Against my better judgment, I volunteered to author this blog. It’s that time of year when many predictions are published. Both professionals and philosophers alike are aware of the challenges of looking into the future. First the image below speaks volumes to the difficulty of doing so for those professionals who are skilled at prognostication:

Even philosophers such as the Yankee’s famous catcher and oft quoted Yogi Berra have opined eloquently on the matter: “The future ain‘t what it used to be.“ and “Predictions are dangerous, especially when you are talking about the future.“

With those cautions, I‘d like to start with the obvious.

• Cybersecurity will continue to get increasing focus and investment, by necessity. Cybercriminals will continue to attack the value-rich data in healthcare.  The proliferation of cellphones, wearables, Internet of medical Things, and BYOD.  As the number of nodes are added to any network, the vulnerabilities increase.  I expect significant growth in successful breaches in spite of the investments.  Prepare your staff, executive team, and Board for the new reality.
• The shift to value-based payments, mergers and acquisitions, and the volume of data being collected will fuel activities which fall into the general category of analytics. Board members at providers organizations will start asking about MDM (Master Data Management) while data architects and their stakeholders struggle to organize data whether it’s being characterized as Big, Smart, Small, and/or Long. My favorite term and hopefully, more descriptive term, is “Eclectic Data” which means that it can be derived from a broad, diverse, and, hopefully, comprehensive set of sources.  Develop a clear, concise approach for MDM and communicate it broadly.
• Organized data will fuel the analytics engines which will be used to help manage population health. To borrow from Ginni Rometty, CEO of IBM, who has been speaking about data as “the new natural resource” since 2013, “…it is of little value unless it can be refined.” To continue that metaphor, data must be refined into countless affordable, consumable, and meaningful products.  Those transformed products (data in raw form, information, and knowledge) will help people who consume them achieve the goals of the Triple Aim.  Get involved in transformation and consumption.

We need practical and pragmatic applications of analytics in its many forms to help individuals and the communities in which they live formulate the right questions — data discourse leading to discovery and meaningful deployment.

And now I’d like to briefly address just a few emerging technologies which hold great promise.  All have significant data implications and will require meaningful conversations.  For each topic, I’m suggesting a couple of questions to get those conversations started.

• Blockchain technology which began in 2009 has been promoted for healthcare with great fanfare. In August of 2016, the Office of the National Coordinator announced 15 winners who submitted whitepapers in response to an ideation challenge.  Though touted to solve interoperability, provide a means for patients to become custodians of their own data, and facilitate research, I remain skeptical of uses in the clinical realm, for now. I do, however, see some operational and payer related benefits based on similar successes in other industries.  There will be many more planning and proof-of-concept activities for 2018, but I don’t think we’ll see lot of production at scale.  What does a distributed ledger do to the concept of the legal medical record?  Who is the custodian of that record?
• AI (artificial intelligence) will disrupt many roles more quickly than might have otherwise been imagined, disruption which should be viewed positively. Though perceived as a challenge by some critics, AI when used properly deployed augments the existing skillsets of very capable professionals, helps evolve many roles, and provides insights that would not otherwise be possible by human endeavor.

The use of AI can be enhanced by machine learning (ML).  Machine learning is critical because the volume of information produced by humans is too vast for us human to learn.  ML makes the voluminous information and knowledge accessible and available via our common technologies.

AI has already proven invaluable reading both traditional and particularly 3D images.  Interpretation of mammograms and abdominal CTs by AI has already been shown to be as effective, sometimes more effective, than radiologists and others who read images without the assistance of such “knowledgeable” technologies.  With the ability to review the combination of images along with both discrete and unstructured data from a wide range of sources more quickly and consistently than its human counterparts, AI can risk stratify patients based on patterns that might otherwise be missed in the ocean of data that describes a patient’s condition.

And what about natural language processing (NLP)?  There will be a huge impact on HIM coders who are so important in the HIM areas and RCM (revenue cycle management) processes.  Already CAC (computer assisted coding) is helping coders.  The combination of AI, NLP, ML, and CAC will reduce costs and errors and improve the coding process.  Perhaps the biggest benefit is to researchers who are trying extract meaning from blogs of text data.

In each of these situations, those who embrace the technologies will advance; the others will lose the edge and be unable to meet their communities’ standards of care.

Who’s going to keep track of the algorithms that were used to help with the diagnostics and coding?  Is that relevant to and should it be a part of the legal medical record?

These are simply the most obvious topics getting a lot of press.  There are many other emerging technologies that will change the way that care is delivered, that already have expanded the care continuum beyond the traditional venues.  Some of the exciting developments include but are not limited to:

• 3D printing
• Augmented and virtual reality
• Autonomous vehicles – driving and parking
• Chatbots
• Data discovery
• Gaming and gamification
• MBAN (medical body area networks)
• Nanotechnology
• Robotics
• Simulation
• Smart data fabric
• Technology gardens for children and adults
• Telehealth (and it’s extraordinary diversity)

One prediction I can make with certainty is that I will address some of my favorites from above in future blogs.  The other is that our futures are brighter than ever before.  At StarBridge Advisors, we are happy to use our experienced Advisors to help you plan, innovate, and then navigate these wonderfully tumultuous transformative times.