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IBM Watson Partners With FDA On Blockchain-Driven Health Sharing

Posted on January 16, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

IBM Watson Health has partnered with the FDA in an effort to create scalable exchange of health data using blockchain technology. The two will research the exchange of owner-mediated data from a variety of clinical data sources, including EMRs, clinical trial data and genomic health data. The researchers will also incorporate data from mobiles, wearables and the Internet of Things.

The initial project planned for IBM Watson and the FDA will focus on oncology-related data. This makes sense, given that cancer treatment involves complex communication between multispecialty care teams, transitions between treatment phases, and potentially, the need to access research and genomic data for personalized drug therapy. In other words, managing the communication of oncology data is a task fit for Watson’s big brain, which can read 200 million pages of text in 3 seconds.

Under the partnership, IBM and the FDA plan to explore how the blockchain framework can benefit public health by supporting information exchange use cases across varied data types, including both clinical trials and real-world data. They also plan to look at new ways to leverage the massive volumes of diverse data generated by biomedical and healthcare organizations. IBM and the FDA have signed a two-year agreement, but they expect to share initial findings this year.

The partnership comes as IBM works to expand its commercial blockchain efforts, including initiatives not only in healthcare, but also in financial services, supply chains, IoT, risk management and digital rights management. Big Blue argues that blockchain networks will spur “dramatic change” for all of these industries, but clearly has a special interest in healthcare.  According to IBM, Watson Health’s technology can access the 80% of unstructured health data invisible to most systems, which is clearly a revolution in the making if the tech giant can follow through on its potential.

According to Scott Lundstrom, group vice president and general manager of IDC Government and Health Insights, blockchain may solve some of the healthcare industry’s biggest data management challenges, including a distributed, immutable patient record which can be secured and shared, s. In fact, this idea – building a distributed, blockchain-based EMR — seems to be gaining traction among most health IT thinkers.

As readers may know, I’m neither an engineer nor a software developer, so I’m not qualified to judge how mature blockchain technologies are today, but I have to say I’m a bit concerned about the rush to adopt it nonetheless.  Even companies with a lot at stake  — like this one, which sells a cloud platform backed by blockchain tech — suggest that the race to adopt it may be a bit premature.

I’ve been watching tech fashions come and go for 25 years, and they follow a predictable pattern. Or rather, they usually follow two paths. Go down one, and the players who are hot for a technology put so much time and money into it that they force-bake it into success. (Think, for example, the ERP revolution.) Go down the other road, however, and the new technology crumbles in a haze of bad results and lost investments. Let’s hope we go down the former, for everyone’s sake.

Newly Released Open Source Libraries for Health Analytics from Health Catalyst

Posted on December 19, 2016 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

I celebrate and try to report on each addition to the pool of open source resources for health care. Some, of course, are more significant than others, and I suspect the new healthcare.ai libraries released by the Health Catalyst organization will prove to be one of the significant offerings. One can do a search for health care software on sites such as GitHub and turn up thousands of hits (of which many are probably under open and free licenses), but for a company with the reputation and accomplishments of Health Catalyst to open up the tools it has been using internally gives healthcare.ai great legitimacy from the start.

According to Health Catalyst’s Director of Data Science Levi Thatcher, the main author of the project, these tools are tried and tested. Many of them are based on popular free software libraries in the general machine learning space: he mentions in particular the Python Scikit-learn library and the R language’s caret and and data.table libraries. The contribution of Health Catalyst is to build on these general tools to produce libraries tailored for the needs of health care facilities, with their unique populations, workflows, and billing needs. The company has used the libraries to deploy models related to operational, financial, and clinical questions. Eventually, Thatcher says, most of Health Catalyst’s applications will use predictive analytics based on healthcare.ai, and now other programmers can too.

Currently, Health Catalyst is providing libraries for R and Python. Moving them from internal projects to open source was not particularly difficult, according to Thatcher: the team mainly had to improve the documentation and broaden the range of usable data connections (ODBC and more). The packages can be installed in the manner common to free software projects in these language. The documentation includes guidelines for submitting changes, so that an ecosystem of developers can build up around the software. When I asked about RESTful APIs, Thatcher answered, “We do plan on using RESTful APIs in our work—mainly as a way of integrating these tools with ETL processes.”

I asked Thatcher one more general question: why did Health Catalyst open the tools? What benefit do they derive as a company by giving away their creative work? Thatcher answers, “We want to elevate the industry and educate it about what’s possible, because a rising tide will lift all boats. With more data publicly available each year, I’m excited to see what new and open clinical or socio-economic datasets are used to optimize decisions related to health.”