MACHINE LEARNING & AI: A HIMSS EVENT

LAS VEGAS, NV - MARCH 5, 2018

HIMSS18 Annual Conference
Wynn Las Vegas
Mar. 5, 2018

Schedule

8:00am - 8:30am
Breakfast and Badge Pick Up
Lafleur

Breakfast will be served in the ballroom so make sure to stop by the sponsor tables. 

8:30am - 8:35am
Opening Remarks
Lafleur

Tom
Sullivan
Editor-in-Chief
Healthcare IT News

8:35am - 9:05am

Keynote

Trust, Transparency and Transformation
Lafleur

One of the biggest mistakes healthcare stakeholders make is believing that technology, include machine learning and AI, solves problems. It does not. Technology is a tool, part of the solution. And the real challenge is implementing that tool into workflows – existing or new – in a way that makes sense, and most importantly, drives the desired results.

In this opening keynote, renowned physician and scientist Lynda Chin takes a hard look at what must happen for machine learning and AI to move beyond hype and deliver on its promise to transform healthcare.

At the end of the day, it boils down to:

  • Trust & transparency: Has the algorithm withstood peer review. Is the source code trustworthy, and does it perform as well or better than a human?
  • Quality data: Without good data, machine learning is worthless.
  • Leadership: Machine learning requires domain expertise, not technical.
  • Training: How do you assure the algorithm is being used as intended?

These days, it seems, everyone wants to jump on the machine learning bandwagon. But real success, real change, as Chin will explain, depends on healthcare laying the proper foundation.

Lynda
Chin
Associate Vice Chancellor for Health Transformation and Chief Innovation Officer for Health Affairs
University of Texas System’s Institute for Health Transformation

9:05am - 9:20am
Intel
Lafleur

Coming soon. 

Jennifer
Esposito
Worldwide General Manager of Health and Life Sciences
Intel Corporation

9:20am - 9:50am

Leadership Panel

State of the Industry
Lafleur

There’s a ton of buzz around machine learning and artificial intelligence and the role they’ll play in revolutionizing and improving healthcare, but what are these evolving technologies, how are they different, and what are the multiple layers of use?

No complex health IT ever created has been plug-and-play. The same goes for ML and AI. To take full advantage of its potential, will require a lot of work.

In this morning leadership panel, our speakers look at the current state of machine learning and AI in healthcare and address where we are, where we are going, and what we need to do to get their faster.

What education do stakeholders need? What new vocabulary is required? How do you integrate ML and AI into operational and clinical processes? How do you show ROI?

Our speakers will address these questions and others head-on in a fascinating and insightful discussion that sets the stage for the speakers to follow.

John
Showalter
Chief Product Officer
Jvion
Tom
Sullivan
Editor-in-Chief
Healthcare IT News
Zeeshan
Syed
Director
Clinical Inference and Algorithms Program at Stanford Health Care

9:50am - 10:20am

Survey of the Inevitable

The Learning Health System & AI
Lafleur

Over the past several years, artificial intelligence and machine learning, with their ability to analyze, process, and adapt to large volumes of data, information, and knowledge to solve complex problems, have garnered significant investment among the world’s most successful corporations, including Google, Apple, Facebook, Amazon, IBM, GM and Uber.

Healthcare is also now stepping into the AI spotlight in a variety of application areas, including natural language processing (NLP), medical image recognition, clinical decision support, diagnosis, and prediction. But to what degree is healthcare embracing these technologies? What are the perceptions, expectations, sourcing approaches, and initiatives?

In this session, Chilmark Research, a health IT research firm, will shed light on those questions by sharing the results of a survey that included 100+ senior healthcare executives.  

Key areas addressed by the survey:

  • What are the major perceptions of artificial intelligence and machine learning by leading healthcare organizations in terms of applicability, timeframes, and benefits?
  • What are the most compelling health care use cases for AI and machine learning and to what extent are they being pursued in healthcare?
  • How can AI and machine learning enable healthcare organizations to become learning health systems?
Joshua
Rubin
Program Officer, Learning Health System Initiatives
University of Michigan Medical Schools
Ken
Kleinberg
Vice President
Chilmark Research

10:20am - 10:40am
Networking Break
Lafleur

Take this opportunity to mingle with your peers in a relaxed setting to build relationships and establish future partnerships. Coffee will be served in the ballroom area so make sure to stop by our sponsor tables.

10:40am - 11:00am

AI in Action: Three Case Studies

CASE STUDE 1: Analyzing EMR Data to Detect Early Colorectal Cancer
Lafleur

This session will discuss the results of a study that used machine learning to analyze EMR data to identify individuals at high risk of colorectal cancer (CRC).

The study included 9,108 controls and 900 cancer cases among Kaiser Permanente Northwest adults, ages 40-89. The model identified individuals with a tenfold higher risk of undiagnosed colorectal cancer at curable stages, and flagged colorectal tumors 180-360 days prior to usual clinical diagnosis.

The detection model can be applied to broad populations to identify people at increased risk of CRC (in particular, right-sided CRC), and enables health systems to more effectively target colonoscopy resources.

This study also demonstrated the feasibility for the model’s use in a U.S.-based HMO adult population.

This CRC detection model narrows the screening gaps associated with people who decline fecal tests and/or colonoscopies, and instead opportunistically analyzes existing demographic data and CBC tests. 

Key discussion points:

  • Used on routine EMR data, machine learning can help detect colorectal cancer.
  • Machine learning can transform massive datasets into personalized medicine and move towards a value based care model.
  • ‘‘Big Data’’ algorithms can be valuable tools for clinicians managing large patient panels.
Elizabeth
Liles
Investigator
Kaiser Permanente Center for Health Research

11:00am - 11:15am
Why Clinical Augmentation is Necessary for Healthcare AI
Lafleur

The nature of clinical medicine is highly nuanced and often localized. While artificial intelligence algorithms can be leveraged across health systems, human oversight by a clinician is necessary in order to achieve high model performance and accuracy and reduce alert fatigue. This unique approach to machine learning allows AI systems to achieve a positive predictive value (PPV) and sensitivity close, if not at, a level as that of a physician.

Ruben
Amarasingham
Founder and CEO
Pieces Technologies, Inc.

11:15am - 11:35am
CASE STUDY 2: Putting Patients at the Center and Reducing Risk
Lafleur

As Atlanta’s preeminent safety net hospital, Grady Memorial Hospital is an essential resource for many who otherwise would have little to no access to medical services. In an attempt to lower rates of patient illness, complications, and deliver personalized care, a little over a year ago the hospital incorporated artificial intelligence with the system’s mobile integrated health program.

The results have been impressive: Grady has seen a 10% decrease in readmissions for the targeted population and saved almost $700,000 in direct costs - a greater than 500% return on the program. Additionally, included within the intelligence of the machine are the socioeconomic factors such as literacy, income, access to transportation, and proximity to a food desert that drive more than half a patient’s risk.

In this session, among other things, attendees will learn how Grady has integrated AI to prioritize patient visits and to identify:

  • The clinical, behavioral, and socioeconomic factors contributing to the risk of a re-hospitalization.
  • The actions that will best reduce risk while ensuring patient engagement. 
  • Opportunities for AI application across hospital clinical operations.

 

Robin
Frady
Executive Director, B&CI Information Services
Grady Health System

11:50am - 12:10pm
CASE STUDY 3: Predicting Hospital Readmissions at Point-of-Care
Lafleur

Excess unplanned hospital readmissions are a quality of care indicator, and pose a financial burden to hospitals.

In this session, attendees will learn how Children’s Hospital of Pittsburgh at UPMC developed an innovative real-time tool that calculates every inpatient's unique readmission risk, at point-of-discharge, from structured and unstructured elements in the EHR.  The hospital integrated this predictor into its EHR, and when run in silent mode (Jan-March '17), it accurately predicted 80% of discharges with a high-risk of readmission.

The hospital now has an intervention plan (based on nurse calls, home health visits) that has already shown a reduction in preventable readmissions.

Key discussion points:

  • The value (financial ROI, improved quality metrics) of machine learning and AI based technologies for improving specific patient care outcomes.
  • The importance of scientific rigor in developing such applications.
  • The critical nature of collaborative work (MDs, RNs, IT experts, biomedical scientists) in the build and implementation.
Srinivasan
Suresh
Chief Medical Information Officer
Children's Hospital of Pittsburgh of UPMC

12:10pm - 12:40pm

Future Priorties

Facing the Healthcare Implementation Crisis
Lafleur

Despite excitement surrounding machine learning in healthcare, examples of full-fledged integration in day-to-day health system operations remains the exception, not the rule. Duke’s Institute for Health Innovation is in its third year developing and piloting machine learning technologies in clinical care.

To benefit from the full potential of machine learning, healthcare must step back from the trenches to acknowledge breakthroughs in technology, address barriers to progress, and critically reflect on the strategic priorities necessary to bring healthcare into a new digital age.

In this session, speakers will address the good, the bad, and the ugly when it comes to machine learning in healthcare.

Key discussion points:

  • New career development opportunities must be created for clinical trainees to develop next-generation technologies.
  • Methodology and infrastructure must be developed to ensure robust machine learning model performance despite dynamic EHR data.
  • Healthcare must shift focus from celebrating isolated successes to fostering an ecosystem that supports exponential growth of interactions.
Michael
Gao
Data Scientist
Duke Institute for Health Innovation
Mark
Sendak
Population Health & Data Science Lead
Duke Institute for Health Innovation

12:40pm - 1:00pm

Networking with Speakers

Speaker Hub
Lafleur

One of the best ways learn is to network with your peers. This session will provide an opportunity to meet speakers and attendees who have similar privacy and security challenges and discuss solutions to those challenges.

Here's how it works:

Speakers will be stationed at different tables in the ballroom, and attendees can circulate and speak one-on-one or in groups with individual speakers. The speakers have been assigned different topics, but other topics can also be addressed.

Mingle, share and learn in this interactive environment.

Joshua
Rubin
Program Officer, Learning Health System Initiatives
University of Michigan Medical Schools
Jeffrey
Axt
Project Manager/Researcher
Hospital for Special Care
Robin
Frady
Executive Director, B&CI Information Services
Grady Health System
Srinivasan
Suresh
Chief Medical Information Officer
Children's Hospital of Pittsburgh of UPMC
Lynda
Chin
Associate Vice Chancellor for Health Transformation and Chief Innovation Officer for Health Affairs
University of Texas System’s Institute for Health Transformation
Ken
Kleinberg
Vice President
Chilmark Research
Michael
Gao
Data Scientist
Duke Institute for Health Innovation
Mark
Sendak
Population Health & Data Science Lead
Duke Institute for Health Innovation

1:00pm - 2:00pm
Networking Luncheon
Lafleur

Take this opportunity to mingle with your peers in a relaxed setting to build relationships and establish future partnerships. Coffee will be served in the exhibit area so make sure to stop by our sponsor tables.

2:00pm - 2:25pm

The Corporate Mushroom

Move AI Out from the Dark
Lafleur

This session discusses how to accelerate AI within your organization by bringing it out of the dark and being honest with what is and is not realistic, and where gaps exist that inhibit progress.

By walking through several use cases, attendees will learn how to solve for enterprise strategy development, use case prioritization, and key talent requirements.

Key discussion points:

  • How to map out your AI journey, taking into account realistic downstream barriers.
  • How to choose the most effective AI use cases to prove value.
  • How to align the organization and keep people up to speed with ongoing successes and challenges.
Anthony
Lambrou
Corporate Strategy and Innovation
Pfizer

2:40pm - 3:05pm

Build or Buy?

Strategies for Selecting Machine Learning Vendors
Lafleur

This session will discuss considerations for selecting a vendor offering machine learning based products and services.

Key discussion points include:

  • Overview of commercial machine learning products applicable to healthcare.
  • Important factors to consider when selecting a vendor.
  • When to build an in-house team vs. contracting with a vendor.
Pushwaz
Virk
Medical Director, Center for Clinical Analytics and Business Intelligence
Providence St Joseph Health - Northern California

3:05pm - 3:30pm

Enterprise Strategy

Implementing AI into Clinical Workflow: Do This, Don't Do That
Lafleur

Research literature indicates that human limitations, such as cognitive bias, can interfere with clinical decision-making and that artificial intelligence has the potential to improve the process.

The research also indicates that AI technologies may be viewed as a disruptive innovation to workflow and clinical practice. This mean that to implement AI effectively often requires a cultural change and strategy to promote clinician engagement.

This session will discuss an approach to implementing AI at the point of care that does just that.

Key discussion points:

  • Knowing where opportunities for AI technologies fit into clinical practice workflow.
  • Understanding the risks of mismanaging AI innovation integration.
  • Understanding how the integration of AI technology can optimize clinical practice, reducing both risk and error.
Jeffrey
Axt
Project Manager/Researcher
Hospital for Special Care

3:30pm - 3:55pm

Specialized vs. Conventional ML

How They're Different — and Why IT Matters
Lafleur

Machine Learning for healthcare differs significantly from other application domains in terms of the use cases, data resources, operational constraints and evaluation metrics. Despite these differences, the majority of existing machine learning solutions for healthcare applications remain grounded in conventional methodologies that are re-purposed from unrelated application domains.

This session addresses the specific characteristics unique to healthcare that distinguish it from other application domains and creates the need for specialized solutions. It will also discuss the requirements created by these characteristics and how healthcare-focused machine learning technologies should meet these requirements to maximize practical impact.

Key discussion points:

  • Limitations of conventional machine learning for healthcare.
  • Requirements of machine learning in healthcare.
  • Opportunities for substantial gains enabled by healthcare-specialization of machine learning.
Zeeshan
Syed
Director
Clinical Inference and Algorithms Program at Stanford Health Care

3:55pm - 4:20pm

Intervention & Outcomes

Lessons Learned in Advancing Precision Medicine
Lafleur

As the healthcare industry moves to implement precision medicine in care delivery, healthcare systems are analyzing massive volumes of data toward the goal of increasing the accuracy and speed of clinical care. During this transition, healthcare IT managers are facing the challenge of managing different types of data to build systems that provide clear results to clinicians.

Penn Medicine's Chief Data Scientist Michael Draugelis will share the lessons learned from programs that have been developed at Penn to deliver clear benefits in improving outcomes.

Key highlights of the presentation will include how artificial intelligence (AI) can advance treatment and prevention as well as scientific and technical challenges for AI to be successful

Michael
Draugelis
Chief Data Scientist
Penn Medicine

4:20pm - 4:40pm

Closing Keynote

The Road to Healthcare's Promised Land
Lafleur

After decades of lagging behind other industries in the use of data, healthcare is poised for its own data-driven transformation. Journalists describe a not-too-distant future where patients, phones, Fitbits, and physicians march hand-in-hand toward a healthier tomorrow.

But not everyone is jumping for joy.

Some healthcare organizations are so frustrated with the state of health IT that the American Medical Association’s CEO James Madara, MD, last year called digital health the “snake oil of the early 21st century.” Rather than improving care and boosting professional satisfaction, many digital tools, he wrote, don’t work that well, and actually impede care, confuse patients, and waste everyone’s time.  

And then there’s machine learning. Or is it artificial intelligence? Or cognitive computing? Which is which or what is what?

In his closing keynote, Harvard Medical School professor Leonard D’Avolio, who previously led informatics at the VA, gets to the bottom of all this confusion and disgruntlement and examines what’s hype and what’s not. And how together, once a few very real barriers are eliminated, big data and machine learning will better serve doctors, patients and families, and contribute to improved healthcare.

Leonard
D'Avolio
Assistant Professor
Harvard Medical School, Brigham & Women's Hospital

4:40pm - 4:45pm
Closing Remarks
Lafleur

Tom
Sullivan
Editor-in-Chief
Healthcare IT News

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