Despite the continuous efforts to improve emergency preparedness and response, large-scale demonstrate the need for improved emergency preparation, alert and response systems within a state and beyond. Considering the dynamic and unpredictable circumstances under which emergency systems must operate, there is a growing interest among researchers and public health stakeholders to measure the capacity and efficiency of these system.As questions relating to emergency preparedness and response continue to rise to the forefront, they have drawn the attention of researchers across many disciplines including the behavioral sciences, economics, engineering, business and law, just to name a few. The field of systems engineering emerges as a promising integrator of these diverse disciplines because it provides sophisticated techniques that have the ability to model (mathematically represent) the system, simulate (run multiple what-if scenarios) and optimize (select the ?best? course of action or system design for) complex systems, even under uncertainty. Furthermore, systems engineering techniques involve the ability to quantify performance and readiness metrics of a system and translate data into real time information quickly and accurately. Considering the importance of timely and accurate information during an emergency, these techniques have the potential to greatly enhance both the design and the operation of current emergency systems and serve as an aid in identifying unforeseen and counterintuitive deficiencies or bottlenecks within a system that may be undetectable to the naked eye. Quantification of emergency systems is of great interest to both public health stakeholders and engineering practitioners who are beginning to understand that improving an emergency or health system requires the ability to measure its capacity, feasibility, capability and efficiency. An objective of this project is to document and describe the processes and resources of the North Carolina Health Alert Network with the goals of developing optimization and simulation models to support the design and operation of Health Alert Networks to ensure efficient, effective response and sustainable public health preparedness and service. The inevitable threat of natural disasters and manmade attacks in light of Hurricane Katrina, the attacks of September 11th and the Indian Ocean Tsunami of 2005 have made emergency preparedness an issue of constant concern. The North Carolina Health Altert Network is positioned to capture and distribute alerts, however additional challenges regarding the response remain. Systems engineering emerges as a promising integrator of diverse disciplines to address these challenges.

This project is focused on the initial bridging of two currently separate research domains one of regenerative medicine and one of manufacturing engineering. The Wake Forest Institute for Regenerative Medicine (WFIRM) is one of the leading research institutes in developing methods for growing replacement tissue and organs for human implantation. WFIRM and North Carolina State University's Department of Industrial and Systems Engineering (NCSU-DISE) seek to accelerate the translation from experimentally oriented production to commercially economical tissue and organ transplants. These two very different research partners will collaborate on initial research to transform experimental laboratory processes into the industrial processes necessary to enable economic production of regenerative medical products. The focus in this initial effort will be to translate the recipes and protocols used in the early production of regenerative tissue and organs into manufacturing engineering terms. The major result of this early bridging research will be translating the biology and medical requirements that have been developed at WFIRM for one specific organ into the kinds of engineering process definitions and resource requirements needed to develop full scale manufacturing. Formal engineering models that will explicitly define the resource requirements for the bio-reactors used to produce tissue and organs will be developed. Process models characterizing the transformations that take place outside as well as within the bio-reactors will also be developed. The intent of the formal model development is not simply to chart the current methods used to produce regenerative products but rather to define the basic transformations that take place as well as to try to identify early constraints on the resources and process methods used in their manufacture. These process models will then become a key resource in developing manufacturing system model of efficient production systems for regenerative products. Broader Impact: The impact of this far reaching. To cite Dan Barrett from Inside Higher ED (January 5, 2011), Advances in medicine and biotechnology -- from the sequencing of the human genome to the development of small chips to detect cancer in the bloodstream -- were driven largely by scientists coming together from diverse disciplines to work on common problems. But a blue ribbon panel said here Tuesday that these advances also signify something larger: the creation of a new model -- dubbed "convergence" -- in which engineering and physical sciences, among other disciplines, join forces with the life sciences. This project provides early convergence of regenerative medicine and manufacturing engineering, and establishes a foundation for a much more ambitious research agenda. Intellectual Merit: This proposal focuses on a critical health care issue facing the aging American population ? that is the critical problem of diseased tissue and organs that every year result in extraordinary medical expenditures, and that take thousands of lives. The merit of the research is bridging two very disparate disciplines so that major inroads into the development of economically practical and safe organ transplants can be made possible. This work will not resolve this problem but will provide the impetus and necessary knowledge base for further research.

The project addresses the production of a new type of product ? the manufacture of human tissue and organs. The project consists of groups of domain experts at Wake Forest's Institute of Regenerative Medicine (WFIRM) and North Carolina State University's (NCSU's) College of Engineering that have come together to work collaboratively to assess, document and understand the current state-of-the-art for manufacturing regenerative medicine products. The primary focus of the proposed research is to use the knowledge base developed at WFIRM to define production requirements for the creation of tissue and organs and use those requirements to define and organize production systems that can produce living products safely, efficiently and affordably. The production system requirements will include: flow patterns, product requirements, process requirements (including FDA) and inventory and materials requirements. These components will be analyzed so that new system concepts for volume manufacture of tissues can be proposed. The manufacturing system design must be scalable to produce lots of size one efficiently and with appropriate quality and traceability in a cost effective manner while understanding the inventory and supply chain of this industry. The project will also address technological development requirements and other manufacturing system tools necessary for successfully achieving this vision.

While much research has focused on modeling the evolution of the natural history of disease, or determining optimal screening and treatment policies using techniques like Markov Decision Processes and discrete-event simulation, there has been little attempt to link these decisions, which determine the demand for facilities such as colonoscopy and treatment suites, to the location, acquisition and sizing of these facilities. There has also been little effort to link patient considerations such as available insurance coverage, travel time to the facility and waiting time at the facility to the design of these systems. The goal of this project is to create a suite of statistical and optimization models that link the demand for costly CRC care resources such as colonoscopy and treatment suites with screening and treatment policies and guidelines. In particular, the geographic characteristics and the cancer care network of screening and treatment facilities need specific consideration.

It is probably no exaggeration to claim that the rapidly escalating costs of health care is one of the most pressing issues facing our nation today - even a cursory review of the media reveals intense public interest and concern, and in recent years, a number of high-profile reports from the National Academies of Engineering and the Institute of Medicine paint a picture of a dichotomous healthcare system that can use the most advanced technology to miraculous therapeutic effect, but whose emergent behavior is such that: (i) the cost of healthcare is more than 15% of the U. S. economy; (ii) these costs are growing at more than three times the rate of inflation; (iii) tens of thousands of Americans die and many more are harmed by disjointed healthcare processes; (iv) waste accounts for perhaps as much as 40% of healthcare costs; (v) 46 million citizens lack access to basic care; (vi) fragmentation limits performance; and (vii) the healthcare delivery system is complex to the point of straining human comprehension. In the United States, rapidly rising healthcare costs threaten manufacturing and service companies with bankruptcy, creating intense pressure to move offshore. Indeed one can make the case that the best way to help competitiveness prospects for U.S. industries as a whole is to improve healthcare delivery. The field of health services research has developed a large following and a substantial body of knowledge, based mostly in medical, public health and health policy programs. This body of work has focused analyzing the issues associated with the equitable and effective delivery of healthcare services. However, the basic paradigms here have focused on statistical techniques, with the randomized clinical trial forming the main basis for progress. The use of mathematical and statistical modeling as a predictive tool to support the design of more effective clinical trials, or in some cases even obviates the need for them, is at extremely early stage of development. In contrast, the use of advanced models and computational techniques to solve them for optimal policies or resource allocations has been common in industrial engineering and operations research for several decades. The development, implementation and transfer into clinical practice of data-driven models to support decisions at different levels of patient care is an area in which industrial and systems engineers and operations researchers can make major contributions. The objective of this project is to bring together nationally recognized researchers from healthcare engineering and health services research to educate each other as to their work and potential for collaboration, and to discuss actionable mechanisms for collaboration between the two fields to help move their findings into clinical practice and health policy. The workshop will be held in Raleigh, NC on April 6-8, 2008. North Carolina State University and its industrial and academic partners will meet local expenses. NSF funds are requested to support travel by doctoral students and researchers travelling from out of state, as well as faculty time to prepare the formal report for publication and dissemination.

This proposal will review and evaluate the alternative factor screening methods applicable to simulation models, with special emphasis on medical decision simulation models. Focus on the theory and application of one method most likely to be successfully applied to medical simulation models. Consider augmenting the selection with net value of information. Develop or adapt an algorithm that will implement the factor screening technique. Demonstrate the implementation by coding the algorithm in VB.NET and interfacing it with a neutral form like Excel to communicate with arbitrary medical decision simulation models.