In the United States chronic illnesses have become a way of life for multiple generations – they are the number one cause of death and disability (accounting for more than 70% of deaths), 60% of American adults have at least one chronic disease, and 40% have multiple chronic conditions. Although multiple factors contribute to the growth in chronic disease prevalence, a major factor has been overreliance on health care systems for promoting health and preventing disease. Large health care systems are ill equipped for this role since they are designed to detect, treat, and manage disease, not to promote health or address the underlying causes of disease. Improving health outcomes in the U.S. will require implementing broad-based prevention strategies combining biological, behavioral, and societal variables that move beyond clinical care. According to community medicine, clinical care alone cannot create, support, or maintain health. Rather, health can only ensue from combining clinical care with epidemiology and community organization, because health is a social outcome resulting from a combination of clinical science, collective responsibility, and informed social action. During the past 20 years, our team has developed an operational community medicine approach known as community health science. Our model provides a simple framework for integrating clinical care, population health, and community organization, using community-based participatory research (CBPR) practices for developing place-based initiatives. In the present paper, we present a brief overview of the model and describe its evolution, applications, and outcomes in two major urban environments. The paper demonstrates means for integrating the social determinants of health into collaborative place-based approaches, for aligning community assets and reducing health disparities. We conclude by discussing how asset-based community development can promote social connectivity and improve health, and discuss how our approach reflects the emerging national consensus on the importance of place-based population system change.
Rationale, aims and objectives The main purpose of this paper is to measure the efficiency and ranking of medical diagnostic laboratories by applying a Network Data Envelopment Analysis. Methods In this study, each medical diagnostic laboratory is considered as a decision making unit (DMU) and a network data envelopment analysis (NDEA) model is utilized to calculate the efficiency of each medical diagnostic laboratory. Therefore, we design a series four-stage system composed of three main laboratory processes (the pre-test process, the test process and the post-test process). We also consider sustainability criteria in order to cover social, economic, and environmental problems of health care organizations. Results The results show that three of the 22 considered laboratories are efficient. Therefore, the network DEA approach can lead to performance scores and ultimately real ranking. Also, the average efficiency scores show that the decrease of the reception unit’s efficiency results in a decrease of the efficiency of each laboratory. Therefore, the laboratories can increase the number of patients. Along with the intermediate values of the reception unit and the sampling unit, the efficiency of the reception unit increases, which results in an increase for the overall efficiency of each laboratory. Conclusion The proposed model can appropriately help the administrators and managers to identify inefficient units in their laboratory and ultimately improve the laboratory performance.
Rationale, aims and objectives Creating networked business models is one of the innovative approaches that have the ability and potential for meeting market needs. The purpose of this study is to provide a decision making model for a fair profit sharing among the members of a diagnostic laboratory network while providing a distinctive value for the patients. Methods To identify the members of the network of laboratories, a suitable approach to calculate members’ efficiency scores is proposed. Then, the network members are classified into three groups based on their performance scores. The three groups help administrators identify eligible members, members who need to improve their performance in order to meet the minimum requirements, and members who do not qualify for admission to the network. Since the performance of the members should play a significant role in the fair profit sharing mechanism, the fair allocation of profits among network members is done by the use of Shapely’s value based on the efficiency scores of members. Results The results show that for such a fair mechanism, the efficiency and sample size (the number of samples (blood, urine) taken from the patients by the laboratories), as the two effective factors, have a decisive role in the share of profit of laboratory units of the network. In the Laboratory Services Network, members receive a number of samples according to their performance. As a result, the sample size received has a direct impact on the net income of each member. Conclusion In conclusion, it is evident that the use of Shapely value may help managers in the process of sharing profits among network members in a fair way, thereby improving network performance. In this way, incentive strategies may be created for the members of the network and long-term survival of the network may be achieved.