Hospital Capacity Planning Research Articles

Building hospital capacity planning mechanisms in Poland: The impact of 2016/2017 regulatory changes.

Capacity planning is a crucial component of modern health care governance. The aim of this paper is to analyze the requirements that need to be met to build effective hospital capacity planning mechanisms in Poland. In this context, the recent regulatory changes strongly influencing hospital sector functioning, including introduction of health care needs maps, capital investment assessment, and hospital network regulations, are analyzed. Some possible ways forward, based on review of international experiences in hospital capacity planning, are discussed. Applied methods include literature review and analysis of statistical data as well as desk analysis of key national regulations related to hospital sector. Results indicate that at the system level, the process of capacity planning involves 4 elements: capital investment in facilities, equipment, and technology; service delivery; allocation of staff; and financial resources. For hospital capacity planning to be effective, the strategic decision at the macrolevel must be complemented by appropriate management of individual hospitals. The major challenge of building hospital capacity planning mechanism in Poland is imbedding it into the overall health system strategy. Because of the lack of such a strategy, the practical implementation of the ad hoc changes, which have been introduced, shows some inconsistencies. The regulations implemented between 2016 and 2017 provided a basis for hospital capacity planning, yet still need evaluation and adjustments. Also, including a mechanism for human resources planning is of crucial importance. The regulations should provide incentives for reducing oversized hospital infrastructure with simultaneous development of the long-term and coordinated care models.

Hospital Policy and Productivity - Evidence from German States.

Total factor productivity (TFP) growth allows for additional healthcare services under restricted resources. We examine whether hospital policy can stimulate hospital TFP growth. We exploit variation across German federal states in the period 1993-2013. State governments decide on hospital capacity planning (number of hospitals, departments, and beds), ownership, medical students, and hospital investment funding. We show that TFP growth in German hospital care reflects quality improvements rather than increases in output volumes. Second-stage regression results indicate that reducing the length of stay is generally a proper way to foster TFP growth. The effects of other hospital policies depend on the reimbursement scheme: Under activity-based (German Diagnosis-related Group) hospital funding, scope-related policies (privatization and specialization) come with TFP growth. Under fixed daily rate funding, scale matters to TFP (hospital size and occupancy rates). Differences in capitalization in East and West Germany allow to show that deepening capital may enhance TFP growth if capital is scarce. We also show that there is less scope for hospital policies after large-scale restructurings of the hospital sector. Copyright © 2016 John Wiley & Sons, Ltd.

Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems

Effective resource requirement forecasting is necessary to reduce the escalating cost of care by ensuring optimum utilization and availability of scarce health resources. Patient hospital length of stay (LOS) and thus resource requirements depend on many factors including covariates representing patient characteristics such as age, gender, and diagnosis. We therefore propose the use of such covariates for better hospital capacity planning. Likewise, estimation of the patient's expected destination after discharge will help in allocating scarce community resources. Also, probable discharge destination may well affect a patient's LOS in hospital. For instance, it might be required to delay the discharge of a patient so as to make appropriate care provision in the community. A number of deterministic models such as ratio-based methods have failed to address inherent variability in complex health processes. To address such complexity, various stochastic models have therefore been proposed. However, such models fail to consider inherent heterogeneity in patient behavior. Therefore, we here use a phase-type survival tree for groups of patients that are homogeneous with respect to LOS distribution, on the basis of covariates such as time of admission, gender, and disease diagnosed; these homogeneous groups of patients can then model patient flow through a care system following stochastic pathways that are characterized by the covariates. Our phase-type model is then extended by further growing the survival tree based on covariates representing outcome measures such as treatment outcome or discharge destinations. These extended phase-type survival trees are very effective in modeling interrelationship between a patient's LOS and such outcome measures and allow us to describe patient movements through an integrated care system including hospital, social, and community components. In this paper, we first propose a generalization of the Coxian phase-type distribution to a Markov process with more than one absorbing state; we call this the multi-absorbing state phase-type distribution. We then describe how the model can be used with the extended phase-type survival tree for forecasting hospital, social, and community care resource requirements, estimating cost of care, predicting patient demography at a given time in the future, and admission scheduling. We can, thus, provide a stochastic approach to capacity planning across complex heterogeneous care systems. The approach is illustrated using a five year retrospective data of patients admitted to the stroke unit of the Belfast City Hospital.

A multilevel integrative approach to hospital case mix and capacity planning

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.

A Multilevel Integrative Approach to Hospital Case Mix and Capacity Planning

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.

Stress on the Ward - An Empirical Study of the Nonlinear Relationship between Organizational Workload and Service Quality

Wir untersuchen den Einfluss der Arbeitsbelastung innerhalb von Organisationen auf die Ergebnis-Qualitat der Dienstleistungen, in diesem Beispiel die Uberlebenswahrscheinlichkeit in Krankenhausern. In der Literatur besteht uberwiegend die Meinung, dass die Qualitat sich verschlechtert sobald die Arbeitsbelastung in der Organisation ansteigt. Wir stellen im Gegensatz dazu die These auf, dass die Beziehung zwischen Arbeitsbelastung und Qualitat nicht-linear ist und eine Qualitats-optimale Arbeitsbelastung moglich ist. Wahrend sich die Qualitat durch einen weiteren Anstieg der Arbeitsbelastung bei bestehender hoher Belastung verschlechtert, wird sie sich durch einen Anstieg bei niedrigem Niveau verbessern. Wir kommen zu dieser These durch die Kombination von drei unterschiedlichen Sichtweisen: 1) die Warteschlangentheorie mit dem Fokus auf Engpasse, (2) die bewuste Entscheidung des Personals ihre Arbeitsbelastung im Rahmen der Moglichkeiten zu steuern und (3) eine endokrinologische Perspektive, aus der wir die unbewusten Reaktionen durch Arbeitsbelastung auf die kognitive Leistungsfahigkeit des Personals ableiten. Wir testen unsere These empirisch mit einem Patientendatensatz von uber 1,4 Mill. Patienten aus insgesamt 101 deutschen Krankenhausern. Daruber hinaus diskutieren wir im Speziellen die Implikationen fur die Krankenhausplanung und ziehen ebenfalls Schlussfolgerungen fur das Management von Dienstleistungsunternehmen.

Improving hurricane disaster preparedness: models for optimal reallocation of hospital capacity

This research presents significant improvements to the hospital capacity planning models for hurricane disaster proposed by Paul and Batta. Firstly, we develop a model that reallocates hospital capacities taking into account the effect of casualty severity on medical care demands and travel time to hospitals on casualty outcomes. Secondly, travel time to hospitals, a key input to the model, is estimated using a stochastic simulation. Thirdly, in order to meet with challenges that arise as disaster evolves, we develop an algorithm that dynamically updates the pre-disaster plans to post-disaster reality. The algorithm evaluates prior decisions by dynamically updating the hospital status, travel times and status of the casualty clusters. We demonstrate the application of models in developing pre-disaster plans (hospital capacity and ambulance reallocation) through a case study on New Orleans. In addition, we show the application of the algorithm for post-disaster planning via illustrative examples.

Hospital capacity planning: from measuring stocks to modelling flows

The metric of "bed numbers" is commonly used in hospital planning, but it fails to capture key aspects of how hospital services are delivered. Drawing on a study of innovative hospital projects in Europe, we argue that hospital capacity planning should not be based on beds, but rather on the ability to deliver processes. We propose using approaches that are based on manufacturing theory such as "lean thinking" that focuses on the value that different processes add for the primary customer, i.e. the patient. We argue that it is beneficial to look at the hospital, not from the perspective of beds or specialties, but rather from the path taken by the patients who are treated in them, the respective processes delivered by health professionals and the facilities appropriate to those processes. Systematized care pathways seem to offer one avenue for achieving these goals. However, they need to be underpinned by a better understanding of the flows of patients, work and goods within a hospital, the bottlenecks that occur, and translation of this understanding into new capacity planning tools.

Psychological Effects of Patient Surge in Large-Scale Emergencies: A Quality Improvement Tool for Hospital and Clinic Capacity Planning and Response

Although information is available to guide hospitals and clinics on the medical aspects of disaster surge, there is little guidance on how to manage the expected surge of persons needing psychological assessment and response after a catastrophic event. This neglected area of disaster medicine is addressed by presenting a novel and practical quality improvement tool for hospitals and clinics to use in planning for and responding to the psychological consequences of catastrophic events that create a surge of psychological casualties presenting for health care. Industrial quality improvement processes, already widely adopted in the healthcare sector, translate well when applied to disaster medicine and public health preparedness. This paper describes the development of the tool, presents data on facility preparedness from 31 hospitals and clinics in Los Angeles County, and discusses how the tool can be used as a benchmark for targeting improvement. The tool can serve to increase facility awareness of which components of disaster preparedness and response must be addressed through hospitals' and clinics' existing quality improvement programs. It also can provide information for periodic assessment and evaluation of progress over time.

Editorial Comments—Psychological Effects of Patient Surge in Large-Scale Emergencies: A Quality Improvement Tool for Hospital and Clinic Capacity Planning and Response

Editorial Comments—Psychological Effects of Patient Surge in Large-Scale Emergencies: A Quality Improvement Tool for Hospital and Clinic Capacity Planning and Response

Hospital capacity planning for disaster emergency management

Hospitals are an integral part of a society's critical functions designed to respond to man-made and natural disasters. Effective hospital capacity planning can significantly enhance the capability and effectiveness of treatment for emergency patients with injuries resulting from a disaster. Such information can be used for patient/ambulance routing, resource planning, and emergency operations management. In the current paper, we develop a generic simulation model that is capable of representing the operations of a wide range of hospitals given an earthquake disaster situation. Using results from our simulations, generalized regression equations are fitted to obtain steady-state hospital capacities. A parametric metamodel is then developed to predict transient capacity for multiple hospitals in the disaster area in a timely manner, as demanded by emergency operations management for guiding the routing and treatment of injured people.

Capacity Planning in Health Care: A review of the international literature

Australian and New Zealand Journal of Public HealthVolume 33, Issue 5 p. 497-497 Free Access Capacity Planning in Health Care: A review of the international literature First published: 06 October 2009 https://doi.org/10.1111/j.1753-6405.2009.00439.xAboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat By Ettelt, Stefanie, Note, Ellen, Thomson, Sarah, Mays, Nicholas and the International Health Care Comparison Network Reviewed by Pat Neuwelt School of Population Health, The University of Auckland, New Zealand Books comparing health system decision-making structures are only infrequently produced, presumably due to the reality of constant health system restructuring in many nations. This recent publication potentially fills that gap. It is a 62-page policy brief, published by the World Health Organization on behalf of the European Observatory on Health Systems and Policies, which provides a useful summary of health systems planning in nine western countries. Written by members of the International Healthcare Comparisons Network, it is exactly as it is named, a ‘policy brief’. It is brief and is most likely to be useful for policy makers in western countries interested in comparing their own planning processes to those of similar western nations. It might also be useful to those involved in health services research and teaching. The book provides a high-level overview of the basic health care structures of Canada, Denmark, England, Finland, France, Germany, Italy, the Netherlands and New Zealand. (Of note, there is no mention of Australia in the publication). It focuses almost entirely on hospital-based health care, with some mention of ambulatory care, and only very limited mention of primary health care. It does not explicitly discuss the planning or delivery of public health services, nor does it discuss equity (despite its mention on p. 1) and, therefore, may be of limited interest to a wider public health audience. For health services research and policy people, the book is a useful ‘primer’ on the essential structures of health care in nine nations, and includes an interesting eight-page analysis comparing and contrasting capacity planning among them. It begins with an introductory section, which summarises the key aspects of capacity planning to be discussed in subsequent chapters. These include health plans and planning types, the scope of planning, hospital capacity planning, and capital-investment planning. The introductory summary states that the review of the nine countries “illustrates how approaches to planning strongly reflect the institutional, legislative and regulatory framework of a country's health system, and this, in turn, reflects the wider political, social, economic and cultural context” (p. 7). My view would be that this publication illustrates those points, but only to a limited extent, given its brevity and its stronger emphasis on description than analysis. As a public health and health services researcher, I would argue that the notable weaknesses of the policy brief are the lack of definitions and of a conclusion section. The authors do not clearly define what they mean by the term ‘capacity planning’, for example. Where one might expect a book to include a discussion of workforce capacity, it primarily focuses on health system structures, as well as high level decision-making processes, particularly with regard to health care expenditure and investment. Its limited discussion of community-based health care and broader public health is disappointing. The lack of a final conclusion section left me unsure of the intended audience. According to an online business dictionary, ‘capacity planning’ is defined as “ Systematic determination of resource requirements for the projected output, over a specific period”.1 Public health researchers and practitioners would tend to interpret the term ‘resources’ more broadly than simply ‘economic resources’, as appears to be the interpretation in this publication. Despite that fact, this is a useful resource that should not be overlooked by health services research and policy personnel, particularly those with an economic focus. References 1 WebFinance Inc, Business Dictionary.com Definition of Capacity Planning. http://www.businessdictionary.com/definition/capacity-planning.html (Accessed 27Apr09). Google Scholar Volume33, Issue5October 2009Pages 497-497 ReferencesRelatedInformation

Incorporating efficiency in hospital-capacity planning in Germany

Hospital occupancy is a key metric in hospital-capacity planning in Germany, even though this metric neglects important drivers of economic efficiency, for example treatment costs and case mix. We suggest an alternative metric, which incorporates economic efficiency explicitly, and illustrate how this metric can be used in the hospital-capacity planning cycle. The practical setting of this study is the hospital capacity planning process in the German federal state of Rheinland-Pfalz. The planning process involves all 92 acute-care hospitals of this federal state. The study is based on standard hospital data, including annual costs, number of cases--disaggregated by medical departments and ICD codes, respectively--length-of-stay, certified beds, and occupancy rates. Using the developed metric, we identified 18 of the 92 hospitals as inefficient and targets for over-proportional capacity cuts. On the upside, we identified 15 efficient hospitals. The developed model and analysis has affected the federal state's most recent medium term planning cycle.