Hospital Capacity Planning Research Articles

Lessons from Covid-19 and the potential benefit of the implementation of Axon’s personal electronic health records (PEHR) into aesthetic care, plastic and reconstructive surgery

BackgroundCovid-19 pandemic highlighted the need for implementing Personal Electronic Health Records (PEHR) for patients’ data management. Furthermore, this pandemic underscored the relevance for integrated and interoperable Electronic Health Records (EHR) to support disease surveillance, hospital capacity planning and resource management (Peek N, Sujan M, Scott P (2020) Digital health and care in pandemic times: impact of COVID-19. BMJ Health Care Inf 27(1):e100166. https://doi.org/10.1136/bmjhci-2020-100166). Due to the lack of comprehensive patients’ record in plastic, reconstructive and aesthetic surgery, Axon’s myHealth app offers a break-through patient-centric design allowing patients to be in control of their records and updating them in real-time for their plastic and aesthetic care providers to have a clearer understanding of patients’ history and progress from pre-op to post-op.MethodsThe Axon Dublin survey took place during Covid-19 pandemic in two phases: Phase 1 aimed to assess the feasibility of patients integrating the Axon myHealth application into their clinical visits. Testing occurred in a clinical environment, where patients were encouraged to download and use the Axon system with a health practitioner (HP) present. Phase 2 focused on home testing, evaluating patients’ willingness to manage their health remotely with HP assistance. This phase included self-testing activities such as performing rapid Covid-19 antigen tests, recording medical history, and measuring blood pressure at home.ResultsThe Axon Dublin Study aimed to assess patient engagement, clinical impact, and cost-effectiveness of the Axon myHealth application. Over 85% of patients showed interest in owning a Personal Electronic Health Record. Notably, 36% continuously monitored chronic conditions. Clinical decisions, informed by patient data, saw 61.9% compliance. Noteworthy, 23% of hypertensive participants required immediate medication changes. Patient self-capture of data reduced consultation time. Public health implications were significant, with 39% vaccinated and 31% reporting complications. High user satisfaction (97%) demonstrated the app’s effectiveness in infection control and chronic care.ConclusionsOffering patients the ability to update and control their data is a growing interest, with a clear need in plastic and aesthetic surgery to have a better understanding of a patient’s medical past and progress throughout the surgical process and period. This platform, which is time and cost efficient, can only facilitate personalised care and improve outcomes while maintaining patient’s confidentiality.Level of evidenceNot gradable.

When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting

Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises.

A combination of annual and nonannual forces drive respiratory disease in the tropics

IntroductionIt is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we...

Introduction of quality indicators in German hospital capacity planning – Do results show an improvement in quality?

In Germany, the use of quality data to support hospital capacity planning was introduced in 2017. On behalf of the Federal Joint Committee, IQTIG suggested 11 quality indicators and developed a program on how to collect, evaluate and report data for the clinical areas gynaecological surgery, obstetrics and breast surgery. By analysing data from 2015 to 2021, effects of the introduction of the program on indicator results, statistical discrepancies and impact on care quality are examined. Effects on capacity planning are discussed.Since the program started, indicator results improved in all clinical areas, and statistical discrepancies and the number of assessments with insufficient quality decreased due to enhanced adherence to quality standards and data validity. Effects on capacity planning or the allocation of hospitals have not occurred.Thus, a change of the legal basis to allow a better link between quality and hospital planning is recommended. The approach to use quality data on hospital regulation in Germany is evolving. The current hospital reform in Germany also addresses other approaches to quality-based regulation. Already now, there have been clear improvements in specific indicators as well as lessons for quality assurance and its link to capacity planning provided by the program, which are also applicable to other countries.

Data-Driven Approaches to Hospital Capacity Planning and Management

Data-driven approaches to hospital capacity planning and management involve using historical and real-time data to identify patterns and trends in patient demand, resource utilization, and other key metrics. This information provides to develop predictive models, forecast patient demand, optimize staffing levels, and improve patient outcomes. Electronic health record systems and Internet of Things devices can also be used to monitor hospital operations in real-time and identify areas of inefficiency. Hospital capacity planning and management are critical to ensuring that healthcare facilities have enough resources to meet the needs of their patients. Data-driven approaches can be helpful in addressing these challenges by providing insights into patient demand, resource utilization, and other key metrics. This article discusses the various data-driven approaches to hospital capacity planning and management and their potential benefits. It also highlights the importance of having the right infrastructure and expertise effectively collect, analyse, and act on this data.

ОЦЕНКА ГОТОВНОСТИ СТАЦИОНАРОВ К РАБОТЕ В УСЛОВИЯХ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЙ

Significance. The COVID-19 pandemic has led to a crisis in society and seriously challenged healthcare capabilities around the world. The industry's lack of preparedness for such a large-scale and rapid spread of the virus around the world was one of the major factors in the fight against the pandemic faced by medical organizations and health services around the world. During the pandemic, the Russian Federation (hereinafter referred to as the RF) has radically restructured activities of medical organizations, re-equipped them with modern medical equipment, and intensively developed digitalization, making it possible to prevent the collapse of the industry and ultimately successfully cope with the pandemic. Analysis of the results and consequences of the restructuring the entire industry, medical organizations providing medical care of various types and in different conditions, allows us to assess emergency preparedness of hospitals and develop recommendations for the future. The purpose of the study was to assess hospital emergency preparedness in the Russian Federation based on the analysis of dynamics in bed capacity, structure of beds and utilization indicators in 2019-2022. Material and methods: comparative analysis of the number of beds, its structure and utilization indicators in the pre-COVID period, the pandemic period and the early post-COVID period based on the federal statistical observation form No. 30 “Information about medical organization” in the Russian Federation and in the constituent entities of the Russian Federation. Methods included descriptive statistics, comparative analysis and analytics. Results. A long-term trend towards reducing bed capacity and availability of beds was once interrupted in 2020, with the trend towards reducing the number of beds in rural areas remaining interrupted. In general, during the observation period, the total number beds decreased by more than 30 thousand, the population-to-bed ratio decreased by 3%, and the hospitalization rate dropped by 7%. The shortage of adult infectious disease beds at the beginning of the pandemic associated with the prior reduction of bed capacity against the background of the network restructuring was met by repurposing other beds to serve as infectious disease ones, and deploying additional infectious disease beds in and outside medical organizations to treat patients with COVID-19. The number of adult infectious disease beds increased 7.5-fold by the end of 2020. However, the number of adult infectious disease beds then started to decrease reaching a 59,4% increase by 2022 compared to 2019, but their share in rural areas decreased from 14% to 8%. The number of pediatric disease beds increased against the background of competing dynamics. Overall, during the period, the total number of pediatric and adult infectious disease beds increased by 27%. In the structure of adult infectious disease beds, the share of COVID-19 beds varied from 92% in 2020 to 34% in 2022; additionally deployed COVID-19 beds were also in operation. Over the observation period, the average bed length of stay and average bed occupancy decreased, while hospital mortality increased. Significant variability in hospital performance indicators across regions was registered. Conclusion In 2021-2022, hospital facilities in the Russian Federation were prepared to meet the increased incidence of infectious diseases. However, the renewed processes of bed capacity reduction necessitated forecasting sustainability of hospitals in emergency situations. The undertaken analysis shows that a cut in the number of round-the-clock beds should not be guided by the mere process of reduction. The decision to change the bed capacity and structure should be preceded by a thorough preliminary analysis of specialized care availability by each specialty planned to be reduced. Reducing planned hospitalizations and postponing treatment of patients with chronic non-communicable diseases turned out to be not fully justified, which was confirmed by other studies. Infectious disease beds must function in optimal numbers to cope with the increased flow of patients in early stages of an emergency. The areas where infectious disease beds are deployed must be sufficient to realize the possibility of quickly increasing the capacity of an infectious disease hospital to the required level. Planning a network of hospitals should ensure a comprehensive specialized care delivery to patients with chronic non-communicable diseases during emergencies including planned care. Scope of application: Hospital capacity and structure planning.

Exploring heterogeneity in hospital length of stay post emergency abdominal surgery in Ireland: A quantile regression approach

Background Emergency Abdominal Surgery (EAS) refers to a range of complex intra-abdominal surgical procedures associated with high mortality risk and long length of hospital stay (LOS). LOS is often used as a proxy measure for hospital resource utilisation in hospital capacity management and planning. Our objective was to explore the heterogeneity in LOS among EAS patients admitted at publicly funded hospitals in Ireland. Methods We analysed national hospital inpatient data (2014 – 2022) for adults discharged following EAS. We used Quantile Regression (QR) methods to explore the heterogeneous effects along the LOS distribution between 10 th - 90 th percentiles. We compared QR with Ordinary Least Squares (OLS) estimates, and identified from which point in the LOS distribution heterogeneous effects were different from OLS estimates. Results From the National Healthcare Quality Reporting System (NHQRS) records for 15,408 EAS adult inpatient episodes were obtained for analysis. We observed significant (p < 0.001) heterogeneous effects across most quantiles of the LOS distribution. LOS was longer for patients with Charlson comorbidity indices of 4 or higher, American Society of Anaesthesiologists physical status scores of 2 and higher, admissions to critical care units, hospital readmissions within 30-days, discharges to nursing home and other hospital, and for patients treated in Model 4 hospitals. LOS was shorter for patients with a cancer diagnosis and patients who died during admission. Across these factors, statistically significant heterogeneous effects above OLS estimates were observed at the 70 th to the 90 th quantile. Conclusions The QR methods identified the presence of significant heterogeneity across the entire LOS distribution. Relative to OLS mean estimates, QR is a better method for identifying heterogeneous effects by exploring the entire LOS distribution. Our results highlight the importance of using appropriate methods for estimating skewed outcomes. This is important to provide valid and relevant empirical analysis to inform policy.

Who will stay a little longer? Predicting length of stay in hip and knee arthroplasty patients using machine learning

BackgroundHospital length of stay (LoS) varies widely across hip (HA) and knee arthroplasty (KA) patients and depends on multiple factors. Prediction methods are necessary to improve hospital capacity planning and identify patients at risk of long LoS. This study aims (1) to compare the performance of previously applied machine learning (ML) as well as regression methods for either LoS classification or regression in a multi-hospital setting for primary HA and KA patients. In addition, the study aims (2a) to assess which variables are the most important predictors for LoS prediction and, specifically, (2b) whether patient-reported outcome measures (PROMs) collected before surgery act as important predictors. Methods2611 primary HA and 2077 primary KA patients from eight German hospitals were included to train and test extreme gradient boosting (XGBoost), naïve Bayes (NB) and logistic regression (LogReg) for classification, and XGBoost as well as a linear regression (LinReg) for regression. Area under the receiver operating characteristics curve (AUC) and mean absolute error (MAE) were used as primary performance indicators for classification and regression. ResultsFor classification, the highest AUC was reached by XGBoost and LogReg (AUC = 0.81) in the HA sample, whereas NB was statistically significantly outperformed by both other methods. In the KA sample, no statistical difference between any method was found, and AUC was lower for all models compared with HA. For regression, MAE was lowest for XGBoost (1.43 days for HA and 1.21 days for KA). PROMs and hospital indicators were among the most relevant predictors in all cases. ConclusionThe study demonstrated robust performance of ML in predicting LoS. PROMs reflect relevant features for prediction. They should be routinely collected and used for practical applications. XGBoost may act as a superior prediction tool compared to regression or other ML models in certain circumstances.

Service-, needs-, and quality-based hospital capacity planning – The evolution of a revolution in Switzerland

Most developed countries spend a large amount of their health budget on hospital capacities and inpatient services. However, those capacities and services are often not comprehensively planned what leads to vague service delivery steering and non-need driven hospital facilities. Switzerland is different as the planning procedure was completely reformed in 2012 and is further refined in 2021/2022. The Canton of Zurich, the frontrunner in Switzerland, has made a comprehensive update of its hospital capacity planning model for acute, psychiatric, and rehabilitative care. The result of this model is the hospital list. This list includes all hospitals which fulfill predefined quality, efficiency, and need requirements. Hospitals on the list receive a mandate to provide inpatient treatments for specific and selected service groups (n = 196), clustered in three areas (acute care, psychiatry, rehabilitation). The underlying health care policy process is transparent and is characterized by a high participation of all relevant actors. The building blocks of the planning model are a classification system of service groups, different quality and efficiency requirements attached to these groups, and an analysis of current and future need for health care. Hospitals which are willing to perform services must apply and demonstrate that the requirements are fulfilled. The canton then decides needs-based which hospital can deliver which services.

Using Principal Component Analysis to Identify Mortality Rates of Covid-19 Patients and Patients at High Risk of Death

The Covid-19 virus emerged in 2019 and spread all over the world in a short time. It caused millions of people to be infected and hundreds of thousands to die. The number of cases is increasing day by day and new variants of the virus are emerging. Polymerase Chain Reaction (PCR) tests are used to detect people with this disease. It is very important to examine the conditions of the people with the disease and to determine the intensive care and mortality rates in advance. In this study, Principal Component Analysis (PCA) was used as a feature extraction method to determine mortality rates from Covid-19 patients, and the successful results of the method were demonstrated with the most popular machine learning techniques. Machine learning techniques used in the study are K-Nearest Neighbor (KNN), Linear Discrimination Analysis (LDA), Extra Trees, Random Tree, Rep Tree and Naive Bayes algorithms. In the performance evaluation of these techniques, Accuracy, Precision, Sensitivity, Rms, F-score values were calculated. In addition, ROC Curves and Confusion matrices were examined and the results were compared. As a result, it was seen that the best performance was obtained with the use of Linear Discrimination Analysis (PCA+LDA) after applying Principal component analysis. With the PCA+LDA application, an accuracy rate of 96.39% was obtained. In the article, it has also been revealed that Pneumonia, Diabetes, COPD and Asthma patients, Pregnant, Elderly and Intubated people are more affected and the risk of death is higher from the Covid- 19 virus by using feature extraction. This study is important in terms of examining the lethality of virus variants, taking the necessary precautions for the treatment of risky patients isolation of patients at risk of death, and improving hospital capacity planning.

Indian study on probability attributed to COVID-19 coronavirus infection using machine learning algorithm

As a result of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak, the present COVID-19 public health crisis has resulted in the loss of human life as well as significant economic disruptions. Using a machine-learning algorithm, we can determine if a patient is more likely to survive than die, or the other way around, for a particular case. We use previous data, such as medical records, demographics, and information specific to the COVID-19, to fine-tune this algorithm. Data on confirmed and probable COVID-19 infections in India, which the Indian Government has collated and made publicly accessible, is used in this report. We show that the suggested technique can predict high-risk patients with high accuracy in each of the four indicated clinical phases, hence enhancing hospital capacity planning and prompt treatment. Our approach may be expanded to produce optimum estimations for hypothesis-testing procedures that are extensively used in biological and medical statistics. With the present epidemic, we hope that the information we've gathered may help doctors make better, more timely decisions about patient treatment.

An Overview of Hospital Capacity Planning and Optimisation.

Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.

Using the NYHA Classification as Forecasting Tool for Hospital Readmission and Mortality in Heart Failure Patients with COVID-19.

During the COVID-19 pandemic, it was observed that patients with heart disease are more likely to be hospitalized and develop severe COVID-19. Cardiac disease takes the top position among patient comorbidities, heart failure (HF) prevalence reaching almost 5% in the general population older than 35 years in Romania. This retrospective study aimed to determine the potential use of the NYHA classification for HF in hospitalized patients with COVID-19 as prognostic tool for in-hospital mortality, length of hospitalization, and probability of rehospitalization for HF decompensation. We observed that patients with advanced HF had a history of significantly more comorbid conditions that are associated with worse disease outcomes than the rest of patients classified as NYHA I and II. However, regardless of existing diseases, NYHA III, and, especially, NYHA IV, patients were at greatest risk for mortality following SARS-CoV-2 infection. They required significantly longer durations of hospitalization, ICU admission for mechanical ventilation, and developed multiple severe complications. NYHA IV patients required a median duration of 20 days of hospitalization, and their in-hospital mortality was as high as 47.8%. Cardiac biomarkers were significantly altered in patients with SARS-CoV-2 and advanced HF. Although the study sample was small, all patients with NYHA IV who recovered from COVID-19 required a rehospitalization in the following month, and 65.2% of the patients at initial presentation died during the next six months. The most significant risk factor for mortality was the development of severe in-hospital complications (OR = 4.38), while ICU admission was the strongest predictor for rehospitalization (OR = 5.19). Our result highlights that HF patients continue to be vulnerable post SARS-CoV-2 infection. Physicians and policymakers should consider this population’s high likelihood of hospital readmissions when making discharge, hospital capacity planning, and post-discharge patient monitoring choices.

Identification of high-risk COVID-19 patients using machine learning.

The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.

Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020–2021 is essential.MethodsAn integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff and ventilators under different epidemic scenarios in France, Germany and Italy across the 2020–2021 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICUs under varying levels of effectiveness is examined, using a ‘dual-demand’ (COVID-19 and non-COVID-19) patient model.ResultsWithout sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy.ConclusionReductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020–2021.

CPAS: the UK\u2019s national machine learning-based hospital capacity planning system for COVID-19

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic—we conclude the paper with a summary of the lessons learned from this experience.

Smart Health Model With A Linear Integer Programming Approach

The concept of intelligent health is part of the realization of a smart city. Most hospitals experience shortages in the availability of resources, such as the lack of truly professional and qualified personnel in the health care field, limitations on hospital facilities and infrastructure. Capacity planning at the hospital is done by ensuring a balance between quality given to health care provided and the costs provided. The measuring instrument used in hospital capacity planning is to calculate the availability of resources at the hospital. In general, the capacity model is intended to calculate the amount of resources needed to be able to serve patients’ needs quickly. The problem that is the main description of providing optimal health services based on the concept of “smart health” is how to provide services as early as possible with limited use of health resources. The main assumptions of modeling are only one type of health service can be met by each patient at a time, the number of medical staff is sufficient to fulfill each request, and the type of disease experienced by patients can be handled by available medical staff. The model produced in this study is expected to minimize travel costs, service costs and other costs arising from inaccuracies in providing services to patients.

Integrated Scheduling and Capacity Planning with Considerations for Patients' Length-of-Stays.

Motivated by the shortcoming of current hospital scheduling and capacity planning methods which often model different units in isolation, we introduce the first dynamic multi-day scheduling model that integrates information about capacity usage at more than one location in a hospital. In particular, we analyze the first dynamic model that accounts for patients' length-of-stay and downstream census in scheduling decisions. Via a simple and innovative variable transformation, we show that the optimal number of patients to be allowed in the system is increasing in the state of the system and in the downstream capacity. Moreover, the total system cost exhibits decreasing marginal returns as the capacity increases at any location independently of another location. Through numerical experiments on realistic data, we show that there is substantial value in making integrated scheduling decisions. In contrast, localized decision rules that only focus on a single location of a hospital can result in up to 60% higher expenses.

Patients with dementia in hospitals: a nation-wide analysis of administrative data 2010-2014.

Considering the limited information available, the aim of the study was to examine the prevalence and characteristics of inpatients with dementia in Belgian general hospitals. All admissions of inpatients aged at least 40years with or without dementia were retrieved from the nationwide administrative hospital discharges database for the period 2010-2014. Admissions of inpatients aged 40years or more with dementia have increased to reach 83,017 out of 1,285,593 admissions (6.46%) in general hospitals in 2014, mostly admitted through the emergency department (79.7%) and for another reason than dementia (85.9%). These patients stayed longer [19.2days, standard deviation (sd) = 23.6, median = 13] than the average length of stay of patients of the same age (7.9days, sd = 14.1, median = 17). Considering patients aged 75years or more falling into the 20 most common pathology groups (of patients with dementia), the group with dementia spent 5days more than the group without dementia. Patients admitted from home spent more time in hospital when they were discharged to a residential care facility than when they returned home (27.2days versus 15.8days). The in-hospital mortality was high in the first days of admission. The growing prevalence of patients with dementia in inpatient setting puts a high pressure on the hospital capacity planning and geriatric expertise. Moreover, as patients with dementia should be kept outside hospitals when possible for safety and quality matters, long-term organizational investments are required inside hospital and residential care settings as well as in community care.

The U.S. Medicare Disproportionate Share Hospital program and capacity planning

U.S. Medicare's Disproportionate Share Hospital (DSH) program provides financial assistance to hospitals that serve low-income populations. Statutory formulas determine the DSH payments made to hospitals; however, the formulas applied to “large” urban hospitals (at least 100 beds) are more generous than those used for “small” urban hospitals (less than 100 beds). The purpose of this study is to determine if the DSH program's 100-bed threshold influences bed capacity planning. We find that the threshold drives a significant discontinuity in the distribution of urban hospital bed capacities, with hospitals tending to maintain just enough beds to qualify for higher DSH payments. The magnitude of the discontinuity is greatest in hospitals with strong incentives to manage bed capacity; and, compared to government and not-for-profit hospitals, for-profit hospitals are more likely to manage capacity to meet the 100-bed threshold. Our findings highlight the potential for regulatory institutions to have unintended influence on capacity planning decisions.