The University of Michigan’s Department of Learning Health Sciences partners with the Institute for Healthcare Policy & Innovation and the Office of Research to convene the LHS Collaboratory: a hub for advancing interdisciplinary research and development of learning health systems at the University of Michigan.
Below are the archives of all the LHS Collaboratory's previous events ranging from 2016 to the current academic year.
Thursday, March 23, 2023
“Supporting Clinical and Translational Researchers with Electronic Patient Data"
Keynote Speaker:
Thomas Campion, Jr., PhD, FACMI, FAMIA
Clinical and translational investigators need patient data, especially from electronic health record (EHR) systems, to conduct research, but optimal approaches are unknown. This talk explores an approach for supporting different types of investigators and study designs by matching investigators with informatics tools and services.
View the recording here.
Tuesday, February 21, 2023
LHS Collaboratory Joint Session with UM School of Dentistry
“Interoperability of Electronic Medical and Dental Records: A Learning Health System (LHS) Approach to Sharing Data, Generating Knowledge, Changing Practice and Improving Patient Outcomes”
Keynote Speaker:
Lawrence A. Tabak, DDS, PhD, Performing the Duties of the NIH Director
"The Future is Data Analytics: Many Challenges, Many Opportunities”
View the recording here.
Breakout room #1: Data Integration and Sharing: Opportunities in Entrepreneurship and Research
Wenyuan Shi, PhD, Chief Executive Officer, Forsyth Institute
Presentation: Building the Eco-system to Support Disruptive Technologies in Dentistry
Christopher Balaban, DMD, MSC, FACD, Vice President of Clinical Affairs, Overjet
Presentation: Entrepreneurship and AI/LHS in Dentistry
View the recording here.
Breakout room # 2: Data Integration and Sharing in/out of the Clinic: New Medical and Dental Technologies and LHS Methods to Optimize Care
Alexandre F. M. DaSilva, DDS, DMedSc, Tenured Professor, Director of Learning Health Systems, University of Michigan School of Dentistry
Presentation: Integrating and Sharing Dental and Medical Data in a Diverse Ecosystem – The Learning Health Systems Perspective
Muhammad F. Walji, PhD, Associate Dean, Technology Services & Informatics, Professor, School of Dentistry, UT Health Science Center at Houston
Presentation: BigMouth: Lessons Learned from a Decade of Sharing EHR Data in Dentistry
View the recording here.
Breakout room #3: Data Integration and Sharing in Imaging and Pharmacogenetics
Lucia Cevidanes, DDS, MS, PhD, Thomas and Doris Graber Endowed Professor of Dentistry, Predoctoral Orthodontics Director, University of Michigan School of Dentistry
Presentation: Innovations in Multimodal Imaging Data Integration and Sharing
Amy Pasternak, PharmD, Clinical Assistant Professor of Pharmacy and Clinical Pharmacist, Michigan Medicine
Presentation: Integrating Pharmacogenomics into Daily Practice
View the recording here.
Thursday, January 19, 2023
"A Conversation about Justice and Governance in Learning Health Systems: Legal and Ethical Issues"
Haas Distinguished Chair, Professor of Law and Bioethics, University of California, Berkeley
An interview and discussion with Professor Osagie K. Obasogie, JD, PhD. Facilitators Jodyn E. Platt, MPH, PhD, Azia Harris-Martin, and Salomé Viljoen.
Thursday, December 1, 2022
"Double-Edged Sword”: Genetic Data Sharing and Implications for the Learning Health System"
Kayte Spector-Bagdady, JD, MBioethics
Interim Co-Director, Center for Bioethics & Social Sciences in Medicine Assistant Professor, Obstetrics & Gynecology
University of Michigan Medical School
In contrast to the laborious and expensive process of generating genetic datasets de novo, academic genetic researchers are increasingly using large and inexpensive “secondary” research datasets held by government, consortia, and industry for their work. Choosing between different kinds of data providers is about more than just convenience, however, it can also have important implications for the kind of science advanced and to which communities it will generalize. This talk will explore the factors driving researchers to select certain datasets for their work as well as their experiences sharing to, as well as using, shared data resources. As researchers wait for the new National Institutes of Health’s “Policy for Data Management and Sharing” to go into effect in January 2023, this talk will explore who ultimately carries the burden of increasing data sharing requirements
Tuesday, November 8, 2022
Health Equity Reviews for AI in Health and Medicine
Assistant Professor
Johns Hopkins Bloomberg School of Public Health
In this talk, Professor Ferryman will discuss the merits and challenges of conducting health equity reviews of artificial intelligence (AI) tools used in health and medicine. The talk will examine how interdisciplinary approaches from the social sciences, bioethics and humanities, and computational fields can be involved in the development of concepts, methods, frameworks, and guidelines for understanding and governing digital health tools.
Dr. Kadija Ferryman's presentation slides
Thursday, October 20, 2022
Explainability - AI and Ethics
Clara L. West Professor of Ethics and Philosophy
Director of the Center for Ethics and Policy at Carnegie Mellon University
Explainability Is Not the Solution to Structural Challenges to AI in Medicine
Explainability is often treated as a necessary condition for ethical applications of artificial intelligence (AI) in Medicine. In this brief talk I survey some of the structural challenges facing the development and deployment of effective AI systems in health care to illustrate some of the limitations to explainability in addressing these challenges. This talk builds on prior work (London 2019, 2022) to illustrate how ambitions for AI in health care likely require significant changes to key aspects of health systems.
John and Melinda Thompson Director of AI in Medicine (Integration lead), Bioethicist
The Hospital for Sick Children
On the Inextricability of Explainability from Ethics: Explainable AI does not Ethical AI Make
Explainability is embedded into a plethora of legal, professional, and regulatory guidelines as it is often presumed that an ethical use of AI will require explainable algorithms. There is considerable controversy, however, as to whether post hoc explanations are computationally reliable, their value for decision-making, and the relational implications of their use in shared decision-making. This talk will explore the literature across these domains and argue that while post hoc explainability may be a reasonable technical goal, it should not be offered status as a moral standard by which AI use is judged to be ‘ethical.’
The session is moderated by Karandeep Singh, MD, MMSc, Assistant Professor of Learning Health Sciences, Assistant Professor of Internal Medicine, University of Michigan.
Dr. Melissa McCradden's presentation slides
Thursday, 9-22-2022 LHS Collaboratory Kickoff Poster Session
"A Celebration of Learning Health Systems at the University of Michigan"
Complete list of poster titles, authors, and affiliations
Tuesday, June 21, 2022
Restructuring Health Systems for Learning: Building Equity into the Learning Health System
Lauren A. Taylor, PhD, MDiv, MPH
Dr. Lauren Taylor's presentation slides
Tuesday, June 14, 2022
LHS Collaboratory Workshop: Operationalizing Learning Communities
View all workshop presentation slides
This virtual workshop will review the concepts behind Learning Communities, which are foundational to Learning Health Systems. We will explore the phases of all learning communities, regardless of size or problem of interest: planning, initiating, implementing, and sustaining. Participants will engage in a collaborative activity to develop a learning community. Please join us either individually or as part of a team.
Tuesday, May 14, 2022
LHS Collaboratory Workshop: Learning Health Systems 101
This virtual workshop will review the basic concepts behind Learning Health Systems including the learning cycle, infrastructure, and learning communities. Participants will engage in a collaborative activity to design a learning cycle.
Dr. Friedman's presentation slides
Tuesday, April 19, 2022
Medical AI and Learning Health Systems
This recording contains Dr. Xiao Liu’s talk as well as the moderated discussion with Drs. Liu and Karthikesalingam.
Medical AI - Three Common Myths on the Path from Code to Clinic
Alan Karthikesalingam, MD, PhD
In this talk, Alan Karthikesalingam will discuss lessons learned in Google's experiences of taking medical AI systems from early research to clinical implementation.
Medical AI - Raising the Bar on Evidence Standards
In this talk, Xiao Liu will discuss existing and new clinical evidence standards as applied to medical AI systems. Her talk will focus on recently published standards to ensure transparency and reproducibility of clinical evidence underpinning medical AI systems, including reporting guidelines such as SPIRIT-AI and CONSORT-AI.
Dr. Xiao Liu's presentation slides
Dr. Karthikesalingam's presentation slides are not public due to proprietary considerations.
Tuesday, March 22, 2022
Network-Based Research
PCORNet and the PaTH Subnetwork
The University of Michigan is an institutional member of PaTH/PCORNet.
In this talk, David Williams describes the organization and processes of the UM site within PCORNet/PaTH, studies in which UM participates, and resources for UM investigators interested in participating in PCORNet studies.
Dr. Kathleen McTigue's presentation slides
Dr. David Williams' presentation slides
Thursday, February 24, 2022
The Invisible Infrastructures and Infrastructuring Enabling Federated Data and Research Networks
Marcelline Harris, PhD, RN, FACMI
The session will describe the landscape history, current status, and future of federated health data networks that are used to support a Learning Health System. Dr. Brown will describe the creation, infrastructure, operation, and uses of several networks from the perspective of a network coordinating center. Dr. Harris will describe insights from participating in multiple networks as a network partner, including infrastructure, governance, and operational lessons learned.
Dr. Jeffrey Brown's presentation slides
Dr. Marcelline Harris' presentation slides
Tuesday, January 18, 2022
Single and Multi-Institutional Experience in Radiation Oncology Constructing a Learning Health System for Machine Learning from Real World Data
This presentation will explore how Big Data Science and Informatics research can overcome deficiencies within the electronic health record and optimize real world data collection. We will discuss examples of how standardized nomenclature integrated into clinical workflow can enable statistical AI methods to advance clinical decision support and improve outcome models. Our successes in radiation oncology come from single multi-institutional, multi-national and multi-professional society collaboration.
Charles Mayo's and Michelle Mierzwa's presentation slides
Tuesday, December 7, 2021
The Current State of US Interoperability and Implications for Learning Health Systems
Interoperability is considered a key capability of a high-performing healthcare system and has been a top policy priority for more than a decade. Implementing interoperability is, however, a complex undertaking – requiring stakeholder coordination that tackles incentives, governance, technology, standards, and more. In this talk, Dr. Adler-Milstein will describe current approaches to interoperability and where we stand with respect to current levels of national adoption. She will then discuss the implications for Learning Health System efforts at different levels of scale.
Julia Adler-Milstein's presentation slides
Thursday, November 11, 2021
Special Fall Workshop: Operationalizing Learning Communities
This virtual workshop will review the concepts behind Learning Communities which are foundational to Learning Health Systems. We will explore the phases of all learning communities, regardless of size or problem of interest: planning, initiating, implementing, and sustaining. Participants will engage in a collaborative activity to develop a learning community. Please join us either individually or as part of a team.
Introduction
Department Chair of Learning Health Sciences
Josiah Macy Jr. Professor of Medical Education
Professor of Information
Professor of Public Health
Charles P. Friedman's presentation slides
Presentation
Building Learning Communities: Thinking about Culture as a Form of Infrastructure
Assistant Professor of Learning Health Sciences
Director of the Medical Education Scholars Program
Alexandra Vinson's Presentation slides
Workshop Presenters
Program Manager
Department of Learning Health Sciences
University of Michigan
Lisa Ferguson's Presentation Slides
Clinical Health Project Manager
Department of Learning Health Sciences
Project Manager
Department of Learning Health Sciences
University of Michigan
Tuesday, October 19, 2021
Making Sense of Unstructured Data
Unlike structured data, unstructured data are often buried within free text clinical narratives that are difficult to analyze and interpret to derive useful insights. Free text cannot be easily categorized in the same way that a structured, numerical data point can, and unstructured data often have nuances that are not easily captured or represented in structured data.
This session will cover methods and techniques for interpreting and converting unstructured text into useful research data using two related, but distinct approaches: (1) Natural Language Processing (NLP), a specialized branch of AI focused on the interpretation and manipulation of human-generated spoken or written data; and (2) information retrieval, which often underlies many search engine technologies. This session will also highlight EMERSE, an open-source information retrieval tool that has been designed to help everyday users work with the free text documents (i.e., clinical notes) in medical records that is now being adopted by other academic medical centers.
Finally, attendees will hear directly from researchers about how they have used these methods and tools to enhance their research by accessing and harnessing the power of unstructured data.
Bringing data to the people: How a secure, self-service, free text search tool can empower clinical research teams and improve productivity
David A. Hanauer, MD, MS, FACMI, FAMIA
Director of MICHR Informatics Program
Associate Professor of Learning Health Sciences
Associate Professor of Informatics U-M School of Information
Clinical Associate Professor of Pediatrics
David Hanauer's presentation slides
Promise of Unstructured Data
Associate Professor of Learning Health Sciences
Associate Professor School of Information
V.G.Vinod Vydiswaran's presentation slides
Using EMERSE to Improve Research Involving Rare Cancers
Surgical Oncology
Director, Sarcoma Research Working Group
Christina Angeles' presentation slides
Making Sense of Unstructured Data
CCMB Affiliate Faculty
Department of Biostatistics
University of Michigan
EMERSE at UCSF Unlocking our De-Identified Unstructured Data!
Chief Information Officer, Clinical and Translational Science Institute (CTSI)
University of California San Francisco
Leslie Yuan's presentation slides
Thursday, September 30, 2021
LHS Collaboratory Kick-0ff
Philip R.O. Payne, PhD, FACMI, FAMIA, FAIMBE, FIAHSI
Janet and Bernard Becker Professor and founding Director of the Institute for Informatics (I2), Associate Dean for Health Information and Data Science, Chief Data Scientist, Washington University in St. Louis
The Learning Health (Record) System
Much has been written about the challenges associated with the use of current EHRs, however the promise of these technology platforms remans vast and mostly under-realized. This presentation will explore the ways in which Biomedical Data Science and Informatics research are helping to realize the potential of EHR technologies in the context of creating an LHS, from the optimization of workflow and human factors, to the generation of reproducible and systemic clinical phenotypes, to the delivery of emergent knowledge to both providers and patients via advanced clinical support systems.
Research Assistant Professor, Department of Biomedical Informatics, Scientific Director for PheWAS Core, Vanderbilt University
Techniques and Challenges for EHR Phenotyping
Electronic health records (EHR) contain a wealth of real world data that can be used for research purposes. However, extracting phenotype information from EHRs can be challenging. EHR phenotyping can be divided into two types: (1) Fast phenotyping which seeks to capture a broad swath of the medical phenome, and is often accomplished using coded EHR data (e.g. billing codes) and (2) slow phenotyping that seeks to achieve high precision and recall for a single phenotype, and often uses multiple EHR data types (e.g. medications, text, lab results). This talk will describe specific use-cases for both fast and slow phenotyping, and review challenges that are commonly encountered in creating research-grade EHR phenotypes.
Professor Payne's presentation slides
Lisa Bastarache's presentation slides
Thursday, June 24, 2021
LHS Collaboratory Summer Workshop: Learning Health Systems 101
This virtual workshop will review the basic concepts behind Learning Health Systems including the learning cycle, infrastructure, and learning communities. Participants will engage in a collaborative activity to design a learning cycle. Registration for this event is limited. Please consider registering early.
Department Chair of Learning Health Sciences
Josiah Macy Jr. Professor of Medical Education
Professor Friedman's presentation slides
Thursday, March 25, 2021
The Swiss Learning Health System (SLHS): A National Initiative to Support Evidence Uptake in Policy and Practice
Dr. Boes and Dr. Mantwill will provide an overview of the SLHS and its key features, as well as its capacity-building efforts to train young researchers in the field of learning health systems, and the development of a centralized metadata repository in support of creating a sufficient large evidence basis to support learning cycles in the Swiss health system. Further, they will discuss lessons learned from the past and the newest developments of the SLHS in light of a second funding phase supported by the Swiss government.
Promoting and supporting the uptake of evidence and evidence-informed decision-making in health-systems related policy and practice is a challenge. In Switzerland, the need to address this matter has been increasingly emphasized by different actors in the health system. In particular, the lack of comprehensive coordination efforts in the field of health services research, and subsequent knowledge translation activities, has been stressed.
In response, the Swiss Learning Health System (SLHS) was established as a nationwide project in 2017, currently involving ten academic partner institutions. One of the overarching objectives of the SLHS is to bridge research, policy, and practice by providing an infrastructure that supports learning cycles by: continuously identifying issues relevant to the Swiss health system, systemizing relevant evidence, presenting potential courses of action, and revising and reshaping responses.
Key features of learning cycles in the SLHS include the development of policy/evidence briefs that serve as a basis for stakeholder dialogues with actors from research, policy, and practice. Issues that are identified to be further pursued are monitored for potential implementation and eventually evaluated to inform new learning cycles and to support continuous learning within the system.
Vice Principal-Department of Health Sciences and Medicine
Professor of Health Economics
Research Manager and Coordinator
Swiss Learning Health System (SLHS), University of Lucerne
Tuesday, February 23, 2021
Digital Transformation in Healthcare for a Diverse World
Keynote speaker:
Bernardo Mariano, Jr.
Director of Digital Health & Innovation, Chief Information Officer (CIO), World Health Organization (WHO)
Topic: WHO's Global Strategy on Digital Health as an Enabler to Achieving SDG 3
Mr. Mariano's presentation slides
Remarks:
Laurie McCauley, DDS, MS, PhD
Dean, William K and Mary Anne Najjar Professor of Periodontics, University of Michigan School of Dentistry
Breakout Session #1 LHS and Pain
Alex F. DaSilva, DDS, DMedSc
Associate Professor
Department of Biologic & Materials Sciences
Director, Learning Health Systems
University of Michigan School of Dentistry
Topic: Integrating Diverse Health Ecosystems for Optimal Pain Treatment, Education, and Research
Dr. DaSilva's presentation slides
Ivo Dinov, Ph.D.
Professor
Computational Medicine and Bioinformatics, Medical School
Associate Director for Education and Training, Michigan Institute for Data Science (MIDAS)
Vice Chair
Department of Health Behavior and Biological Sciences
University of Michigan
Perspective: Data De‐Identification and Clinical Decision Support
Dr. Dinov's presentation slides
Breakout Session #2 LHS and Caries Risk
Margherita Fontana, DDS, PhD
Clifford Nelson Endowed Professor and Professor of Dentistry
University of Michigan School of Dentistry
Topic: Caries Risk Prediction Models
Dr. Fontana's presentation slides
Alonso Carrasco-Labra, DDS, MSc, PhD
Senior Director, Department of Evidence Synthesis and Translation Research
American Dental Association,
Science and Research Institute, LLC
Adjunct Assistant Professor
Department of Oral and Craniofacial Health Science, School of Dentistry,
University of North Carolina
at Chapel Hill
Perspective: LHS and Evidence-based Clinical Practice
Dr. Alonso Carrasco-Labra's presentation slides
Breakout Session #3 LHS and Opioids
Romesh Nalliah, BDS, MHCM
Associate Dean for Patient Services and Clinical Professor of Dentistry
University of Michigan School of Dentistry
Topic: Iteratively Learning about Dental Opioid Prescribing
Dr. Nalliah's presentation slides
Chad Brummett, MD
Bert N. LaDu Professor of Anesthesiology
Senior Associate Chair, Research
Co-Director of the Michigan Opioid Prescribing Engagement Network (Michigan OPEN)
University of Michigan
Perspective: Precision Health in Opioid Management
Dr. Brummett's presentation slides
Thursday, January 21, 2021
Data Standards and Learning Health Systems–Challenges and Opportunities
Learning Health Systems thrive with use of real-world data from electronic health record (EHR) systems in both observational and interventional research to generate real-world evidence. Standardized EHR data can enable the aggregation of data and the generation of real-world evidence that will inform care delivery and improve patient outcomes. Data standards can also increase the speed in which promising evidence-based interventions are disseminated and adopted into new settings. However, despite their great potential, data standards have proven to be difficult and costly to implement, and nationally standardized EHR data continues to elude us.
Speaker: Rachel Richesson, PhD, MPH, MS, FACMI, Professor of Learning Health Sciences
Thursday, November 12, 2020
Dr. Bryk’s talk describes a set of normative challenges that confront us, including ways to shift from pursuing the hot new idea as the silver bullet to continuously improving how core processes and systems in educational institutions work. It is a distinct form of inquiry that values basic research knowledge and rigorous evidence about new programs and interventions, but that also sees efforts aiming to solve problems of practice reliably and at scale as generating its own form of generalizable knowledge. It transforms the core improvement question from “What Works” into “What Works for Whom and under What Set of Context Conditions.”
Speaker:
Anthony S. Bryk, President of the Carnegie Foundation for the Advancement of Teaching
Moderator:
Donald J. Peurach, PhD, Professor, University of Michigan School of Education; Senior Fellow, Carnegie Foundation for the Advancement of Teaching
Discussants:
Elizabeth Birr Moje, PhD, Dean, George Herbert Mead Collegiate Professor of Education, and Arthur F. Thurnau Professor School of Education; Faculty Associate in the Institute for Social Research; Latino/a Studies; and the Joint Program in English & Education, University of Michigan
Caren M. Stalburg, MD, MA, Collaborative Lead for Education; Associate Professor of Learning Health Sciences
Tuesday, October 20, 2020
The October 2020 LHS Collaboratory shared lessons from health advocates working on the frontlines to make healthcare and health more equitable. These thought leaders and do-ers illuminated the transformative power of LHSs - and the diverse and inclusive communities of interest that are collaborating to realize them.
Moderator:
Joshua C. Rubin, J.D., M.B.A., M.P.P., M.P.H., Program Officer, Learning Health System Initiatives
Panelists:
Luis Belén, Chief Executive Officer of the National Health IT Collaborative for the Underserved (NHIT Collaborative)
Danielle Brooks, J.D., Director of Health Equity, Amerihealth Caritas
Melissa S. Creary, Ph.D., M.P.H., Assistant Professor, Department of Health Management and Policy School of Public Health
Thursday, September 17, 2020:
The Collaboratory's fall symposium event showcased the LHS experiences of three research-intensive academic centers that have been promoting LHS methods. Distinguished senior colleagues from Duke, Vanderbilt, and Washington University, described and discussed their institutions' work in this area. They discussed strategies employed, investments made, challenges encountered, and successes achieved.
Discussant:
Carol R. Bradford, M.D., M.S., Leslie H. and Abigail S. Wexner Dean's Chair in Medicine, Ohio State University
Presenters:
Kevin B. Johnson, M.D., M.S., F.A.A.P., F.A.C.M.I., Cornelius Vanderbilt Professor & Chair, Department of Biomedical Informatics Professor; Department of Pediatrics Informatician-in-Chief, Vanderbilt University Medical Center
Christopher J. Lindsell, Ph.D., Professor of Biostatistics, Vanderbilt University; Co-director of the HEAlth Data Science, (HEADS) Center, Vanderbilt University
Philip R. O. Payne, Ph.D., F.A.C.M.I., F.A.M.I.A., Janet and Bernard Becker Professor and Director, Institute for Informatics (I2); Associate Dean for Health Information and Data Science, Chief Data Scientist, Washington University School of Medicine
Michael Pencina, Ph.D., Professor of Biostatistics and Bioinformatics, Duke University; Vice Dean for Data Science and Information Technology, Duke University
Eric G. Poon, M.D., M.P.H., Professor of Medicine, Chief Health Information Officer, Duke University
Speakers: Charles P. Friedman, PhD, Karandeep Singh, MD, MMSc, Allen Flynn, PhD, PharmD, Anne Sales, PhD, RN, and Jody Platt, MPH, PhD
Speakers: Michael Lanham, MD, and Karandeep Singh, MD, MMSc
February 25, 2020: Value Proposition of Learning Health Systems
Speakers: David F. Massaro, MD, MHCDS, MBA, FACHE, Deputy Chief Medical Officer, Veterans Health Administration, Department of Veterans Affairs
Speakers: David H. Roberts, MD, Dean for External Education and Associate Professor of Medicine, Harvard Medical School
Speakers: Charles Friedman, PhD, Chair of Department of Learning Health Sciences, Surveillance and Prevention of NCDs: Applying LHS Principles to Kidney Disease,
Rajiv Saran, MBBS, MD, MRCP, MS, Director of USRDS Coordinating Center, Developing a Community-Based Learning Health System,
Part 2, and
Michael Klinkman, MD, MS, Co-Director of Great Lakes Research Into Practice Network
Speakers: Harold P. Lehmann, MD, PhD, FACMI, Director of Research and Training, Division of Health Sciences Informatics, Johns Hopkins University School of Medicine and Stephen M. Downs, MD, MS, Director Children's Health Services Research, Indiana University
Speaker: William D. Smoyer, MD, Director of Center for Clinical and Translational Research, Nationwide Children's Hospital
Speakers: Joesph M. Piffaretti, Senior Director, Corporate and Foundations Relations,U-M Medical School
Speaker: Allen Lichter, MD, FASCO, former Chief Executive Officer of the American Society of Clinical Oncology and former Dean of the U-M Medical School
Speaker: Sachin Kheterpal, MD, MBA, Associate Dean for Research Information Technology and Associate Professor of Anesthesiology, U-M Medical School
