AI in Healthcare Summit AI in Healthcare Summit schedule

Be honest – do your AI efforts look more like The Matrix, or a High School Science Fair? Developing an AI solution is a worthy endeavor, but how do you deliver multiples? How do you enable teams? How do you support what you’ve created? In this session we will discuss broader considerations of crafting a growth-enabled, deliverable, vibrant AI operation.

Jess Perkins is a technology executive, with 25+ years in healthcare. His background covers a wide range of experience in leading-edge consulting, solution development, and sales in the healthcare payer, provider, public sector, Pharma, and Life Sciences markets. He has consulted independently, as well as working with companies including Siemens, McKesson, Oracle, Optum, and several mid-sized and start-up organizations.

Mary Jane Dykeman is a managing partner at INQ Law. In addition to data law, she is a long-standing health lawyer. Her data practice focuses on privacy, artificial intelligence (AI), cyber preparedness and response, and data governance. She regularly advises on use and disclosure of identifiable and de-identified data. Mary Jane applies a strategic, risk and innovation lens to data and emerging technologies. She helps clients identify the data they hold, understand how to use it within the law, and how to innovate responsibly to improve patient care and health system efficiencies. In her health law practice, Mary Jane focuses on clinical and enterprise risk, privacy and information management, health research, governance and more. She currently acts as VP Legal, Chief Legal/Risk to the Centre for Addiction and Mental Health, home of the Krembil Centre for Neuroinformatics, and was instrumental in the development of Ontario’s health privacy legislation.

Mary Jane regularly consults on large data initiatives and use of data for health research, quality, and health system planning. Her consulting work extends to modernizing privacy legislation and digital societies, and she works with Boards, CEOs and CISOs, as well as innovation teams on the emerging risks, trends and opportunities in data. Mary Jane regularly speaks on AI, cyber risk and how to better engage and build trust with clients and customers whose data is at play. She is also a frequent speaker and writer on health law and data law. Mary Jane is co-founder of Canari AI, an AI risk impact solution.

Bill Gillis was formally the Vice President, Product Management for the Mayo Clinic Platform. In his role, Bill lead Health Information Technology (HIT) innovations that supported the Mayo Clinic Platform vision. To create a healthier world where personalized, predictive and innovative care is accessible to all. Having worked in health care IT for more than twenty five years, Bill specializes in Next Generation Clinical Insight generation, Accountable Care, Technology driven Population Health solutions, Electronic Health Records (EHR) and interoperability strategies, technologies and deployments. Bill has led multiple enterprise-wide clinical system integrations over his tenure in the HIT space.

Prior to his role with Mayo Clinic Platform, Bill was the CIO for Beth Israel Lahey Health Performance Network. (BILHPN). He led the BILHPN team that architected and deployed what is believed to be the first cloud-based EHR offerings in the country. He was an early adopter of using real-time EHR data over lagged claims data to drive performance in value based care contracts. This use of technology enabled BILHPN to achieve ranking as the #1 performing CMS ACO in Massachusetts and #3 nationally, as well as the #1 nationally performing ACO in quality reporting for multiple years.

Bill is a leading, national authority sought after for his compelling historical perspective on HIT, innovative solutions to current industry technology challenges and his thought leadership. He is often called on to provide expert commentary on emerging trends in health care IT for some of the leading industry news health care brands, Healthcare Informatics, Health IT News, Health Data Management, HIT Outcomes, Health Leaders, SearchHealthIT, Tech Target, Search CIO, and Health Tech Magazine.

Bill has also led public roundtable discussions, panels and notable keynote addresses including at Healthcare Information and Management Systems Society (HIMSS) conferences, Value-Based Care Summit Series, AI in Healthcare Summit, Becker's Hospital Review conferences and Healthcare Innovation’s, Health IT Summit, among others. He has also delivered speeches for Harvard Medical School, Boston University, and Suffolk University.

In his free time, Bill works with several nonprofit organizations whose mission is to improve health care in developing countries through the use of technology. Along with esteemed HIT accomplishments, Bill is also an accomplished motorsport athlete who has competed in several international motorsports events such as the Daytona 200, Isle of Man TT, FIM Supermono World Championship and the Bonneville Speed Trails.

While thousands of machine-learning models are developed and published every year, only a small fraction are implemented into clinical care, and even fewer are successful. Through a successful use case of implementation of a patient triage model for patients with dizziness in a tertiary care center, we discuss some common pitfalls and present a methodology to maximize the chances of a successful development and implementation of an AI model into clinical practice. We discuss how workflow analysis, appropriate accuracy metrics to the clinical problem, model deployment, change management and pilot design apply to the implementation of AI solutions into clinical practice.

Santiago Romero-Brufau, MD, PhD is Assistant Professor of Healthcare Systems Engineering and ENT at Mayo Clinic, where he leads the AI initiatives in the Department of ENT. He is also Adjunct Assistant Professor and a member of the Executive Committee for the Master's in Health Data Science at the Harvard T.H. Chan School of Public Health, where he teaches how to implement machine-learning models into the clinical workflow.

While thousands of machine-learning models are developed and published every year, only a small fraction are implemented into clinical care, and even fewer are successful. Through a successful use case of implementation of a patient triage model for patients with dizziness in a tertiary care center, we discuss some common pitfalls and present a methodology to maximize the chances of a successful development and implementation of an AI model into clinical practice. We discuss how workflow analysis, appropriate accuracy metrics to the clinical problem, model deployment, change management and pilot design apply to the implementation of AI solutions into clinical practice.

Santiago Romero-Brufau, MD, PhD is Assistant Professor of Healthcare Systems Engineering and ENT at Mayo Clinic, where he leads the AI initiatives in the Department of ENT. He is also Adjunct Assistant Professor and a member of the Executive Committee for the Master's in Health Data Science at the Harvard T.H. Chan School of Public Health, where he teaches how to implement machine-learning models into the clinical workflow.

Between leveraging AI health assistants, augmented reality, sensor technology and light grids, the possibilities for the future of AI in healthcare are nearly endless: AI assistants that use personalized algorithmic plug-ins from your doctor’s office to coordinate your in-home care, track your vitals, share exercise tips, order heart healthy foods, tiny drones to bring you medicine, holographic alerts that capture emergencies in real time and send captured video to authorized family and care providers. We have the potential to improve quality of life for all ages, as well as to allow those who need additional care more comfort from the privacy of their own homes. By preventing and reacting to major health incidents, AI could literally save your life. However, no one will be willing to allow these systems into their home and personal care if there is no trust. How can we build that fundamental foundation to get us to the “fun stuff” that can be done with AI?

Announced as one of “12 Brilliant Women in Artificial Intelligence & Ethics to Watch in 2018” by Medium, “Top 100 Innovators in Data and Analytics in 2018” by Corinium Intelligence, and one of “10 Big Data Experts to Know” by Information Management, Cortnie Abercrombie advises teams, organizations, venture capitalists, and startups on driving innovation sustained by responsible AI practices. She is also the founder of AI Truth, a nonprofit advocating for ethical and responsible AI systems creation and use. At IBM she pioneered AI solutions for Fortune 500 companies and is world-renowned for establishing Chief Data Officers and data-driven organizations. Her coverage includes Forbes, Inc. Magazine, Medium, CRN, KD Nuggets, The Cube, CEO Forum, Diversity in Action and Chief Content Officer Magazine.

While thousands of machine-learning models are developed and published every year, only a small fraction are implemented into clinical care, and even fewer are successful. Through a successful use case of implementation of a patient triage model for patients with dizziness in a tertiary care center, we discuss some common pitfalls and present a methodology to maximize the chances of a successful development and implementation of an AI model into clinical practice. We discuss how workflow analysis, appropriate accuracy metrics to the clinical problem, model deployment, change management and pilot design apply to the implementation of AI solutions into clinical practice.

Santiago Romero-Brufau, MD, PhD is Assistant Professor of Healthcare Systems Engineering and ENT at Mayo Clinic, where he leads the AI initiatives in the Department of ENT. He is also Adjunct Assistant Professor and a member of the Executive Committee for the Master's in Health Data Science at the Harvard T.H. Chan School of Public Health, where he teaches how to implement machine-learning models into the clinical workflow.

Good AI can enrich understanding of data by building explanations to highlight what is important. These explanations can be used to reveal issues in data or drive scientific understanding when an AI model outperforms human experts. In this session, I will cover some challenges and pitfalls in developing explanations with AI models and lay out techniques to build and evaluate explanations. As a motivating example along the way, I will discuss our work on detecting new-onset diabetes from electrocardiograms where AI significantly outperforms electrophysiologists.

Rajesh Ranganath is an assistant professor at NYU's Courant Institute of Mathematical Sciences and the Center for Data Science. He is also affiliate faculty at the Department of Population Health at NYUMC. His research focuses on approximate inference, causal inference, probabilistic models, and machine learning for healthcare. Rajesh completed his PhD at Princeton and BS and MS from Stanford University. Rajesh has won several awards including the the Porter Ogden Jacobus Fellowship, given to the top four doctoral students at Princeton University, the Savage Award in Theory and Methods, and an NSF Career Award.

Radiological images such as CT, MRI and PET allude a plethora of tumor-phenotypic features such as shape, size, texture and intensity of a tumor region of interest (ROI) carved by radiologists and further biopsied and examined using immunohistochemistry technique in pathology. Such a specimen then undergoes a variety of genomic and proteomic tests to identify genetic aberrations in patients responsible for the underlying state of their disease and alluding treatment options thereof. Further, a patient’s response to such treatments is also recorded. Such imaging and omic profiles along with the corresponding treatment outcomes data could be correlated statistically and modeled together using several AI techniques to understand which of the features would actually allow the most non-invasive and fastest way to predict a patients disease state and/or their response to a particular therapy. ImaGene, a web-based software platform was designed keeping in mind the robustness, flexibility and transparency required for such a wide data-analysis yielding a systematic report explaining how certain parameter settings affect the results, thereby allowing researchers to steer their efforts in appropriate direction yielding significant outcomes. ImaGene is free to use and has been proven to yield significant imaging-omic associations in breast, head-and-neck and lung cancer cases and is currently being tested on several other cancer types as well. The models build therein (and the reports thereof) are shareable across laboratories and hospitals boosting collaborations achieving consensus in such imaging-omic associations (or predictions) for several cancer types thereby aiding clinical research in cancer.

Shrey Sukhadia has been leading clinical bioinformatics efforts at top tier hospitals in United States, such as Dartmouth-Hitchcock Medical Center, Phoenix Children’s Hospital and Hospital of the University of Pennsylvania. His expertise includes Genomic data analysis, Precision Medicine, Statistics, Artificial intelligence, Imaging-genomics, Transcriptomics, Copy number alterations, Protein structure predictions and protein-protein docking, and Software Engineering.

Through his PhD-research in Radiogenomics at Queensland University of Technology, Australia, Shrey has developed a robust AI-based software named “IMAGENE” for effective integration and analysis of imaging and omics data for cancer patients. IMAGENE has already been tested on Breast, Head-and-Neck, Lung and Brain Cancer datasets and is currently being tested for other cancer types as well. It accommodates both radiology and histopathology (aka tissue slide) imaging data along with omic data such as genomic, proteomic or several patient treatment outcomes and builds robust AI models thereof accompanied by a transparent report. A web app for IMAGENE is freely available at “https://www.imagene.pgxguide.org/index.php” to foster testing of cancer datasets worldwide. It hosts a novel knowledge-base of imaging-genomics’ AI models, thereby aiding the advancement of precision medicine for better (and faster) diagnosis and treatment of cancer patients globally.

Come join us in our efforts to democratize the imaging-genomics analysis for all patients worldwide and provide them with a better future thereof.

While thousands of machine-learning models are developed and published every year, only a small fraction are implemented into clinical care, and even fewer are successful. Through a successful use case of implementation of a patient triage model for patients with dizziness in a tertiary care center, we discuss some common pitfalls and present a methodology to maximize the chances of a successful development and implementation of an AI model into clinical practice. We discuss how workflow analysis, appropriate accuracy metrics to the clinical problem, model deployment, change management and pilot design apply to the implementation of AI solutions into clinical practice.

Santiago Romero-Brufau, MD, PhD is Assistant Professor of Healthcare Systems Engineering and ENT at Mayo Clinic, where he leads the AI initiatives in the Department of ENT. He is also Adjunct Assistant Professor and a member of the Executive Committee for the Master's in Health Data Science at the Harvard T.H. Chan School of Public Health, where he teaches how to implement machine-learning models into the clinical workflow.

Elliot is a Senior Data Scientist at Geisinger's Steele Institute for Health Innovation, where he develops, implements, and evaluates human-centered AI / ML interventions throughout the health system. Elliot graduated from Columbia University with a PhD in Biomedical Informatics where he researched AI tools for chronic disease management and coaching. Previously, he worked in a technical role at Epic in Madison, WI.

Uma Sridharan serves as the Senior Director of Data Analytics as part of the technology and Global Services team at Beckton Dickinson. She joined the company in Feb 2021 and is accountable for analytics value creation from data assets and ecosystems enabling the company’s 2025 bold growth and transformation agenda.

Prior to joining BD, she served in numerous data and analytics leadership roles at Cytiva and GE including Digital Strategy leader for the Cytiva business. During her 20+ years of experience, Uma has progressed through global roles in multiple functions and locations and managed critical product launches and delivery of new data engineering capabilities.

Uma is known for her innovative and results-oriented approach and leads a global team. Uma also supports and mentors’ young engineers through the Asian Associate Resource Group as well as STEM women engineers. Uma received her executive MBA from Columbia Business School and an electrical engineering degree from National Institute of Technology, Suratkal, India.

Gene Tunik is the director for AI+health at the Institute for Experiential AI, as well as the Associate Dean of Research and Innovation and is a faculty member in the Bouvé College of Health Sciences with adjunct appointments in the departments of Bioengineering and Electrical and Computer Engineering. He directs the Laboratory for Movement Neuroscience in the Department of Physical Therapy, Movement, and Rehabilitation Science.

Tunik’s research focuses on the study of human neural control of movement. More specifically, he looks at dextrous control of the hand as it relates to cognitive-perceptual-motor processes in virtual reality applications, human and human-robot interactions, and neurorehabilitation. His lab work includes motion capture (including gaze), physiological recording, virtual reality and robotic technology, cutting-edge non-invasive brain stimulation, and computational modeling techniques. Tunik is also currently studying neural circuits underlying reach-to-grasp organization and coordination for prosthetic, human-computer interaction, and stroke rehabilitation applications; and developing algorithms to improve human-robot collaboration on object handover tasks. His other projects explore early biomarkers of ALS and Identifying declines and reserves in cognitive-motor interactions in aging.

Tunik earned a Bachelor of Science in Physical Therapy from Northeastern, and a doctoral degree in Cellular, Molecular, and Behavioral Neuroscience from Rutgers University, before completing his postdoctoral training at the Center of Psychological and Brain Science at Dartmouth College.

Lei Cheng, MSc, is the Lead Data Scientist for BlueCross and BlueShield of Rhode Island. He has experience in data science across multiple fields and focuses on corporate AI strategy & Care Management AI to better patient outcomes. He has degrees in Applied Data Science and Industrial & Systems Engineering.

Dr. Besa Bauta is the Chief Data and Analytics Officer for the Texas Department of Family and Protective Services, Office of Data and System Improvement, where she oversees data, analytics, and data science initiatives. Previously she served as the Chief Data Officer and Chief Compliance Officer of MercyFirst, an organization that provides health and mental health services for clients in NYC and Long Island. As the divisional lead for the Research, Evaluation, Analytics, and Compliance for Health (REACH) at MercyFirst, she oversaw data integration technology, infrastructure development, research, evaluation, and analytics. She also served as the Chief Analytics Officer for Precision Human Services and was one of the founders of the Social Impact AI Lab (SIAIL), whose goal is to support the digital transformation of the human service sector. SIAIL won the national Social Determinants of Health Innovation Challenge sponsored by the Robert Wood Jonson Foundation in 2020 and was a finalist in the NYU Berkley Center for Entrepreneurship 2021 challenge under her guidance. Dr. Bauta was also the Research Director for the United States Agency for International Development (USAID) community-based education project in Afghanistan and the Senior Director of Research and Evaluation at the Center for Evidence-Based Implementation and Research (CEBIR) at Catholic Guardian Services.

Dr. Bauta is an Adjunct Assistant Professor at New York University and teaches both public health and social work at the graduate and doctoral levels. She holds a Ph.D. from NYU with training in Health Services and Implementation Science, an MPH in Health Promotion and Disease Prevention from NYU College of Global Public Health, and an MSW in Clinical Social Work from NYU Silver School of Social Work. Dr. Bauta has Psychoanalytic training from the Institute of Psychoanalytic Education, NYU Langone School of Medicine, Division of Psychiatry, and a BA from Rutgers University with training in Evolutionary Anthropology, and Biomedical Engineering. She served as the Associate Editor for the journal of Administration and Policy in Mental Health and Mental Health Services Research, for AI in Mental Health. She is an Editorial Board Member of Telehealth and Medicine Today, Business of Data Global Advisory Board member, and one of the founders of Women Leaders in Data and Analytics.

Dr. Bauta has extensive expertise in translational research, evaluation, healthcare data systems and services, and global public health. She was selected by CDO magazine as a Global Data Power Woman and key influencer in shaping the landscape of business and pioneering the field of data and analytics in 2020 and 21, and as the top 100 Data and Analytics professionals in 2021. Dr. Bauta is a published author who has written about mental health, non-communicable diseases, and improving health and mental health systems. She has worked both domestically and internationally on health and mental health projects and her current research focuses on optimizing health systems to improve health outcomes, and protection of health information including ethical practices in implementing Artificial/Augmented Intelligence technologies in human services and healthcare.

Between leveraging AI health assistants, augmented reality, sensor technology and light grids, the possibilities for the future of AI in healthcare are nearly endless: AI assistants that use personalized algorithmic plug-ins from your doctor’s office to coordinate your in-home care, track your vitals, share exercise tips, order heart healthy foods, tiny drones to bring you medicine, holographic alerts that capture emergencies in real time and send captured video to authorized family and care providers. We have the potential to improve quality of life for all ages, as well as to allow those who need additional care more comfort from the privacy of their own homes. By preventing and reacting to major health incidents, AI could literally save your life. However, no one will be willing to allow these systems into their home and personal care if there is no trust. How can we build that fundamental foundation to get us to the “fun stuff” that can be done with AI?

Announced as one of “12 Brilliant Women in Artificial Intelligence & Ethics to Watch in 2018” by Medium, “Top 100 Innovators in Data and Analytics in 2018” by Corinium Intelligence, and one of “10 Big Data Experts to Know” by Information Management, Cortnie Abercrombie advises teams, organizations, venture capitalists, and startups on driving innovation sustained by responsible AI practices. She is also the founder of AI Truth, a nonprofit advocating for ethical and responsible AI systems creation and use. At IBM she pioneered AI solutions for Fortune 500 companies and is world-renowned for establishing Chief Data Officers and data-driven organizations. Her coverage includes Forbes, Inc. Magazine, Medium, CRN, KD Nuggets, The Cube, CEO Forum, Diversity in Action and Chief Content Officer Magazine.