Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

The utility of rapid participatory appraisal for community assessment, diagnosis, and policy development

The selection of a model to guide the understanding and resolution of community problems is an important issue relating to the foundation of public health practice: assessment, policy development, and assurance. Many assessment models produce a diagnosis of community weaknesses, but fail to promote planning and interventions. Rapid Participatory Appraisal (RPA) is a participatory action research model which regards assessment as the first step in the problem solving process, and claims to achieve assessment and policy development within limited resources of time and money. Literature documenting the fulfillment of these claims, and thereby supporting the utility of the model, is relatively sparse and difficult to obtain. Very few articles discuss the changes resulting from RPA assessments in urban areas, and those that do describe studies conducted outside the U.S.A. ^ This study examines the utility of the RPA model and its underlying theories: systems theory, grounded theory, and principles of participatory change, as illustrated by the case study of a community assessment conducted for the Texas Diabetes Institute (TDI), San Antonio, Texas, and subsequent outcomes. Diabetes has a high prevalence and is a major issue in San Antonio. Faculty and students conducted the assessment by informal collaboration between two nursing and public health assessment courses, providing practical student experiences. The study area was large, and the flexibility of the model tested by its use in contiguous sub-regions, reanalyzing aggregated results for the study area. Official TDI reports, and a mail survey of agency employees, described policy development resulting from community diagnoses revealed by the assessment. ^ The RPA model met the criteria for utility from the perspectives of merit, worth, efficiency, and effectiveness. The RPA model best met the agencies' criteria (merit), met the data needs of TDI in this particular situation (worth), provided valid results within budget, time, and personnel constraints (efficiency), and stimulated policy development by TDI (effectiveness). ^ The RPA model appears to have utility for community assessment, diagnosis, and policy development in circumstances similar to the TDI diabetes study. ^