BIG DATA & HEALTHCARE ANALYTICS: A HIMSS EVENT
BOSTON, Oct. 22-23, 2018
Breakfast will be served in the ballroom, be sure to stop by the sponsor tables.
When data from all aspects of our lives can be relevant to our health - from our habits at the grocery store and our Google searches to our FitBit data and our medical records - can we really differentiate between big data and health big data?
Will health big data be used for good, such as to improve drug safety, or ill, as in insurance discrimination? Will it disrupt health care (and the health care system) as we know it? Will it be possible to protect our health privacy? What barriers will there be to collecting and utilizing health big data? What role should law play, and what ethical concerns may arise?
This timely, insightful presentation by one of the world’s leading experts on the intersection of bioethics and law, explores these questions and more from a variety of perspectives, examining how law promotes or discourages the use of big data in the health care sphere, and also what we can learn from other sectors.
The American Medical Association opened its doors to the Integrated Health Model Initiative™ (IHMI) in late 2017 and has been immediately well received. From its public release, individuals from 48 states and 79 countries are on the IHMI platform and 17 collaborating organizations have joined the initiative. This multi-stakeholder and collaborative effort features three key components: 1) a digital platform for collaborative communities around costly and burdensome areas of care; 2) a clinical validation process to review submissions and incorporate feedback from the market place 3) a general purpose, machine-readable information data model. During this session Matt Menning, Director of Engagement for IHMI, will provide an update on IHMI efforts, and attendees will learn how they can get involved with the initiative, and how IHMI plans to support health care improvements.
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.
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.
CIO John Halamka, MD, and his team at Beth Israel Deaconess System use big data and machine learning to create real-world applications that improve clinical decision-making, control costs, and drive efficiencies.
In this session, Dr. Halamka and colleague Paul Cerrato (they collaborated on the book Realizing the Promise of Precision Medicine) cut through the machine-learning hype and present evidence of what it can, and can’t do. Along the way, Dr. Halamka will discuss some of the dozen or so machine-learning projects underway at Beth Israel. These include managing ICU census, decreasing specialty appointment no-shows, and forwarding patient-consent forms to the right OR staffers.
For less than $60 in hosting fees (and a great deal of support and advice from many generous people), 16-year-old high school student Justin Aronson used publicly available data to build a website that enables laboratories to determine whether their genetic variant classifications conflict with the assessments of other laboratories. While not yet incorporating machine learning, Justin’s website demonstrates how access to data is fundamental to scientific innovation.
In this session, Justin - the youngest speaker ever at a HIMSS event - hopes to convince HIMSS and attendees that data democratization is critical to enabling future generations to innovate in a more equitable and effective way than currently possible.
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:
Take this opportunity to mingle with your peers in a relaxed setting to build relationships and establish future partnerships
Efforts are underway to develop an approach to precision medicine across research, clinical and academic settings, but a gap exists in forming a comprehensive model that is rigorous enough to enable the development of comprehensive approaches to harnessing data, yet flexible enough to allow for innovation and creativity.
This session, healthcare visionary Sam Hanna describes model that is flexible, rigorous and addresses the need for structure as healthcare undergoes massive changes and embrace precision medicine
The presentation will address:
Evolving technology in precision medicine combined with conventional treatment therapies have transformed modern medicine, helping clinicians maximize patient outcomes. But though advancements in biotechnology hold incredible promise, precision medicine’s emergence is not without programmatic challenges that inhibit the ability for both doctors and patients to take full advantage of its application and benefits.
Over the past 18 months, Dr. Douglas Reding, chief medical officer, Ascension Wisconsin, has led a national task force working to “enterprise” precision medicine practices across the health system. Dr. Reding will speak to the critical infrastructure components for implementation on this scale, the need for artificial intelligence support and the importance in streamlining collaboration across locations for the future.
This interview with Nadia Haque, director of the precision medicine program at Henry Ford Health System, and Bat-Ami Katzman Gordon, director for precision medicine at the University of Miami Miller School of Medicine, will look at how analytics play a key role scaling a precision medicine program throughout a healthcare organization. What's more, Nadia and Bat-Ami will explain how data integrity and interoperability are key to extending precision medicine into the community to address disparities in care.
Anyone can be blinded by the shiny object, and when data gathering and number crunching start to bear fruit, it’s hard not to jump to conclusions.
In her closing keynote, Rochelle Tractenberg, associate professor of neurology at Georgetown University Medical Center, waves a flag of caution to avoid a credibility crisis. Tractenberg will discuss how, when it comes to data analysis, no matter the field or to what end data is being applied, the route must be open, transparent, and repeatable to be considered credible.
In healthcare, trust and transparency will drive clinical adoption of machine learning and other advanced analytic techniques. And with patient well-being on the line, there’s no place for, among other things, cherry picking results or failing to transparently report the number of analyses that were done.
"The data analyst, whether a professional statistician or just the group member who is most skilled with the analysis software, has an obligation to treat and interpret the data ethically," Tractenberg says. "In a post-truth world, this may be the best way to promote scientific integrity."