Acute Hospital Bed Capacity - A National Review

The Programme for Prosperity and Fairness outlined the commitment of the Government to a review of hospital bed capacity in both acute and non-acute settings, to be carried out by the Department of Health and Children in conjunction with the Department of Finance and in consultation with the Social Partners. The focus of this report is on bed capacity in publicly-funded acute hospitals in Ireland. The capacity needs of the sub-acute sector have been assessed separately in the context of the Health Strategy, Quality and Fairness: A Health System for You. Download document here

Impact of long-stay beds on the performance of a tertiary hospital in emergencies

ABSTRACT OBJECTIVE To assess the impact of implementing long-stay beds for patients of low complexity and high dependency in small hospitals on the performance of an emergency referral tertiary hospital. METHODS For this longitudinal study, we identified hospitals in three municipalities of a regional department of health covered by tertiary care that supplied 10 long-stay beds each. Patients were transferred to hospitals in those municipalities based on a specific protocol. The outcome of transferred patients was obtained by daily monitoring. Confounding factors were adjusted by Cox logistic and semiparametric regression. RESULTS Between September 1, 2013 and September 30, 2014, 97 patients were transferred, 72.1% male, with a mean age of 60.5 years (SD = 1.9), for which 108 transfers were performed. Of these patients, 41.7% died, 33.3% were discharged, 15.7% returned to tertiary care, and only 9.3% tertiary remained hospitalized until the end of the analysis period. We estimated the Charlson comorbidity index – 0 (n = 28 [25.9%]), 1 (n = 31 [56.5%]) and ≥ 2 (n = 19 [17.5%]) – the only variable that increased the chance of death or return to the tertiary hospital (Odds Ratio = 2.4; 95%CI 1.3;4.4). The length of stay in long-stay beds was 4,253 patient days, which would represent 607 patients at the tertiary hospital, considering the average hospital stay of seven days. The tertiary hospital increased the number of patients treated in 50.0% for Intensive Care, 66.0% for Neurology and 9.3% in total. Patients stayed in long-stay beds mainly in the first 30 (50.0%) and 60 (75.0%) days. CONCLUSIONS Implementing long-stay beds increased the number of patients treated in tertiary care, both in general and in system bottleneck areas such as Neurology and Intensive Care. The Charlson index of comorbidity is associated with the chance of patient death or return to tertiary care, even when adjusted for possible confounding factors.

Bed Capacity Recommendations - Summary Table

Summary table of bed capacity recommendations by Durrant, in association with Pulitzer-Bogard & Associates and Criminal Justice Institute.

Simulit, un modèle de simulation pour la planification hospitalière. [Simulit, a simulation model for hospital planning].

The aim of this computerized simulation model is to provide an estimate of the number of beds used by a population, taking into accounts important determining factors. These factors are demographic data of the deserved population, hospitalization rates, hospital case-mix and length of stay; these parameters can be taken either from observed data or from scenarii. As an example, the projected evolution of the number of beds in Canton Vaud for the period 1893-2010 is presented.

Can the impact of bed closure in intensive care units be reliably monitored?

OBJECTIVE: To assess the properties of various indicators aimed at monitoring the impact on the activity and patient outcome of a bed closure in a surgical intensive care unit (ICU). DESIGN: Comparison before and after the intervention. SETTING: A surgical ICU at a university hospital. PATIENTS: All patients admitted to the unit over two periods of 10 months. INTERVENTION: Closure of one bed out of 17. MEASUREMENTS AND RESULTS: Activity and outcome indicators in the ICU and the structures upstream from it (emergency department, operative theater, recovery room) and downstream from it (intermediate care units). After the bed closure, the monthly medians of admitted patients and ICU hospital days increased from 107 (interquartile range 94-112) to 113 (106-121, P=0.07) and from 360 (325-443) to 395 (345-436, P=0.48), respectively, along with the linear trend observed in our institution. All indicators of workload, patient severity, and outcome remained stable except for SAPS II score, emergency admissions, and ICU readmissions, which increased not only transiently but also on a mid-term basis (10 months), indicating that the process of patient care delivery was no longer predictable. CONCLUSIONS: Health care systems, including ICUs, are extraordinary flexible, and can adapt to multiple external constraints without altering commonly used activity and outcome indicators. It is therefore necessary to set up multiple indicators to be able to reliably monitor the impact of external interventions and intervene rapidly when the system is no longer under control.

Comparison of different methods to forecast hospital bed needs

Objectives : This study compares three methods to forecast the number of acute somatic hospital beds needed in a Swiss academic hospital over the period 2010-2030. Design : Information about inpatient stays is provided through a yearly mandatory reporting of Swiss hospitals, containing anonymized data. Forecast of the numbers of beds needed compares a basic scenario relying on population projections with two other methods in use in our country that integrate additional hypotheses on future trends in admission rates and length of stay (LOS).

The use of cusum analysis in the early detection and management of hospital bed occupancy crises

Objective: To assess the value of cusum analysis in hospital bed management. Design: Comparative analysis of medical patient flows, bed occupancy, and emergency department admission rates and access block over 2 years. Setting: Internal Medicine Services and Emergency Department in a teaching hospital. Interventions: Improvements in bed use and changes in the level of available beds. Main outcome measures: Average length of stay; percentage occupancy of available beds; number of patients waiting more than 8 hours for admission (access block); number of medical patients occupying beds in non-medical wards; and number of elective surgical admissions. Results: Cusum analysis provided a simple means of revealing important trends in patient flows that were not obvious in conventional time-series data. This prompted improvements in bed use that resulted in a decrease of 9500 occupied bed-days over a year. Unfortunately and unexpectedly, after some initial improvement, the levels of access block, medical ward congestion and elective surgical admissions all then deteriorated significantly. This was probably caused by excessive bed closures in response to the initial improvement in bed use. Conclusion: Cusum analysis is a useful technique for the early detection of significant changes in patient flows and bed use, and in determining the appropriate number of beds required for a given rate of patient flow.

Difficulties in access and estimates of public beds in intensive care units in the state of Rio de Janeiro

ABSTRACT OBJECTIVE To estimate the required number of public beds for adults in intensive care units in the state of Rio de Janeiro to meet the existing demand and compare results with recommendations by the Brazilian Ministry of Health. METHODS The study uses a hybrid model combining time series and queuing theory to predict the demand and estimate the number of required beds. Four patient flow scenarios were considered according to bed requests, percentage of abandonments and average length of stay in intensive care unit beds. The results were plotted against Ministry of Health parameters. Data were obtained from the State Regulation Center from 2010 to 2011. RESULTS There were 33,101 medical requests for 268 regulated intensive care unit beds in Rio de Janeiro. With an average length of stay in regulated ICUs of 11.3 days, there would be a need for 595 active beds to ensure system stability and 628 beds to ensure a maximum waiting time of six hours. Deducting current abandonment rates due to clinical improvement (25.8%), these figures fall to 441 and 417. With an average length of stay of 6.5 days, the number of required beds would be 342 and 366, respectively; deducting abandonment rates, 254 and 275. The Brazilian Ministry of Health establishes a parameter of 118 to 353 beds. Although the number of regulated beds is within the recommended range, an increase in beds of 122.0% is required to guarantee system stability and of 134.0% for a maximum waiting time of six hours. CONCLUSIONS Adequate bed estimation must consider reasons for limited timely access and patient flow management in a scenario that associates prioritization of requests with the lowest average length of stay.

Groupes diagnostiques utilisés sur SIMULIT 14

Un premier exercice avait proposé un regroupement des diagnostics pour la planification des lits. Ce regroupement avait été établi empiriquement sur une base de données provenant des hôpitaux de zone vaudois (1983-1984). Lorsqu'il s'est agi d'appliquer cette grille au Centre Hospitalier Universitaire Vaudois (CHUV), il est rapidement apparu que la structure de la clientèle d'un tel hôpital rendait indispensable le remaniement de la grille descriptive. C'est l'objet du présent cahier... [Auteurs]

Analyse de la dotation en lits par groupes diagnostiques : exemple du service d'obstétrique dans les hôpitaux de zone

Ce document présente le modèe utilisé par le Service de la santé publique du canton de Vaud pour l'estimation du nombre de lits de court séjour et propose un enrichissement de ce modèle par l'utilisation de la statistique médicale VESKA. L'exemple présenté est celui de l'obstétrique, mais vaut pour d'autres secteurs de l'activité médico-hospitalières.

SIMULIT : description du modèle de simulation

Le modèle développé à l'Institut universitaire de médecine sociale et préventive de Lausanne utilise un programme informatique pour simuler les mouvements d'entrées et de sorties des hôpitaux de soins généraux. Cette simulation se fonde sur les données récoltées de routine dans les hôpitaux; elle tient notamment compte de certaines variations journalières et saisonnières, du nombre d'entrées, ainsi que du "Case-Mix" de l'hôpital, c'est-à-dire de la répartition des cas selon les groupes cliniques et l'âge des patients.

Définition des groupes cliniques utilisés sur SIMULIT 15

L'objet de ce cahier est de décrire la méthode de construction d'un système de "Case MiX" qui, en se fondant sur les DRG, ne décrit plus seulement la clientèle hospitalière en fonction des diagnostics principaux mais aussi des comorbidités ou complications recensées et des interventions chirurgicales subies.

SIMULIT : un modèle de simulation pour l'analyse et la planification de l'activité hospitalière

Le modèle développé à l'Institut universitaire de médecine sociale et préventive de Lausanne utilise un programme informatique pour simuler les mouvements d'entrées et de sorties des hôpitaux de soins généraux. Cette simulation se fonde sur les données récoltées de routine dans les hôpitaux; elle tient notamment compte de certaines variations journalières et saisonnières, du nombre d'entrées, ainsi que du "Case-Mix" de l'hôpital, c'est-à-dire de la répartition des cas selon les groupes cliniques et l'âge des patients.

SIMULIT : manuel de l'utilisateur

SIMULIT est un programme permettant la simulation de l'occupation des lits des hôpitaux de soins aigus. La mise en oeuvre de SIMULIT et des programmes annexes requiert de l'utilisateur qu'il sache créer et modifier un fichier à l'aide d'un éditeur, et lancer l'exécution d'un programme sur la machine dont il dispose. Le schéma général de la mise en oeuvre se trouve à l'annexe 1 de ce cahier.

Avoidable hospitalizations in Switzerland: a small area analysis on regional variation, density of physicians, hospital supply and rurality.

BACKGROUND Avoidable hospitalizations (AH) are hospital admissions for diseases and conditions that could have been prevented by appropriate ambulatory care. We examine regional variation of AH in Switzerland and the factors that determine AH. METHODS We used hospital service areas, and data from 2008-2010 hospital discharges in Switzerland to examine regional variation in AH. Age and sex standardized AH were the outcome variable, and year of admission, primary care physician density, medical specialist density, rurality, hospital bed density and type of hospital reimbursement system were explanatory variables in our multilevel poisson regression. RESULTS Regional differences in AH were as high as 12-fold. Poisson regression showed significant increase of all AH over time. There was a significantly lower rate of all AH in areas with more primary care physicians. Rates increased in areas with more specialists. Rates of all AH also increased where the proportion of residences in rural communities increased. Regional hospital capacity and type of hospital reimbursement did not have significant associations. Inconsistent patterns of significant determinants were found for disease specific analyses. CONCLUSION The identification of regions with high and low AH rates is a starting point for future studies on unwarranted medical procedures, and may help to reduce their incidence. AH have complex multifactorial origins and this study demonstrates that rurality and physician density are relevant determinants. The results are helpful to improve the performance of the outpatient sector with emphasis on local context. Rural and urban differences in health care delivery remain a cause of concern in Switzerland.