In the last three decades, evidence-based medicine (EBM) has been the driving force in shaping guidelines and clinical decision making in screening, prevention and treatment of diseases. Evidence review, evidence grading and meta-analysis of trials are standardized and routinely conducted. However, recent technological developments have significant impacts on future directions of EBM. With recent advances in health information technology, electronic medical records (EMRs), proteomics and genomics, clinical evidence has become increasingly abundant and diverse. Traditional approaches of evidence review and grading will not work in this new world of information explosion. At the same time, the inputs into medical decision making have also become increasingly complex. For every treatment decision, physicians need to account for results from a multitude of diagnostic tests, genomic/genetic information, family and personal history, presence of co-morbidities and patient preferences. Model Based Medicine (MBM) has recently emerged as a framework to address the above challenges. MBM is the use of large-scale integrated physiology and pathology-driven mathematical models to translate and to synthesize existing evidence and medical knowledge into a unified framework, which will then be used to support clinical decision making at individual patient level. MBM is much more than applying machine learning to health data. It not only incorporates all available evidence and most up-to-date understanding of diseases but also account for uncertainties in data and gaps in knowledge. MBM serves as an interface between evidence and physicians, allowing rapid extraction of quantitative, robust and already synthesized information for customized clinical decision making. The decisions can be optimized not only based on the therapeutic efficacy of health interventions, current health status of patients but also patient’s health behavior (e.g. past likelihood to comply with treatment recommendations) and preferences. Based on our recent experience at Archimedes, I will present several case studies to illustrate the power of MBM in leveraging data from EHR and other data sources to support decision making at both population and individual level. I will also speak about the scientific and technical challenges faced by MBM and our strategy in addressing these challenges, including developments of standardized and automatic tools for data integration and synthesis, model calibration and validation, uncertainty quantification and optimal design for model-physician interface.
As Vice President, Analytics and Modeling, Dr Tuan Dinh provides strategic and tactical leadership to all analytics and modeling activities at Archimedes Inc. Archimedes is a leading healthcare analytics company with a portfolio of innovative solutions such as the Archimedes Model, a large-scale, integrated simulation model of human physiology, diseases, behaviors, interventions, and healthcare systems, and IndiGO, the first point-of-care decision support tool that was designed to create individualized guidelines. Dr Dinh has more than 15 years of experience developing advanced analytics and modeling solutions to understand and to optimize complex systems, including nuclear power plants, biological cells, human physiology and pathology and healthcare systems. His recent works span several therapeutic areas, including prevention, screening and treatment of cancers, diabetes and cardiovascular diseases, genomic and genetic testing, mental health, and medication adherence. Dr. Dinh obtained a PhD. degree in Chemical Engineering from University of California, Santa Barbara and an MSc degree in Mechanical and Nuclear Engineering from Royal Institute of Technology, Stockholm, Sweden.
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