Digital data has become the lifeblood of healthcare, touching every facet of the delivery system and informing – sometimes transforming – the way clinical decisions are made and operational strategies are developed and deployed.
And, as anyone who is a healthcare decision-maker knows all too well, there's more data than ever before – structured, unstructured, semi-structured; labs and imaging; genomic and proteomic; patent-generated data; social determinants of health – with more being amassed in electronic health records, connected devices and data lakes every day.
Thankfully, the technology used to access, analyze and put that data to work is also getting more advanced on a continuing basis. Ever more sophisticated analytics software and precise predictive algorithms are at our fingertips. Artificial intelligence and machine learning tools are finding their place and hospitals and health systems large and small.
At the same time, many more basic challenges – related to information governance, say, or simple data literacy – continue to vex many healthcare providers. But whether data beginners, or more advanced analytics innovators, everyone can learn more about how to make clinical and financial information work for higher-quality and more efficient care.
At the two-day HIMSS Big Data and Healthcare Analytics Forum in Boston, October 22 and 23, experts from across the care delivery spectrum will offer advice, perspective and best-practices across four different areas that are poised to transform or be transformed by advanced analytics.
The morning of Day 1 will focus on basic challenges related to basic questions of blocking and tackling: data governance, analytics strategies, foundational knowledge.
For instance, Jason Burke, chief analytics officer at UNC Health Care & School of Medicine, will speak alongside Philip Bradley, regional director, North America at HIMSS Analytics, about UNC's journey up the ladder of HA's International Adoption Model for Analytics Maturity, explaining how it reached Stage 7 by treating data as an asset, advancing system-wide adoption of data visualization and enabling self-service analytics.
And I'll be moderating a panel focused on best practices for data governance: Douglas Gentile, MD, CMIO at University of Vermont Health Network; Michael Johnson, data scientist at St. Charles Health System, and others will offer their hard-won perspective on how to manage the availability, usability, consistency, data integrity and security of health information.
The afternoon of Day 1 will begin to explore, through specific use cases, how data of all shapes and sizes can be put to work improving outcomes and enabling population health management.
Lynda Chin, executive director of the REDI (Real-World Detection and Intervention) Platform at the University of Texas System will discuss her approach to "empowering clinicians and patients to be proactive" in their data-driven efforts to improve.
And Simon Jones, MD, professor in the Department of Population Health, and Harry Saag, MD, medical director of Network Integration and Ambulatory Quality, both of NYU Langone, will show how data science can streamline and enhance care coordination on a large scale, leading to big improvements in quality and efficiency.
On Day 2, the topic schedule will get a bit more advanced. The morning will offer a real-world perspective on operationalizing AI and machine learning effectively.
A leadership panel of several clinical and data science experts will take stock of the current status artificial intelligence in healthcare, and offer some predictions about where it's headed. What we need to do to get their faster while deploying these leading-edge technologies safely? What education do various stakeholders need? What new vocabulary is required? How is AI best woven into operational and clinical processes?
Then, 16-year-old high school student Justin Aronson – the youngest speaker ever at a HIMSS event – will discuss his own computer science bona fides, and explain how he's put publicly available data to work building a website that enables laboratories to determine whether their genetic variant classifications conflict with the assessments of other labs. Data democratization like that will play a key role in the development of machine learning, he says.
And the afternoon of Day 2 will take a closer look at the fast-approaching future of precision medicine, exploring the promise of what smart analysis of genetic and genomic data could mean for personalized care.
Douglas Reding, MD, chief medical officer at Ascension Wisconsin will describe his efforts to spread precision medicine practices enterprise-wide at ther health system, explaining the critical infrastructure needed to tackle genomics at scale, and the use cases for artificial intelligence in analytics and care coordination.
Then, Bat-ami Katzman Gordon, director for Precision Medicine at the Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, and Nadia Haque, director of the Precision Medicine Program at Henry Ford Health System, will offer their own perspectives on expanding precision med programs beyond the inpatient setting – scaling it out into community care, where genetic and social determinant data could best impact personal and population health.
The HIMSS Big Data and Healthcare Analytics Forum is scheduled for Oct. 22-23 in Boston. Register here.
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