Efficiency evaluation for pooling resources in health care

Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centers, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples include specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question of whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper we examine service and patient group characteristics to study the conditions where a centralized model is more efficient, and conversely, where a decentralized model is more efficient. This relationship is examined analytically with a queuing model to determine themost influential factors and then with simulation to fine-tune the results. The tradeoffs between economies of scale and economies of focus measured by these models are used to derive general management guidelines

Efficiency evaluation for pooling resources in health care

Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centres, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples include specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question of whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. Using quantitative Queueing Theory and Simulation models, we examine service and patient group characteristics to determine the conditions where a centralized model is more efficient and conversely where a decentralized model is more efficient. The results from the model measure the tradeoffs between economies of scale and economies of focus from which management guidelines are derived

Efficiency evaluation for pooling resources in health care: An interpretation for managers

Subject/Research problem\ud Hospitals traditionally segregated resources into centralized functional departments such as diagnostic departments, ambulatory care centres, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples are specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are grappling more and more with the question, should we become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper service and patient group characteristics are examined to determine conditions where a centralized model is more efficient and conversely where a decentralized model is more efficient.\ud Research Question\ud When organizing hospital capacity what service and patient group characteristics indicate that efficiency can be gained through economies of scale vs. economies of focus?\ud Approach\ud Using quantitative models from the Queueing Theory and Simulation disciplines the performance of centralized and decentralized hospital clinics are compared. This is done for a variety of services and patient groups. \ud Result\ud The study results in a model measuring the tradeoffs between economies of scale and economies of focus. From this model “rules of thumb” for managers are derived.\ud Application\ud The general results support strategic planning for a new facility at the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital. A model developed during this study is also applied in the Chemotherapy Department of the same hospital.\u

Designing for Economies of Scale vs. Economies of Focus in Hospital Departments

Subject/Research problem: Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centres, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples are specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper service and patient group characteristics are examined to determine conditions where a centralized model is more efficient and conversely where a decentralized model is more efficient. - Research Question: When organizing hospital capacity what service and patient group characteristics indicate efficiency can be gained through economies of scale vs. economies of focus? - Approach: Using quantitative Queueing Theory and Simulation models the performance of centralized and decentralized hospital clinics is compared. This is done for a variety of services and patient groups. - Result: The study results in a model measuring the tradeoffs between economies of scale and economies of focus. From this model management guidelines are derived. - Application: The general results support strategic planning for a new facility at the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital. A model developed during this research is also applied in the Chemotherapy Department of the same hospital

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

On two-echelon inventory systems with Poisson demand and lost sales

We derive approximations for the service levels of two-echelon inventory systems with lost sales and Poisson demand. Our method is simple and accurate for a very broad range of problem instances, including cases with both high and low service levels. In contrast, existing methods only perform well for limited problem settings, or under restrictive assumptions.\u

The right to health as the basis for universal health coverage : a cross-national analysis of national medicines policies of 71 countries

Persistent barriers to universal access to medicines are limited social protection in the event of illness, inadequate financing for essential medicines, frequent stock-outs in the public sector, and high prices in the private sector. We argue that greater coherence between human rights law, national medicines policies, and universal health coverage schemes can address these barriers. We present a cross-national content analysis of national medicines policies from 71 countries published between 1990-2016. The World Health Organization's ( WHO) 2001 guidelines for developing and implementing a national medicines policy and all 71 national medicines policies were assessed on 12 principles, linking a health systems approach to essential medicines with international human rights law for medicines affordability and financing for vulnerable groups. National medicines policies most frequently contain measures for medicines selection and efficient spending/cost-effectiveness. Four principles ( legal right to health; government financing; efficient spending; and financial protection of vulnerable populations) are significantly stronger in national medicines policies published after 2004 than before. Six principles have remained weak or absent: pooling user contributions, international cooperation, and four principles for good governance. Overall, South Africa ( 1996), Indonesia and South Sudan ( 2006), Philippines ( 2011-2016), Malaysia ( 2012), Somalia ( 2013), Afghanistan ( 2014), and Uganda ( 2015) include the most relevant texts and can be used as models for other settings. We conclude that WHO's 2001 guidelines have guided the content and language of many subsequent national medicines policies. WHO and national policy makers can use these principles and the practical examples identified in our study to further align national medicines policies with human rights law and with Target 3.8 for universal access to essential medicines in the Sustainable Development Goals

Can we design a market for competitive health insurance? CHERE Discussion Paper No 53

The topic of this paper is whether it is possible, given the current state of knowledge and technology, to design the appropriate market structure for managed competition. The next section reviews market failure in the private health insurance market. The subsequent two sections describe the principles of managed competition and its development and application in other countries. Then, the paper outlines recent developments in private health insurance policy in Australia, and proposals to apply managed competition in this country. The required design of the managed competition market place is described, and four major issues, risk adjustment, budget holding, consumer behaviour, and insurer behaviour, are identified. The final sections of the paper review the evidence on these four issues to determine if managed competition can be implemented, given current knowledge.Health Insurance, Managed competition, Australia