2017 Healthcare IT Predictions – Part 2

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The excitement continues to build around HIMSS17 as many in the industry are eager to share their ideas for innovations to advance healthcare – both outcomes for patients and workflow for providers.

In part two of our 2017 Health IT Predictions, our Chief Healthcare and Innovation Officer, Dr. Geeta Nayyar, asked her fellow 2016 and 2017 HIMSS social media ambassadors to share their insights and predictions for what will move the industry forward this year. Interoperability for value-based care is the key winner with this group – whether it’s interoperability between data, devices or stakeholders (payers/providers), unifying goals and communication will surely benefit all.

Here are some key insights on the future of Health IT:

Blockchain, which allows digital information to be distributed but not copied, will start to fuel new healthcare opportunities. In 2017, healthcare will find immediate use cases supported by the ONC (Office of the National Coordinator for Health Information Technology), larger payers and savvy health systems. Early proof cases around interoperability, auto-adjudication and a patient’s timely access to their health records, will assist the technology in gaining momentum.

The model for clinical trials will be turned on its head. Low patient recruitment and retention in clinical trials is one of the biggest challenges facing pharma and contract research organizations. Models of decentralized or virtual clinical trials can help address many of these obstacles – moving clinical research away from the study site through the use of technology (telemedicine and wearables.) This new approach can help give access to participants that would otherwise be overlooked in the current PI-centric model. Use of telemedicine tools now make this model possible and can contribute to better adherence and retention.

Value-based care moves to pharma. Before 2017, we saw a few noteworthy cases where pharma was asked by payers to demonstrate the value of their therapies. In 2017, we’ll see this expand with more value-based contracts between pharmaceutical manufacturers and payers. As this pressure is heightened, we will see companies that can successfully assist providers in orchestrating better care and by illustrating to payers the cost-effective nature of this care, it will gain an edge as this new business model gains traction.

Tamara StClaire, PhD, MBA
Chief Innovation Officer, Xerox Commercial Healthcare
@drstclaire

 

Payer/provider collaboration will get increased attention because it will become abundantly clear that the idea of single-institution value-based care models is silly and we need deeper integration and collaboration between multiple stakeholders across multiple institutions to get the outcomes we need. There will still be many organizations fighting to stay independent, but unless insurers begin to roll back from their commitments to increasing pay for performance contracts, there’s no hope of long term survival for organizations that aren’t collaborative by design and culture.

Since 2017 will be a “reset” year driven by a single party in charge of both the executive and legislative branches of government, many organizations will take more risks as they attempt to figure out which way the government mandates will take over the next few years. Those betting on innovation, increased autonomy for states (as opposed to centralized federal control), more open architectures, and leaning more on consumer choice due to higher consumer financial burdens, will come out winners.

Medical data is already ubiquitous and will continuously be growing – instead of getting less “noisy” data and more actionable information, EHRs and other digital health tools will continue to inundate providers in 2017 but a small set of machine learning (ML) initiatives and intelligence augmentation (IA) tools will begin to take hold. Not as sexy as its artificial intelligence (AI) counterpart, IA and ML will start to uncover small nuggets of implicit data from larger and larger datasets. Instead of relying on human defined reports and explicitly defined rulesets, ML and IA will use patterns in the data itself to help find useful information that humans would typically miss.

 Shahid Shah
Digital Health Entrepreneur
@ShahidNShah

 

The theme of information discovery and decision support will dominate 2017. Here are three trends I predict will gain momentum in 2017:

Growth in usage of digital devices and sensors will be a catalyst for progress in interoperability. This is a safe prediction, but I include it because it sets the stage for my other predictions. With the proliferation of devices and sensors, it won’t be sufficient to suggest that IT systems manage each device and its data separately. The devices will have to interact with other devices and with EHR & other systems. To make sense of the all of the data being produced by sensors and other devices, information systems that can interpret the data in context (i.e., AI systems that incorporate machine learning techniques) will be needed.

Healthbots increasingly become the new interface to health information and health data for patient information. Chatbots have evolved from simple voice recognition technologies to cognitive computing interfaces that can execute complex commands and improve their utility over time with machine learning technologies. I expect success in the consumer space via Apple’s Siri, Google Assistant, Amazon’s Echo and other examples to carry over to the patient engagement and patient education space quite rapidly, although a secure channel will be required for healthbots, whether the bot uses a voice, haptic, or typing interface. Applications in clinical decision support for professionals will emerge in some specialized areas where the knowledgebase is less complex and hands-free use is important, but adoption within clinical enterprises will likely take longer to reach wide acceptance. Information discovery will no longer require an active search. Search will still exist, but it will exist primarily in the background. This shift from blank search boxes and look-up tables toward surfacing relevant information based on context, prior behavior, collaborative filtering algorithms and other patterns has been occurring for some time.

One example, that bridges the search and discovery paradigm, is Google’s inclusion of knowledge graph items that are displayed at the top of search results when one enters a disease such as ‘diabetes’ in the search box. Another example, is the TrendMD model , which appends personalized contextual links to the article someone is reading. The links can be sourced from any of the 3,000+ sources within the TrendMD network, which provides the chance to include related information from other fields or specialties, but offers the assurance that links won’t be sourced from unwanted advertising sites. Like machine learning-enhanced healthbots, the quality of the links improve over time with increased usage as the algorithms learn about an individual’s preferences and gain knowledge from the broader community of users.

Closer to home for the HIMSS audience, CDS Hooks, an open standard within the SMART on FHIR framework, will advance the clinical decision support goal of delivering the right information, to the right person, at the right time, in the right format within the right channel. However, as described above, cognitive computing and machine learning technologies can take this type of information alert to the next level and act on the data. It will take time for executable CDS to become widespread; mistakes are too costly and clear rules for executing orders aren’t yet available in most areas.”

Janice McCallum
Managing Director, Health Content Advisors
@janicemccallum

 

I see three emerging trends that will have a significant impact on healthcare in 2017 and beyond:

The first is machine learning and associated artificial intelligence technologies such as automation and predictive analytics. With the massive amount of data that is now flooding in for both research and clinical care, including both genomic and proteomic, managing these data and gaining useful insights is beyond the capability of our traditional digital health systems. Machine learning tools, both supervised and unsupervised, are well positioned to solve some of these issues. AI is coming quickly into healthcare and 2017 is the beginning of a substantial change in the usual data management and analytics framework.

The second area I see sort of sneaking up on us is blockchain technology. With the work of groups like the Hyperledger Project, and a host of vendors jumping into the scene, I expect some of the excellent ideas put forth last year at the ONC Blockchain for Healthcare Challenge to begin to be deployed. During HIMSS I’m excited to see the IEEE has a collocated event the “Rockstars of Blockchain” where we will hear from experts in the field some of the revolutionary concepts that could materialize into production over the next few years.

Finally, digital transformation. There will be a profound and accelerating transformation of healthcare business activities, processes, new competencies alongside the new care and payment models rapidly advancing in the system. This will fully leverage the innovations and opportunities of digital health technologies. Digital transformation is going to have a massive effect on every industry, and healthcare is certainly ripe for transformation, more than any other vertical. So hang on because it is going to be a wild ride.

Brian Ahier
Digital Health Evangelist
@ahier

 

Remember to check in with TopLine MD throughout the year to find out how these predictions – many of which may become reality – will affect patient and provider choices going forward.

Check out Part 1 of our coverage on Health IT predictions for 2017.