve discharge timeliness emphasised completion of discharge tasks prior to meeting criteria. Interventions focused on buy-in from key team members, pharmacy process redesign, subspecialty consult timeliness and feedback to frontline staff. A P statistical process control chart assessed the impact of interventions over time. Length of stay (LOS) and readmission rates before and after implementation of process measures were compared using the Wilcoxon rank-sum test. RESULTS: The percentage of patients discharged within 2 h significantly improved from 42% to 80% within 18 months. Patients studied had a decrease in median overall LOS (from 1.56 to 1.44 days; p=0.01), without an increase in readmission rates (4.60% to 4.21%; p=0.24). The 12-month rolling average census for the study units increased from 36.4 to 42.9, representing an 18% increase in occupancy. CONCLUSIONS: Through standardising discharge goals and implementation of high-reliability interventions, we reduced LOS without increasing readmission rates.

Optimal occupancy in the ICU: a literature review.

INTRODUCTION: In intensive care, occupancy is a commonly used measure. There is inconsistency however in its measurement and optimal occupancy targets need to be defined. The objectives of this literature review were to explore how occupancy is measured, reported, and interpreted and investigate optimal occupancy levels for ICUs. METHOD: A literature search was performed using the Medline, Embase and CINAHL databases and citation tracking identified additional relevant articles. Articles published since 1997, written in English and focused on the adult ICU setting were included. As a result, 16 articles were selected for this review. RESULTS: Although it was apparent there was no commonly accepted or used method for calculating ICU occupancy, methods described as more accurate enumerate actual patient hours in the ICU, use operational (and preferably fully staffed) beds as the denominator, and are calculated daily. Issues pertaining to the utility, interpretation, and reporting of ICU occupancy measures were identified and there were indications that optimal ICU occupancy rates were around 70-75%. It was evident however that setting a uniform target figure for all ICUs would be problematic as there are a range of factors both at the unit and the hospital level that impact occupancy figures and optimal occupancy levels. IMPLICATIONS: This literature review informed the recommendation of a proposed method for calculating ICU occupancy which provides a realistic measure of occupied bed hours as a percentage of available beds. Despite the importance of gaining an understanding of ICU occupancy at the local and broader health system levels, there are a number of unknown factors that require further research. Appropriate occupancy targets, impact of unavailable beds, and the intrinsic and extrinsic factors on occupancy measurement are a few examples of where more information is required to adequately inform ICU monitoring, planning and evaluation activities.

Improving hospital bed utilisation through simulation and optimisation: with application to a 40% increase in patient volume in a Norwegian General Hospital.

PURPOSE: This paper analyses the problem of allocating beds among hospital wards in order to minimise crowding. METHOD: We present a generic discrete event simulation model of patient flow through the wards of a hospital. In the generic model, each ward can have separate probability distributions for arrival times and length of stay, which may be time dependent. Output of the model is a matrix, with statistics on the utilisation of different hypothetical numbers of beds for each ward. This matrix is fed into an allocation algorithm, which distributes the available beds among the wards in an optimal way. We define bed utilisation either in terms of how often it is in use (prevalence), or in terms of how often a newly arriving patient is placed in it (incidence). For these classes of utilisation measures we develop efficient allocation algorithms, which we prove to be optimal. APPLICATION: The model was applied to Akershus University Hospital in Norway. In 2011, some of the wards of this hospital experienced a high occupancy rate, while others had a lower utilisation. Our model was applied in order to reallocate the hospital beds among the wards. For each ward, acute arrivals were modelled with Poisson-distributions with time-varying intensity, while elective arrivals were programmed to arrive in specific numbers at specific times. The arrival rates were based on empirical data for 2010, scaled up by an expected increase of 40% due to a restructuring of the hospital districts in Oslo and the greater metropolitan area in 2011. Length of stay was modelled as beta-distributions, using a combination of subject matter experts' evaluations and empirical data from 2010. The model has been verified and validated. RESULTS: Intuitively, both prevalence (average number of crowding beds in use) and incidence (number of patients placed in crowding beds) might seem like relevant optimisation criteria. However, our experiments show that prevalence optimisation gives more sensible solutions than incidence optimisation, as the latter tends to sacrifice entire wards where length of stay is long and patient turnover is slow. Prevalence optimisation was therefore used. The main results show that when the bed distribution is optimised, the share of crowding patient nights is reduced from 6.5% to 4.2%. CONCLUSION: This model provides a powerful tool for optimising hospital bed utilisation, and the application showed an important reduction in crowding bed usage. The generic model is flexible, as the level of detail in the modelling of arrivals and length of stay can vary according to the data available and accuracy required.

Midnight census revisited: Reliability of patient day measurements in US hospital units.

BACKGROUND: Patient days are widely used in nurse staffing research and for nursing quality measurement. Nursing hours per patient day (NHPPD) and fall rates incorporate patient days in the denominator and are endorsed by the US National Quality Forum (NQF) as nursing sensitive consensus measures. Measurement error introduced by patient days would affect the accuracy of these nursing quality indicators. OBJECTIVES: The aim of this study was to assess the reliability of five patient day reporting methods accepted by the National Database of Nursing Quality Indicators (NDNQI). The specific aims were (1) to investigate the agreement of five patient day measurements with a defined quasi-gold standard, (2) to explore method bias by investigating the association of potential confounding variables with the differences between the routine measurements and the quasi-gold standard, and (3) to extrapolate the potential effect of bias of the patient day methods on nursing quality indicators. DESIGN: A multiple census study with a national convenience sample of hospital units in the US was conducted. SETTING: 260 out of 282 units (92%) from 54 hospitals sent bi-hourly patient census data for seven randomly selected days in September 2008. METHODS: The multiple census data comprised the quasi-gold standard and was compared with data routinely submitted to the database. Intraclass correlations were calculated for an agreement analysis. A Bayesian regression analysis was conducted to explore the impact of different data collection methods and the degree of short stay patients. RESULTS: Overall agreement between routine data and the quasi-gold standard was excellent (ICC [95% CI]: 0.967 [0.958-0.974]). A Bayesian regression analysis identified that two methods underestimated patient days and an interaction between the degrees of short stay patients and one of the data collection methods also affected patient day measurement by up to 7.6%.

Informação compatível com a realidade para permanência maior que 24 horas em leitos de observação dos hospitais / Information compatible with reality to stay longer than 24 hours in observation beds in hospitals

Pode-se concluir que o preenchimento incorreto de dados relacionados à movimentação de pacientes na unidade de emergência e urgência distorce vários indicadores formados com esses dados, tanto na unidade de internação quanto na própria unidade de emergência, podendo levar a erros de diagnóstico de como está funcionado as respectivas unidades dentro do estabelecimento de saúde, interferindo e dificulta a análise e proposituras sistêmicas para modificar incoerências, superlotação e insuficiência de leitos no município(AU)

Informação compatível com a realidade para permanência maior que 24 horas em leitos de observação dos hospitais

Pode-se concluir que o preenchimento incorreto de dados relacionados à movimentação de pacientes na unidade de emergência e urgência distorce vários indicadores formados com esses dados, tanto na unidade de internação quanto na própria unidade de emergência, podendo levar a erros de diagnóstico de como está funcionado as respectivas unidades dentro do estabelecimento de saúde, interferindo e dificulta a análise e proposituras sistêmicas para modificar incoerências, superlotação e insuficiência de leitos no município

Managing rural emergency department overcrowding.

Emergency department visits reached more than 115 million in 2005, a 30% increase over the past decade. Although much has been written regarding these numbers, little attention has been focused on the impact of overcrowding and volume increases on rural emergency departments. Rural emergency departments face challenges unlike their urban counterparts that make implementation of current overcrowding strategies difficult or impossible. This article addresses these challenges and suggests strategies specific to the needs of rural emergency departments.