Parsing the Progress of Health IT at HIMSS16

Since my first HIMSS conference in 2010, my consistent theme has centered on how health IT will get more interesting as it matures. My rationale: the types of problems we can solve become more complex and impactful as more core data become accessible for analysis.

That’s the focus on one of my articles in the HIMSS16 newsfeed, Identifying Signs of Maturity in Health IT at HIMSS16. One sign of maturity includes an increasing volume of partnerships and merger & acquisition (M&A) activity, especially among established firms. I’d like to thank IBM for announcing its acquisition of Truven Health Analytics prior to HIMSS16; that relieves me of relaying that information during the conference! At $2.6 billion, I expect this acquisition to be one of the biggest announced for the health IT and analytics community for the whole quarter and I look forward to learning more about the combined entity next week.

Another sign of maturity can be found in terminology changes. I’ve already seen indications that some specific categories in health IT will be rolled up into larger categories. For instance, the distinction between the EMR and the EHR categories is blurring.[1] Over time, more amorphous categories such as clinical decision support will likely get subsumed into larger categories like clinical intelligence. And, before long, we won’t need a dedicated area to showcase interoperability at HIMSS; instead, we will be able to talk about all the things we can do because of the underlying interoperability between previously distinct systems.

Moving From Meaningful to Valuable Uses of Health Data

With the foundation for ‘meaningful use’ of basic EHR data nearly complete, we can move forward to developing analytic solutions that depend on a solid foundation of process and outcomes measures. We’re making progress understanding the relationships between outcomes and interventions, but in this segment, we’re at very early stages of maturity. However, the shift to a value-based payment system will accelerate developments in outcomes measurement and analysis.

Where to Find Me at HIMSS16

My schedule for HIMSS16 is a mish-mash of 1:1 meetings, press briefings, receptions, keynotes and education session with a few minutes here & there left open to wander the exhibit hall.

A few notable sessions & events where I hope to run into friends and colleagues follow:

• Opening keynotes on Monday, March 1 at 5 pm.
• HIStalkapalooza Monday evening
• #HealthITChicks meetup, Tuesday, March 1 at 10 am, HIMSS Spot
• #TheWalkingGallery meetup, Tuesday, March 1 at 1:30 pm, Xerox booth #8005
• New England HIMSS reception, Tuesday, March 1 at 4:30 pm, Elsevier booth #3039
• Social Media Ambassador meetup, Wednesday, March 2, HIMSS Spot

I look forward to a super-charged week at HIMSS16. I’ll try to live up to the new “Health IT Maturity Champion” moniker that HIMSS Media has coined for me (or maybe not!).

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[1] The HIMSS Analytics team tipped me off to some terminology changes that are likely to occur soon. See my article on HIMSS Analytics LOGIC here: http://www.himssconference.org/updates/himss-analytics-logic-health-it-market-intelligence-value-based-world

Update on Saturday, February 27, 2016 at 11:20AM by Janice McCallum

Note, the time for the SMA meetup is 3:00 - 3:45 pm on Wednesday at the HIMSS Spot: http://www.himssconference.org/session/himss16-trendspotting-social-media-ambassadors

The Semantics of Big Data

I had the pleasure of attending the Big Analytics Road Show in Boston this week. The presenters and sponsors did an outstanding job of describing the “big data” ecosystem. They even offered clear descriptions of Hadoop and MapReduce for non-technies, which is quite an achievement.

The most rewarding aspect of the day’s program, however, was its emphasis on how the data can be used to add value to business decisions. Consequently, the focus wasn’t on acquiring massive quantities of data (although zettabytes and yottabytes were mentioned!)—or even on the value of organizing big data sets. Instead, the program provided many examples of how analysis of structured and unstructured data in tandem can lead to new insights that can improve business processes and marketing decisions.

Years ago, at InfoCommerce Group we coined the phrase “data that can do stuff” to describe the advantages of well-designed data products. In essence, a data product that is designed to meet a defined need of a target audience becomes a decision tool when analytics are applied. With the era of big data upon us, even textual data and real-time streams of behavioral data can be leveraged via semantic and pattern matching technologies to obtain data that can do stuff. Furthermore, the different types of data can be overlaid to achieve higher levels of insight into customer behavior or patient outcomes, for example.

The takeaway point: data analysis tools and techniques that used to be available only to big life-science companies and search engines are now entering a phase where the costs make the technologies more widely accessible. However, as someone mentioned at the Big Analytics event, Gartner Group places big data at the peak of inflated expectations on its hype cycle curve. I agree with Gartner because of the level of noise surrounding big data. Nonetheless, with proper alignment between the data, business goals, and execution, opportunities to benefit from big data—or should I say big analytics—exist today.

Leveraging the Liberated Data

Todd Park, CTO of HHS, gave an inspiring keynote at the Rock Health Boot Camp yesterday that could turn the starkest pessimistic into an optimist about the future of healthcare in the US. From what I know, Park always gives inspiring keynotes, but I want to use his message to connect the key themes I extracted from the Rock Health event (#hcbc) and Health Camp SF Bay (#hcsfbay) on Friday.

My first observation: nearly every speaker referred to the plethora of new apps and technology companies in healthcare. We’re beginning to get inundated by new apps that often compete with dozens of similar apps to do nearly the same thing.

Second, it is a safe statement to say that health remains a siloed ecosystem. Collaboration is improving as a result of internal and external forces, with the HITECH Act and ACA (Affordable Care Act) among the most powerful forces promoting change. But we’re at early stages of figuring out how to share data and collaborate for the good of patient outcomes and overall population health.

Yet in this technology-rich environment, the level of awareness of existing data sources is poor. We can liberate all the data in the world and make it available on the Web, but if entrepreneurs are focused on sexy new gadgets that add to the data explosion but do nothing to help organize and normalize the massive datasets that already exist, we’ll fail to make use of the data in meaningful ways (yes, I used the term “meaningful” on purpose).

Park spent some time describing Healthcare.gov and HealthData.gov and how they can act as a resource for entrepreneurs. I loved his analogy between HealthData.gov and NOAA data. He told an anecdote of how someone once told him that NOAA is unnecessary because one can find the same data in a more user-friendly application on Weather.com.  What the commenter didn’t realize is that NOAA data form the backbone of Weather.com. The federal government provides the data gathering, normalizing, and updating functions and then makes the data available to others who can overlay, combine, segment, analyze, integrate and distribute the data in any variety of mashed-up and improved formats.

The tradition of building data businesses on the foundation of federal, state, and local government data is strong. Savvy data publishing entrepreneurs have been digging deeply into government sources of data and providing new applications based on the data for centuries and new data products and services continue to emerge. The opportunities for leveraging data aren’t restricted to using government data by any means. Just look at companies like IMS Health that compiles data on prescribing behavior from pharmacies.

Some healthcare IT companies understand the power of leveraging data. In fact, athenahealth, Todd Parks’ former company, is one of them. Thomson Reuters Healthcare (now Truven Healthcare Analytics) is another company that has built a big part of its portfolio around leveraging CMS data.

Bob Kocher, a partner at Venrock, also spoke at the Rock Health event. He stated that healthcare is the only industry where investments in IT haven’t led to labor-saving productivity improvements. I’m not surprised by this fact. We’ve had lots of new technologies in healthcare that help us do things we weren’t able to do before.  However, we haven’t been very good at building on our innovations to create a better healthcare system.  In today’s world, combining data with software to build tools that improve efficiency and productivity leads to much richer sets of products and services. Readers of this blog have heard this sentiment from me before and I’m known for defining “meaningful use” as the intelligent combination of IT and content.  It’s a theme worth repeating and I was pleased to hear it articulated so well by Todd Park, Bob Kocher and others yesterday.