Human Service Nonprofits and Government Collaboration: Findings from the 2010 National Survey of Nonprofit Government Contracting and Grants

Presents nonprofit survey results on government contracts; their share of revenue; problems, including late or partial payments, complex application and reporting processes, and changes to contract terms; how nonprofits cope with them; and the results

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

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: 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

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Reallocating resources to focused factories: a case study in chemotherapy

This study investigates the expected service performance associated with a proposal to reallocate resources from a centralized chemotherapy department to a breast cancer focused factory. Using a slotted queueing model we show that a decrease in performance is expected and calculate the amount of additional resources required to offset these losses. The model relies solely on typical outpatient scheduling system data, making the methodology easy to replicate in other outpatient clinic settings. Finally, the paper highlights important factors to consider when assigning capacity to focused factories. These considerations are generally relevant to other resource allocation decisions

Optimizing the strategic patient mix

In this paper we address the decision of choosing a patient mix for a hospital that leads to the most beneficial treatment case mix. We illustrate how capacity, case mix and patient mix decisions are interrelated and how understanding this complex relationship is crucial for achieving the maximum benefit from the fee-for-service financing system. Although studies to determine the case mix that is of maximum benefit exist in the literature, the hospital actions necessary to realize this case mix has seen less attention. We model the hospital as an $M/G/\infty$ queueing system to evaluate the impact of accepting certain patient types. Using this queueing model to generate the parameters, an optimization problem is formulated. We propose two methods for solving the optimization problem. The first is exact but requires an integer linear programming solver whereas the second is an approximation relying only on dynamic programming. The model is applied in the department of surgery at a Dutch hospital. The model determines which patient types result in the desired growth in the preferred surgical treatment areas. The case study highlights the impact of striving for a certain case mix without providing a sufficiently balanced supply of resources. In the case study we show how the desired case mix can be better archieved by investing in certain capacity