This research targets lupus, a prototypic complex disease, and supports care delivery in other contexts. To achieve care based on best practices, lupus requires sophisticated operational planning and scheduling capabilities and sometimes requires infusion-based treatments. A key innovation is to model capacity and provide planning decision support so as to effectively integrate clinical research activities into the clinical care delivery site. Lacking effective planning and appointment scheduling, the inconsistent capture of key disease activity tools degrades the ability to manage a disease like lupus and results in unnecessarily frequent visits to already scarce physicians. Novel operations decision support methodology will be developed to (1) improve access to care (reduced waiting), (2) reduce costly overtime and staff turnover while better utilizing physicians and staff, (3) better support evidence-based medicine, and (4) provide useful decision support for the administration of clinical research studies with clinical care.
If successful, key fundamental science and technology innovations will provide longitudinally coordinated health care and better health services research. For lupus, targeted operational systems improvements are known to improve patient health outcomes. The methodology will integrate delivery site planning and appointment scheduling. The former includes decision support for setting the mix of clinical care and clinical research services to be provided and setting the staff size (and composition) and number of rooms. Stochastic process models with integrated optimization algorithms will be created. The research will generate freely available teaching resources for engineers, physicians, researchers, and administrative delivery personnel. The dissemination will also facilitate commercialization. Conduct of the research will support the education and research development of graduate and undergraduate students with attention to underrepresented students.
