Health Informatics and Systems Engineering Symposium

hise_bannerLearn more about how Wayne State University's internationally-renowned faculty are using health informatics and systems engineering to bolster effectiveness, efficiency, safety and quality in health care education, systems management, and medical research.

 

2017 Symposium

Thursday, March 2

8:30 a.m.-4:30 p.m.

Integrative Biosciences Center

Event summary

Health informatics is a multidisciplinary field that uses health information technology to improve health care on its effectiveness, efficiency, timeliness, safety and quality. It is also a powerful tool for medical research.

Health Systems Engineering is an academic discipline that treats the health care industry as complex systems, and identifies and applies engineering design and analysis principles in such areas. It is a growing profession that has been insignificantly positioned to dramatically move the U.S. health care system forward.  Several health systems in the Metro Detroit area, such as Henry Ford Health System, Detroit VA, and University of Michigan Health System are among the leaders in health informatics and systems engineering and Wayne State University has good working relationships with them.

Wayne State possesses strong capabilities in both health informatics and systems engineering.  WSU has some internationally well-known leading faculty members in both areas. Currently, fostering the collaboration of informatics and systems engineering professionals with medical professionals is one of the university's strategic priorities. This will open the doors for much more opportunities in grant applications, new educational programs, and industry-university programs. Wayne State is proposing to establish a university-level center for health informatics and health care systems engineering to fully reap the benefits of this collaboration.

 

Key tracks:

Health Informatics

  • Health care data acquisition, transmission, management and visualization
  • Medical imaging/biomedical signal processing and analytics
  • mHealth innovations
  • Cognitive computing for health care delivery and disease management

Health Systems Engineering

  • Lean and six sigma
  • Method for improving patient quality and safety
  • Health care workflow and process modeling and improvement
  • Health care operations management
  • Social technical system analysis
  • Health logistics and supply chain
  • Health economics
  • Health care management and policy

 

Wayne State University believes that this symposium and subsequently follow up activities can bring the following benefits:

  • Create strong working groups to apply many new funding opportunities to improving Wayne State University's funding prospects
  • Greatly expand Wayne State's outreach and collaboration to health care organizations, community services, etc.
  • Make WSU to becomes a center of excellence in health care education, health systems continuous improvement and comprehensive medical research

2017 Health Informatics and Systems Engineering Symposium

 

Agenda

8:30 – 9 a.m. Registration and continental breakfast
9 – 9:30 a.m. Welcoming remarks

Steve Lanier, Ph.D., Vice President, Research, Wayne State University

Farshad Fotouhi, Ph.D., Dean, College of Engineering, Wayne State University

Kai Yang, Ph.D., Professor, Department of Industrial and Systems Engineering; Director, Healthcare Systems Engineering Group, Wayne State University

9:30 – 10:15 a.m. Keynote speaker
Alfred Hero, Ph.D., Director, Michigan Institute of Data Science, University of Michigan
Title: Data Science for Personalized Health
10:15 – 10:30 a.m. Coffee break
10:30 a.m. – 12 p.m. Session 1 (concurrent sessions)

Health Informatics

Session chair: Juri Gelovani, M.D., Ph.D., Director, Medical Engineering; Professor, Department of Biomedical Engineering, Wayne State University

Min Zhang, Ph.D., Assistant Professor of Radiology, College of Medicine, Mayo Clinic
Title: Radiology Informatics:  Medical Imagining Data Integration and Analytics

Phillip Levy, M.D., M.P.H., Professor and Associate Chair for Research, Department of Emergency Medicine, Wayne State University

Dongxiao Zhu, Ph.D., Associate Professor, Department of Computer Science, Wayne State University
Title: Machine Learning Approaches to Precision Medicine

Shiyong Lu, Ph.D., Associate Professor, Department of Computer Science, Wayne State University
Title: Diagnosis Recommendation form Medical Text Record

Yan Li, Ph.D., Postdoc fellow, Computational Medicine and Bioinformatics, University of Michigan
Title: Data Driven Approaches for Timely and Effective Healthcare Service


Healthcare Systems Engineering

Session chair: Kai Yang, Ph.D., Professor, Department of Industrial and Systems Engineering; Director, Healthcare Systems Engineering Group, Wayne State University

Kai Yang, Ph.D., Professor, Department of Industrial and Systems Engineering; Director, Healthcare Systems Engineering Group, Wayne State University
Title: Overview of Healthcare Systems Engineering

Mariel Lavieri, Ph.D., Associate Professor, University of Michigan
Title: Personalizing Management of Glaucoma Patients

Mouhanad Hammami, M.D., Director & Health Officer, Wayne County Department of Health, Veterans and Community Wellness – Wayne County Michigan
Title: Department of the People – A New Model for Health and Human Services Delivery

Richard E. Hughes, Ph.D., Associate Professor of Orthopaedic Surgery, Biomedical Engineering, and Industrial & Operations Engineering, University of Michigan; Project Co-Director, Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI); Director, Laboratory for Optimization and Computation in Orthopaedic Surgery
Title: Using Registry Data and Collaboration to Drive Quality Improvement in Total Hip and Knew Arthroplasty Across the State of Michigan

12 – 1 p.m. Lunch break (provided)
1 – 1:45 p.m. Keynote speaker
Lucy Young, Director, Performance Improvement, Henry Ford Health Systems
Title: Built to Last – Henry Ford Health System's Story of Embracing Performance Improvement
1:45 – 2 p.m. Coffee break
2 – 3:30 p.m. Session 2 (concurrent sessions)

Health Informatics

Session chair: Kai Yang, Ph.D., Professor, Department of Industrial and Systems Engineering; Director, Healthcare Systems Engineering Group, Wayne State University

Sham Vaidya, IBM Distinguished Engineer; Director, Watson Solutions Lab, IBM Watson Group
Title: Solving Healthcare Problems Using Cognitive Technologies

Sorabh Dhar, M.D., Associate Professor, Division of Infectious Diseases, Wayne State University School of Medicine
Title: Clinical Service and Research in Infection Control and Antimicrobial Stewardship: Opportunities for Reducing the Burden of Drug-Resistant Bacterial Infections in Acute Care Settings

Kyoung-Yun Kim, Ph.D., Associate Professor, Department of Industrial and Systems Engineering, Wayne State University
Title: Reusable Medical Equipment Service Lifecycle Management

John McCarthy, Ph.D., M.P.H., Director, SMITREC, Office of Mental Health Operations; Investigator, Center for Clinical Management Research, Department of Veterans Affairs; Research Associate Professor, Department of Psychiatry, University of Michigan
Title: Using Predictive Modeling in Suicide Prevention:  Supplementing Current Strategies for Suicide Prevention in the Veterans Health Administration


Healthcare Systems Engineering

Session chair: Juri Gelovani, M.D., Ph.D., Director, Medical Engineering; Professor, Department of Biomedical Engineering, Wayne State University

Susan Yu, Chief of Quality and Performance, John Dingell VA Medical Center
Title: Quality, Safety and Value Team in the Detroit VA

John D. Piette, Ph.D., Professor of Health Behavior and Health Education, and of Internal Medicine; Director, Center for Managing Chronic Disease; Senior Research Career Scientist, VA Ann Arbor Center for Clinical Management Research
Title: Advances in Mobile Health Using Simple Devices

Jayant Trewn, Ph.D., Fellow of American Society of Quality; Lecturer, Department of Industrial and Systems Engineering, Wayne State University
Title: Identifying Error-Prone Human-Machine Interfaces in Healthcare Delivery Systems

Timothy Williams, Lean Manager, Performance Management & Innovation, Detroit Medical Center
Title: Lean at the DMC – The Journey to Delivery Value-Based Care

3:30 – 4:30 p.m. Panel discussion

 

Keynote speakers

 

hero_picAlfred Hero, Ph.D., Co-Director, Michigan Institute of Data Science, University of Michigan

Alfred O. Hero III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is also the Co-Director of the University of Michigan Institute for Data Science (MIDAS). His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics.

Hero holds a B.S. (summa cum laude) from Boston University (1980) and a Ph.D. from Princeton University (1984), both in Electrical Engineering. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has served as President of the IEEE Signal Processing Society and as a member of the IEEE Board of Directors.

He has received numerous awards for his scientific research and service to the profession including the IEEE Signal Processing Society Technical Achievement Award in 2013 and the 2015 Society Award, which is the highest career award bestowed by the IEEE Signal Processing Society. Hero's recent research interests are in high dimensional spatio-temporal data, multi-modal data integration, statistical signal processing, and machine learning. Of particular interest are applications to social networks, network security and forensics, computer vision, and personalized health.

Speech Title:  Data Science for Personalized Health

The increasing availability of massive personal health-related data is opening new frontiers in health and medicine. This includes longitudinal molecular genomic, proteomic and metabolomic data extracted from blood and other tissues, clinical data, and physiological data from wearable health monitors.  The massive size and heterogeneity of such data is multifaceted and this raises several challenges to data analysis.  We will discuss some of these challenges in the context of developing data science for applications in personalized health and medicine.

 

young_picLucy Young, Director, Corporate Performance Improvement, Henry Ford Health System

Lucy Young has 36 years of experience in health care management engineering and quality improvement. She has lead and been involved with several types of projects and improvement initiatives related to quality improvement, cost reduction, re-engineering, benchmarking and information systems implementation, mergers, and opening new hospitals and facilities. Currently, Young is the Director of the Henry Ford Corporate Performance Improvement Department, where she directs a team of 20 performance improvement consultants and works with the HFHS leadership team on the deployment of key system strategies.

From 2013 to 2015, Young was the Director of Quality and Performance Excellence at Henry Ford Hospital in Detroit, Michigan, the "flagship" hospital in Henry Ford Health System. She was responsible for the performance improvement key strategies at the hospital as well as risk management, infection control and regulatory compliance. Previously, Young was a Senior Consultant for Premier, Inc. where she was assigned as the Director of Performance Engineering at McLaren Health Care Corporation in Flint, Michigan. She also worked for the Detroit Medical Center (DMC) for 14 years, finishing her tenure there as their Director of Corporate Operations Analysis.

Young served on the Society for Health Systems Board from 2006 to 2008 and was SHS President from 2011-2012. Lucy has a B.S. in Industrial and Operations Engineering from the University of Michigan and an MBA from Oakland University. She is married with six children. 

Speech Title: Built to Last: Henry Ford Health System's Story of Embracing Performance Improvement

As described in its 2012 winning Baldrige Application, one of Henry Ford Health System's (HFHS) core organizational competencies is "innovation," stemming from its long-standing culture of performance improvement. In this keynote presentation, Young will provide a brief history of the evolution of HFHS's performance improvement culture started when Henry Ford built the Hospital in 1915.  She will explain how the Performance Improvement department was established, highlight their improvement work over the years, and describe the growth in their demand and how a team of performance improvement and performance analytics staff are organized to support the strategic initiatives of the system. Young will also share the key factors of a successful relationship with Performance Improvement and System leadership that ensures that the Performance Improvement team is delivering value to the organization that is "built to last."

 

Session speakers

 

dharSorabh Dhar, M.D., Associate Professor, Division of Infectious Diseases, Wayne State University School of Medicine

Dr. Sorabh Dhar is an Associate Professor of Medicine in the Division of Infectious Diseases at the Wayne State University School of Medicine. He is a graduate of the School of Medicine at the State University of New York at Buffalo, N.Y. Dr. Dhar went on to complete his internal medicine/pediatrics residency at the same institution. This training was followed by a two-year infectious diseases fellowship at the Cleveland Clinic. Following his fellowship, Dr. Dhar completed additional post-doctoral research training at the Institute of Infectious Diseases at the University of Bern, Switzerland. He is currently the Corporate Medical Director of Antimicrobial Stewardship at the Detroit Medical Center and John D. Dingell Veterans Affairs Medical Center, and the Medical Director of Infection Prevention & Hospital Epidemiology at the John D. Dingell VAMC.

His research interests include various aspects of infection control (including hand hygiene and contact precautions), and the epidemiology and treatment of multidrug resistant organisms (MDROs). He has published over 30 articles and book chapters, and has presented over 60 national abstracts in this field. Dr. Dhar is an investigator on five clinical trials that are evaluating optimal treatment and outcomes of patients infected with resistant gram negative organisms, and in ways to prevent the development and spread of resistant organisms in hospitals.

He has chaired the Society for Healthcare Epidemiology of America (SHEA) Continuing Medical Education (CME) Committee and served on the Society for Healthcare Epidemiology of America (SHEA) Education Commitee.

Speech Title: Clinical Service and Research in Infection Control and Antimicrobial Stewardship: Opportunities for Reducing the Burden of Drug-Resistant Bacterial Infections in Acute Care Settings

Infection prevention and control, and antimicrobial stewardship are two distinct disciplines of infectious diseases responsible for preventing health care associated infections (HAIs) and reducing the development of antimicrobial resistance among microorganisms. Recently there has been a growing emphasis on prevention and process, as the quality and cost implications of infections caused by these organisms becomes more clear. Health informatics and health systems engineering have become integral tools to routine infection prevention and antimicrobial stewardship activities. Collectively, these fields have the potential to improve direct patient care and program outcomes through automated infection surveillance and prevention activities such as hand hygiene, awareness of the carriage of antibiotic resistance bacteria at time of admission, inappropriate isolation precautions, and use of antimicrobials. This talk will discuss clinical, quality improvement, and research aspects of infection prevention and antimicrobial stewardship in reducing the burden of drug resistance bacterial infections within the context of health informatics and health systems engineering. 

 

gelovani_picJuri Gelovani, M.D., Ph.D., Director, Medical Engineering; Professor, Department of Biomedical Engineering, Wayne State University

Dr. Gelovani is the pioneer of molecular-genetic in vivo imaging. His research interests include molecular PET imaging of cancer and the central nervous system using newly developed radiotracers, genomics and proteomics for cancer therapy, adoptive immunotherapy and regenerative stem cell therapies. He holds more than 15 patents, has published more than 160 papers and book chapters, and edited a major book in molecular imaging in oncology. Additionally, several diagnostic imaging compounds he developed are currently in clinical trials in cancer patients.

Dr. Gelovani received the International Fellow Award from the Alexander von Humboldt Foundation, the George and Barbara Bush Endowment for Innovative Cancer Research, and the Gold Medal for significant contributions to the field of molecular imaging from the Society for Molecular Imaging (SMI). He was president of the SMI and the Academy of Molecular Imaging, for which he received service awards. Dr. Gelovani also was president of the World Molecular Imaging Society. He is a member of several grant review study sections at the National Institutes of Health, an associate editor of Molecular Imaging and Biology, and one of the academic editors of PLOS One.

Dr. Gelovani earned both his M.D. (1986) and Ph.D. (1990) in neurosurgery from the University of Tartu in Estonia. He completed his postdoctoral fellowship at the Memorial Sloan-Kettering Cancer Center in New York City (1991-96).

 

hammami_picMouhanad Hammami, M.D., Director & Health Officer, Wayne County Department of Health, Veterans and Community Wellness – Wayne County Michigan

A graduate of Aleppo University in Syria, Dr. Hammami completed his postdoctoral research in Pediatrics at the Newborn Center of the University of Tennessee in Memphis, and then accepted a faculty appointment at Wayne State University School of Medicine and a research position at the Detroit Medical Center, Department of Pediatrics.

In 2006 he was granted the American Medical Association (AMA) foundation for Excellence in Medicine and Leadership award for his public health advocacy and community work. He is listed in the Marquis 2006-07 Who's Who in Medicine and Healthcare, Strathmore's 2006-07 Who's Who in Healthcare and Madison's Who's Who in the World 2008-09.   He was awarded the "Health Policy Champion Award" by the Michigan Department of Community Health in 2011, Arab American of the Year in Medicine in 2012 and nominated by the White House for Heroes for Health in 2013.

Dr. Hammami was appointed as the Chief of Health Operations of Wayne County Department of Health and Human Services and the County Health Officer in 2009.  Recently, Dr. Hammami was appointed as the Director of the new consolidated department of Wayne County Department of Health, Veterans and Community Wellness where he oversees all health, wellness and human services for the 13th largest County in the Nation.  Dr. Hammami is responsible for promoting and assuring population health, community wellness and quality of life by providing, maintaining, developing and coordinating a wide-range of innovative and fiscally responsible educational and health services including technology intitatives.

Dr. Hammami is a member of several professional and honor societies and has been an invited speaker nationally and internationally in addition to having many publications in various peer reviewed medical journals. 

Speech Title: Department of the People - A New Model for Health and Human Services Delivery

In times of limited resources and uncertain economic forecasts, the need for "out of the box" approaches might be the only solution for a health department to maintain its role as the conduit of wellness and population health for its community. 

Wayne County is the 13th most populous county in the United States and is responsible for providing a wide range of direct services to Wayne County residents through its several departments.  These silo operations often did not communicate despite sharing "the human factor" of providing services that impact the social and physical wellness of County residents.  And although services provided by these departments overlapped, similar programs were not connected, thus creating redundancies and inefficiencies.

In an effort to increase efficiency a new model was structured that offers "wrap around" service delivery. The goal is for County residents to have greater access to services and programs that, in the past, required several layers of cross-departmental processes.  This model benefits from sharing resources, grants, and funding streams as a single entity where all residents' needs are provided through one point of service.

 

hughes_picRichard E. Hughes, Ph.D., Associate Professor of Orthopaedic Surgery, Biomedical Engineering, and Industrial & Operations Engineering, University of Michigan; Project Co-Director, Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI); Director, Laboratory for Optimization and Computation in Orthopaedic Surgery

Richard E. Hughes, Ph.D., is an Associate Professor of Orthopaedic Surgery at the University of Michigan. He holds a B.S.E. in Civil Engineering from Princeton University as well as M.S.E. and Ph.D. degrees in Industrial & Operations Engineering from the University of Michigan. He completed a post-doctoral fellowship at the Mayo Clinic in orthopaedic research. He is Co-Director of the Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI), which is a statewide collaboration among 59 hospitals across Michigan dedicated to improving the quality of care for hip and knee replacement patients. He has published over 95 peer-reviewed papers in musculoskeletal science and is a Fellow of the American Society of Biomechanics.

Speech Title: Using registry data and collaboration to drive quality improvement in total hip and knee arthroplasty across the state of Michigan

Total joint replacement (arthroplasty) is definitive treatment for end-stage osteoarthritis of the knee and hip. While it is proven to be highly effective, it carries risk of infection, deep vein thrombosis, pulmonary embolism, mortality, early revision, and other complications. Regional quality improvement collaboratives have been shown to reduce complications and deaths in cardiothoracic surgery. Following the example of those collaboratives, the Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI) was started in 2012 to improve quality of care for hip and knee replacement patients in Michigan. Sixty hospitals participate in MARCQI, and the patient registry has over 140,000 cases. This talk will review the structure and function of MARCQI, with special emphasis on improvements made in reducing the risk of transfusion, readmission, and venous thromboembolism across Michigan.

 

kim_picKyoung-Yun Kim, Ph.D., Associate Professor, Department of Industrial and Systems Engineering, Wayne State University

Dr. Kyoung-Yun Kim is an associate professor in the Department of Industrial and Systems Engineering at Wayne State University, where he directs the Computational Intelligence and Design Informatics (CInDI) Laboratory. Dr. Kim's research focuses on Design Science; Design Informatics; Design Awareness on Manufacturing Processes; Semantic Assembly Design; and Product Life-cycle Modeling. Dr. Kim has received external funding from several U.S. federal agencies including NSF, DMDII, NIDRR, VA-CASE, DOD, and DOE, the Korean Ministry of Knowledge Economy, and industries including Ford, GM, and GDLS. With the funding from the Department of Veterans Affairs, Dr. Kim's team has developed a design evaluation system for reusable medical equipment, which can conduct decision analysis with lifecycle information. He has published over 30 top journal papers and over 50 conference papers in proceedings and numerous technical reports and presentations. Currently, Dr. Kim is a Site Director for the NSF Industry and University Cooperative Research Center (I/UCRC) for e-Design. Dr. Kim is an Associate Editor of Journal of Integrated Design and Process Science.  Dr. Kim received top cited article award (2005-2010) from Journal CAD and 2003 IIE Transactions Best Paper Award. Dr. Kim was a visiting professor at Kyung Hee University, South Korea from September 2013 to June 2014. Dr. Kim's education includes a Ph.D. in Industrial Engineering from University of Pittsburgh. Dr. Kim is a member of IIE, ASME, SDPS, and ASEE.

Speech Title: Reusable Medical Equipment Service Lifecycle Management

The idea of reusable medical equipment (RME) has emerged as economical and sustainable medical applications. Healthcare analytics for RME is an emerging field for handling service quality and reliability. To analyze life-cycle events of RME, the research team develops a medical equipment evaluation and analytics framework, which can identify patterns in hidden cost factors related to RME life-cycle cost. Historical events, such as repair and maintenance of RME and costs incurred over the lifespan provide important insights into the performance of RME. This research goal is to extract decision rules from random forest models and assess these decision rule sets in terms of their accepted prediction rate, correlation, and coverage towards future data. In this research, a real legacy dataset is used to validate the presented rule extraction framework and the process is illustrated. From the unstructured legacy dataset, the random forest regression model extracts rules associated with the total cost and total repair cost of RME. Different experiments are conducted and the best rule sets are identified in the consideration of the highest coverage and prediction performance. From the conducted experiments, this research reports that rules provide knowledge about cost patterns based on individual equipment category. Finally, this presentation discusses how the extracted rules are useful in an RME life-cycle evaluation system based on their performance of prediction. 

 

lavieri_picMariel Lavieri, Ph.D., Associate Professor, University of Michigan

Dr. Mariel Lavieri is an Associate Professor in the Department of Industrial and Operations Engineering at the University of Michigan.  She has bachelor's degrees in Industrial and Systems Engineering and Statistics and a minor in String Bass Performance from the University of Florida. She holds a Master's and Ph.D. in Management Science from the University of British Columbia. In her work, she applies operations research to healthcare topics. Among others, Dr. Lavieri has developed dynamic programming, stochastic control, and continuous, partially observable state space models to guide screening, monitoring and treatment decisions of chronic disease patients. She has also created models for health workforce and capacity planning. Dr. Lavieri is the recipient of the 2016 National Science Foundation CAREER Award, the 2013 International Conference on Operations Research Young Participant with Most Practical Impact Award, and the 2006 Bonder Scholarship. She received the 2009 Pierskalla Best Paper Award, and an honorary mention in the 2010 George B. Dantzig Dissertation Award. She participated in the 2016 Frontiers of Engineering Symposium organized by the National Academy of Engineering. Dr. Lavieri has mentored students who won the 2012 Doing Good with Good OR, the 2013 Society for Medical Decision Making Lee Lusted Award, the 2015 IBM Research Service Science Best Student Paper Award, and the 2016 Production and Operations Management Society College of Healthcare Operations Management Best Paper Award.

Speech Title: Personalizing Management of Glaucoma Patients

This talk discusses work of a multidisciplinary collaboration between the Department of Industrial and Operations Engineering and the Kellogg Eye Institute at the University of Michigan to develop a forecasting tool that assists eye doctors by (a) helping to identify which patients will experience worsening of existing glaucoma, and at what pace, (b) recommending when the patient should next be assessed for possible disease worsening as well as which test to take, and (c) calculating the patient's optimal intraocular pressure. Using novel extensions of linear quadratic Gaussian (LQG) control and Kalman filtering, the forecasts and controls are calculated by incorporating detailed longitudinal testing information from two landmark clinical trials and data on the specific patient for whom the forecasts and recommendations are being made. This tool has the potential to greatly inform doctors' decisions on who, when, and how to treat glaucoma patients in a personalized manner.  The objective is to avoid overtreatment and unnecessary treatment, while giving the patients at highest risk for blindness their best possible chance at preserving their sight in the long term.

 

levy_picPhillip Levy, M.D., M.P.H., Professor and Associate Chair for Research, Department of Emergency Medicine, Wayne State University

Dr. Levy is a Professor at the Wayne State University School of Medicine and the Associate Chair for Research in the Department of Emergency Medicine. He is a Fellow of the American College of Emergency Physicians and the American Heart Association. He serves as a reviewer for the NIH CHAS study section, a member of the Grants Advisory Panel for the Blue Cross Blue Shield of Michigan Foundation and a member of the Scientific Review Committee for the American College of Emergency Physicians. Dr. Levy is a recognized expert in cardiovascular research and has served on the National Heart, Lung and Blood Institute's Working Group on Management of Acute Heart Failure in the Emergency Department: Research Challenges and was as a member of the American Heart Association Scientific Statement Writing Group on Acute Heart Failure Syndromes - Emergency Department Presentation, Treatment and Disposition.

Dr. Levy's research interests center on heart failure and hypertension with a dual focus on acute management and early disease detection. He has been the Principal Investigator for grant projects funded by the Emergency Medicine Foundation, the Blue Cross Blue Shield of Michigan Foundation, the Robert Wood Johnson Foundation's Physician Faculty Scholars Program, and the NIH/National Institute of Minority Health and Health Disparities (5R01 MD005849). He has also served as the project mentor for grants funded by the Fulbright Program for Scholars and Professionals from the Caribbean and Central America and the Henry Ford Hospital Physician Scientist Track Funding Program. Over the past 10 years, Dr. Levy has published more than 100 manuscripts, authored 18 textbook chapters and has been an invited lecturer on cardiovascular disease related topics more than 200 times.

 

li_picYan Li, Ph.D., Postdoc fellow, Computational Medicine and Bioinformatics, University of Michigan

Dr. Yan Li is a Postdoc fellow in the Department of Computational Medicine and Bioinformatics at University of Michigan, Ann Arbor. He received his Ph.D. and M.S. from Wayne State University and B.S. from Xidian University. His primary research interests are Data Mining and Machine Learning with applications to Healthcare Analytics and Bioinformatics. His research works have been published in leading conferences and journals including SIGKDD, ICDM, WSDM, SDM, CIKM, DMKD, and Information Science.

Speech Title:  Data Driven Approaches for Timely and Effective Healthcare Service

Annually the United States of America spends more than $3 trillion on healthcare, and the cost has a trend to grow faster in recent years. Although the cost is high, several patients fail to get timely and effective medical treatments. Therefore, there is a crucial need to predict the recovery time and the death time of patients to achieve timely and effective healthcare services. Albeit the last few years have witnessed an explosive increase of healthcare data in terms of volume, variety and veracity, it is insufficient to build a robust prediction model in various scenarios due to time, geographical and domain inherent constraints. Survival analysis can overcome time constraints to build a robust model at early stage, where there are only a few fully observed patients but numerous of partially observed patients. Transfer learning models can be used to overcome geographical constraints to help the small hospitals from rural areas, where they do not have enough medical records, to build a robust prediction models using the healthcare information exchange. Recently, precision medicine has become a new nation-wide effort, which aims at providing personalized prevention methods and treatment strategies with the integrating of genomic and traditional clinical information. Some of the fundamental challenges that arise in this domain is the presence of longitudinal information of the patients, insufficient number of samples, and preserving patient privacy. This talk will describe several ways to overcome these challenges and discusses several new exciting research directions.

 

lu_picShiyong Lu, Ph.D., Associate Professor, Department of Computer Science, Wayne State University

Dr. Shiyong Lu is an Associate Professor in the Department of Computer Science at Wayne State University, and the Director of the Big Data Research Laboratory. Dr. Lu received his Ph.D. in computer science from State University of New York at Stony Brook in 2002. Before that, he received his M.E. from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing in 1996 and B.E. from the University of Science and Technology of China at Hefei in 1993. He is an author of two books and over 140 articles published in various international conferences and journals.

Dr. Lu's research is supported by NSF, Department of Agriculture, Michigan Tri-corridor, and Wayne State University.  Dr. Lu is an associate editor of International Journal of Big Data and an editorial board member of International Journal of Big Data Intelligence (IJBDI) and International Journal of Healthcare Information Systems and Informatics (IJHISI). He is a senior member of IEEE.

Dr. Lu is currently involved in several big data projects, including NSF projects to develop big data workflow technologies for bioinformatics, automobile industry, and civil engineering projects. Dr. Lu is the PI of the DATAVIEW project, which focuses on the design and development of an online big data workflow system that supports the modeling, execution, and monitoring of big data workflows in the cloud to support large-scale big data analytics.

Speech Title:  Diagnosis Recommendation from Medical Text Record

Finding diagnosis recommendation from medical text record is an important and challenging problem. This issue has huge effect on enhancing the patient quality of life. Existing systems for this problem require a large set of diagnosis-labeled patient medical records, which might not be available in many cases. To overcome this limitation, in this work, we propose a semi-automated analysis framework to increase the size of labeled patient medical records. In this approach, at first groups are separated from the dataset; then by forcing certain relative least support to a specific bunch, the incessant examples and their comparing marks are obtained. As a result, unlabeled information is named by appointing them to the closest cluster. Finally, we group patients on the produced new dataset and prescribe the analysis name by applying continuous example mining once more. To assess the effectiveness of our proposed approach, extensive experiments have been conducted over the i2b2 dataset, which show the preliminary results of our approach are promising.

 

mccarthy_picJohn McCarthy, Ph.D., M.P.H., Director, SMITREC, Office of Mental Health Operations; Investigator, Center for Clinical Management Research, Department of Veterans Affairs; Research Associate Professor, Department of Psychiatry, University of Michigan

Dr. McCarthy's research interests include access and quality of care among veterans with mental illness, suicide risk and suicide prevention, mental health program evaluation, mental health services operations and performance assessment, long term care for patients with mental illness, and integration of mental health with primary care. He directs the VA Serious Mental Illness Treatment Resource and Evaluation Center (SMITREC), a national program evaluation center in the Department of Veterans Affairs Office of Mental Health Operations. He holds a B.A in History from Brandeis University, an M.P.H. in Health Behavior and Health Education from the University of Michigan, and a Ph.D. in Health Services Organization and Policy from the University of Michigan.

Speech Title:  Using Predictive Modeling in Suicide Prevention: Supplementing Current Strategies for Suicide Prevention in the Veterans Health Administration

Dr. McCarthy will present about efforts to monitor suicide mortality among individuals receiving care in the Veterans Affairs health system.  He will describe trends in suicide rates among VA patients, overall and in relation to rates in the general US adult population.  He will discuss the rationale for predictive modeling as a tool to supplement VA suicide prevention efforts.  And he will discuss development and application of suicide predictive modeling in the Veterans Affairs health system.

 

piette_picJohn D. Piette, Ph.D., Professor of Health Behavior and Health Education, and of Internal Medicine; Director, Center for Managing Chronic Disease; Senior Research Career Scientist, VA Ann Arbor Center for Clinical Management Research

Dr. John Piette is a Professor of Public Health and Internal Medicine, the Director of the University of Michigan Center for Managing Chronic Disease, and a VA Senior Research Career Scientist.  His research focuses on developing and evaluating novel strategies for using patient-facing health technology to improve the accessibility and quality of care for patients with chronic illnesses.  Much of this work focuses on the use of mHealth monitoring systems in socioeconomically vulnerable populations in the U.S. and low-income countries of Latin America. Dr. Piette has been the PI on multiple VA, NIH, and AHRQ-funded randomized trials of disease management services supported by communication tools for patients and informal caregivers, such as automated telephone assessment and behavior change calls (IVR). Ongoing trials include: two NIH trials using mobile health support for self-management and caregiver assistance among patients with diabetes and with depression in safety-net health systems, a VA trial using artificial intelligence to improve the patient-centeredness of self-care assistance among people with back pain, and an implementation study of a similar intervention to improve chronic pain patient outcomes in VA outpatient clinics (CBOCs) in Indianapolis, Boston, and Palo Alto. 

Speech Title:  Advances in Mobile Health Using Simple Devices

In this talk he will review some of the most important recent advances in mobile health using standard cell phones, i.e., using automated calls (IVR), and text messaging (SMS) to reach socioeconomically-vulnerable patients without smartphones - including people living in low-income countries. In particular, he will highlight engineering-related advances such as the use of machine learning principles and other adaptive algorithms to make mobile health interventions more efficient, effective, and engaging for users.  As time permits he also will mention some important hardware issues in order to make IVR/SMS systems scalable for low-resource environments without a substantial informatics and telecommunication infrastructure.

 

trewn_picJayant Trewn, Ph.D., Fellow of American Society of Quality; Lecturer, Department of Industrial and Systems Engineering, Wayne State University

Dr. Jayant Trewn is an Industrial Engineer specializing in Quality Systems design, development, implementation and management. Dr. Trewn has accumulated over a decade of experience working in healthcare organizations such as Spectrum Health Medical Group, Beaumont Hospitals, and service organizations such as Thomson Reuters and Lason Systems, where he built healthcare and service delivery process improvement programs based on lean, Six Sigma and PDCA concepts.  He also worked for two years at Thomson Reuters working as Director of Quality Assurance, IP and Science division, managing the quality of acquisition of data for scientific research. Dr. Trewn has also taught Higher Secondary Calculus and Statistics at University Liggett School. He is a robotics mentor and coach and he coaches high school girls' field hockey.

Dr. Trewn has been teaching quality engineering since 1997. In his role current role as Lecturer of Industrial and Systems Engineering at Wayne State University and in the past as Adjunct Professor at Lawrence Technological University, Wayne State University and Oakland University, in addition to giving quality engineering talks, seminars and workshops at numerous national and international conferences.  He has also served as a Research Analyst for Wayne State University, Center for Urban Studies and Office of Strategic Planning from 1993 to 1999.

Dr. Trewn has written three books, Kaizen Demystified (MCS Media, Inc.), Practical Lean Sigma for Healthcare Kaizen Demystified (MCS Media, Inc.) ad Multivariate Statistical Methods in Quality Engineering (McGraw-Hill Education) and he has been published in international journals.  He is a Fellow of ASQ and he holds a Ph.D. in Industrial Engineering from Wayne State University. He earned his MBA in Information Systems at Wayne State and his bachelor's from Madras University in India.

Speech Title: Identifying error-prone Human-Machine interfaces in Healthcare Delivery Systems

Human factors engineering design criteria are steadily getting recognized as a valuable quality management tool to build failure proofing into healthcare processes. In the past 20 years as technology in the form of advanced diagnostic machines, robotics surgery, nurse job aids, etc. are rapidly being implemented, the possibility of improper interfaces or failure prone man-machine interfaces are also rapidly rising.  This presentation will expose quality management, improvement and design engineering participants to human factors criteria that may be used to evaluate the healthcare processes (man-machine coupled systems) and also strategize on selecting man-machine interface systems that have the best work interfaces so that performance improvement and process design initiatives can have lower chance of failures and also to build error prevention into the process.  

 

vaidya_picSham Vaidya, IBM Distinguished Engineer; Director, Watson Solutions Lab, IBM Watson Group

Sham Vaidya is an accomplished IBM Executive with the IBM Watson Group. He currently leads the Watson Solutions Labs within IBM to drive solutions, accelerators and prototypes into the market. His expertise is in using IT Consulting and building solutions from developing strategies that enable enterprises to adopt Artificial Intelligence technologies to enterprise architectures. His Watson expertise is around dialogue/conversations, natural language processing, chatbots, unstructured data, intents and entity determinations in implementing market facing cognitive solutions. His expertise is in defining solutions and integrating systems to edge based technologies such as mobiles or tablets and providing an end to end solution with a maniacal focus on business value. Vaidya has over twenty-five years of experience spanning a diverse mix of clients and provides ongoing leadership across the IBM organization. His activities provide management and technical direction for CxOs, executive management and technical direction to IT staff members.

Speech Title:  Solving Healthcare problems using cognitive technologies

The cognitive era is here. This discussion discusses the core elements of cognitive technologies within IBM that make up its product space to the marketplace. See how cognitive capabilities establish a relationship between machines and humans and how the role of technology is changing from an enabler to an advisor in the healthcare industry. A new set of business models converging across several healthcare categories are emerging with cognitive technologies in the center, such are video recognition, machine learning, and natural language processing. The IBM Watson team is breaking new grounds at healthcare organizations. Vaidya will further be presenting a case study on a healthcare organization that adopted cognitive learning capabilities to improve its business processes and provided deeper customer insights in solving health related issues.

 

williams_picTimothy Williams, Lean Manager, Performance Management & Innovation, Detroit Medical Center

Tim Williams is a Lean Manager for the Detroit Medical Center.  His responsibilities include supporting hospitals' Lean Daily Management programs as well as facilitating improvement efforts across the organization primarily utilizing Lean-based tools & techniques.  Williams' experience includes a background in finance, project management, and continuous improvement in both the retail industry and healthcare.  He has led a diverse array of projects spanning business finance, sustainability, procurement, and hospital and healthcare operations.  His goal is to drive improvement efforts that enable staff and providers to deliver an exceptional and predictable experience for their patients, and to do so in a financially-viable way.

Williams moved to Detroit in July 2016 and currently lives in Berkley.  He enjoys taking advantage of Michigan's beautiful golf courses and all that the Metro Detroit area has to offer.  Tim is a Lean Six Sigma Green Belt and holds a B.S. in Finance from Indiana University's Kelley School of Business.

Speech Title:  Lean at the DMC - The Journey To Deliver Value-Based Care

With increasing pressures put on healthcare organizations to improve outcomes with more constraints on current and future resources, the need to have highly-effective systems and processes in place has become of the utmost importance.  So how does the DMC meet the needs of its patients, providers, and staff, while also improving quality and reducing cost?

Detroit Medical Center, like many healthcare organizations, has been adopting Lean principles to help guide the organization in its mission to deliver high-quality, efficient, and cost-effective care.  With a team of Lean Managers and Project Managers, the Performance Management & Innovation team (PMI) is a pivotal group that supports a wide array of programs and initiatives across its hospitals and clinics.  In this presentation you will have the opportunity to learn about what the PMI program looks like in the DMC and how those individuals drive value through every member of the organization via the application of Lean tools.

 

yang_picKai Yang, Ph.D., Professor, Department of Industrial and Systems Engineering; Director, Healthcare Systems Engineering Group, Wayne State University

Dr. Kai Yang is a Professor of the Industrial and Systems Engineering Department at Wayne State University. Dr. Yang has been the Director of the Healthcare System Engineering Group of Wayne State University since 2009.  Dr. Yang's areas of research include statistical methods in quality and reliability, healthcare system engineering, data analytics and applications. His research has been supported by NSF, the VA, Siemens, General Motors, Ford, Chrysler, and many others. He is the author of eight books in four languages, and hundreds of publications. He speaks frequently in international conferences, and research seminars, and consults for industrial corporations.  Dr. Yang serves important roles in American Society of Quality and Institute of Industrial and Systems Engineering (IISE), and serves various editorial roles for many journals, especially his leadership role in two flag journals of IISE.

Dr. Yang is one of the founding proposal writers and an academic faculty advisor of VA Center of Applied Systems Engineering (VA CASE), by far the largest national engineering resource center sponsored by the US Department of Veteran Affairs whose mission is to promote the use of industrial engineering to improve healthcare systems. Dr. Yang's healthcare system engineering group works closely with VA CASE and Dr. Yang is the PI of 28 healthcare system engineering-sponsored projects.  Dr. Yang has been a quality engineering coach to several famous international companies, including Apple Inc. and Siemens Energy.  Dr. Yang obtained both his M.S. and Ph.D. from the University of Michigan.

Speech Title: Overview of Healthcare Systems Engineering

American health care industry is facing increasing challenges featured by increasing health care cost, mediocre health care quality, and low operating efficiency.  However, based on a 2005 report by the National Academy of Engineering (NAE), and Institute of Medicine (IOM), the health care sector has been relative slow to adopt and apply industrial and systems engineering tools and practices, or health care systems engineering. In this talk, he will discuss the important success factors and our framework for health care systems engineering to effectively improve health care industry's performances.  Based on eight years of extensive work with the VA's Center of Applied System Engineering, all industrial and systems engineering methodologies, ranging from human factor engineering, operations research and statistical method; can play important roles in health care industry. He will illustrate the effectiveness of health care systems engineering with projects in the area of health care access improvement, human reliability, and readmission reduction.

 

yu_picSusan Yu, Chief of Quality and Performance, John Dingell VA Medical Center

Susan (Qian) Yu is the chief of Quality and Performance at the John D. Dingell VA Medical Center. She is also the senior technical consultant of the VA Center for Applied Systems Engineering (VA-CASE), prior to joining VA; Yu was a vice president in a computer software company since 1993.

Yu was certified Six Sigma Master Black Belt from ASQ, she holds two master degrees in computer science from Jilin Technology University in 1993 and industrial and systems engineering from Wayne State University in 2008. Yu has 15 years of working experience in computer information technology and 11 years in Lean and Six Sigma implementation. She has lead more than 100 Lean and Six Sigma projects successfully. Some of the projects are published in refereed journals.  She was awarded an innovation grant in healthcare system redesign of VA administration and she received excellent employee award from Detroit Federal Executive Board in 2009 and was awarded Federally Employed Women of the Year in 2012.

Speech Title: Quality, Safety and Value team in the Detroit VA

VHA is the largest integrated health care system in the United States, within its budget of $51.4 billion (2011), VHA delivered clinical services to 6.1 million out of 8.5 million enrolled Veterans. VHA operated a wide range of facilities and program including 152 hospitals, more than 800 hospitals and community-report summarizes performance data for clinical quality and patient safety for all VA medical facilities.

VA started lean journey since 2006 and established four Veteran Engineering Resource Centers in 2009. Since 2013, lean team was recommended to merge with Quality management team with the new name "Quality, Safety and Value". In this talk we will discuss how the Detroit VA Quality, Safety and Value team facilitates accreditation, risk management and lean transformation.

 

zhang_picMin Zhang, Ph.D., Assistant Professor of Radiology, College of Medicine, Mayo Clinic

Dr. Zhang is an imaging scientist and an Assistant Professor of Radiology at Mayo Clinic. He is also an adjunct faculty member of the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. He received a Master's degree and Ph.D. from Arizona State University in Industrial Engineering. Dr. Zhang is a member of IEEE and the Society of Imaging Informatics in Medicine (SIIM). He also serves on the Healthcare Professional Editorial Board of IISE Transactions on Healthcare Systems. Dr. Zhang's research interests are healthcare informatics, machine learning and data mining in medical images.

Speech Title: Radiology Informatics: Medical Imaging Data Integration and Analytics

The patient safety, imaging exam quality and the imaging device's utilization are the key concerns in the daily Radiology operations. To monitor the patient safety, assure the imaging quality and track the image devices' utilization are important and always labor-intensity tasks. In this presentation, a novel tool named DICOM Index Tracker (DIT) will be introduced to harvest rich information available from Radiology imaging devices. It is designed to capture and maintain longitudinal patient-specific exam indices of interests for all diagnostic and procedural uses of imaging modalities. Thus, it effectively serves as a patient safety monitoring, quality assurance and imaging devices tracking tool. Analytics applications using DIT like imaging device utilization study, automated monitoring of Abdomen Pelvis CT Exams and fluoroscopy simulations will also be discussed.

 

zhu_picDongxiao Zhu, Ph.D., Associate Professor, Department of Computer Science, Wayne State University

Dr. Zhu is currently an Associate Professor at Department of Computer Science, Wayne State University. He received his Ph.D. from University of Michigan, his master's from Peking University and his bachelor's from Shandong University. His research interests have been in areas of machine learning and data science with applications to big data in bioinformatics, health informatics, natural language processing and multimedia. Dr. Zhu has published over 60 peer-reviewed publications and numerous book chapters and he served on several editorial boards of scientific journals. Dr. Zhu's research has been supported by both NIH and NSF and he has served on multiple NIH and NSF grant review panels. Dr. Zhu has advised numerous students at undergraduate, graduate and postdoctoral levels and his teaching interest lies in programming, data structures and algorithms, machine learning and data science.

Speech Title: Machine Learning Approaches to Precision Medicine

Eradicating health disparity is a new focus for precision medicine research. Identifying patient subgroups is an effective approach to customized treatments for maximizing efficiency in precision medicine. Some features may be important risk factors for specific patient subgroups but not necessarily for others, resulting in a potential divergence in treatments designed for a given population. In this talk, I will first introduce a tree-based method, called Subgroup Detection Tree (SDT), to detect patient subgroups with personalized risk factors. SDT differs from conventional CART in the splitting criterion that prioritizes the potential risk factors. I will then introduce a new deep feature selection method based on deep architecture. Our method used unsupervised deep learning techniques for feature representation and learning in higher-level abstraction. We applied our methods to analyze a clinical hypertension (HTN) dataset, investigating significant risk factors for hypertensive heart disease in African-American patients. The results show that our subgroup detection, feature learning and representation approach leads to better results in comparison with otherwise.