We’re Just Getting Started with an Internet of Healthy Things (Part 3 of 3)

Posted on November 27, 2015 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.

The previous sections of this article described the state of health care today and some of the impressive advances described in Joseph Kvedar’s new book, The Internet of Healthy Things. Now we’ll look at the possibilities for advancing further, and what stands in the way.

Futures postponed

Later in the book, Kvedar explores the promise of analytics. On a small scale, analytics can tie the results of traditional clinical research to recommendations for individuals. For instance, if the A1C hemoglobin of a person with diabetes hits a certain level that clinical research has established as dangerous, she can be notified. We also know what heart rates are best for exercising and other useful statistics. Walgreens and CVS also use data at this level to market their products to consumers who sign up for their fitness tracking programs.

At the next level, analytics can signal out high-risk patients and predict hospital readmissions. It seems to me that a doctor can tell by the end of a patient’s first visit whether he’s high-risk, but in a large institution (most of which nowadays are getting larger), following all the high-risk patients calls for technical support.

An explosion of analytics applications predicting hospital readmissions has occurred ever since the Centers for Medicare and Medicaid Services started penalizing hospitals for them. This is a crude example of fee-for-value at work; what CMS is trying to do with ACOs and more recent initiatives is to extend it further, with the aggressive goal of making 90% Medicare payments fee-for-value by 2018. But the field is lagging, as we’ve seen with the defections from the ACO program. In the afterward to his book, Kvedar admits that the fee-for-value model is not clearly on a path to success.

In regard to analytics, a third level of sophistication still eludes most organizations. This level enhances or complements clinical research by following large cohorts of patients and finding insights that the clinical research did not find. PatientsLikeMe has made a business out of this, using data submitted voluntarily by its members to drive numerous useful studies. But PatientsLikeMe (and a couple others, such as Kaiser and Johns Hopkins) are exceptions that prove the rule: few institutions can use analytics at this level. Kvedar admits that even Partners is not making use of analytics.

Did I mention that it’s hard to find people who can even do the analytics? According to a McKinsey report, “The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.” Yes, your organization will be competing to hire these hard-to-get staff.

If the analytics aren’t yet in place to mine patient data for useful information, the value of sharing data is greatly diminished. We will ask patients to share very sensitive information–the topic of the last chapter in The Internet of Healthy Things–without having much to show for it. Luckily, clinical trials may still produce enough information for clinicians to know how to handle most conditions.

A final note: funding for health care change is extremely high-stakes. We see this already in the pharmaceutical industry, which Kvedar portrays as caught in the scissors of declining incomes and steeper hills to climb when finding new drugs. Organizations developing comprehensive solutions–not just simple apps–for behavior change and running the trials that will win FDA approval have just as many funding barriers, and require many more years to show a return on investment than most VCs want to countenance.

In this regard, it’s interesting to see in The Internet of Healthy Things what kinds of innovations are being funded. Diabetes treatments such as WellDoc get funded partly because some VCs personally suffer from diabetes. Other conditions, such as AIDS, find sufferers disproportionately among those who are less wealthy. Who will find the personal motivation to drop twenty million dollars on a cure?

I’ve been deliberately skeptical–more than I usually am–because The Internet of Healthy Thingsis so far-reaching and aims so high. On the whole, the book is a remarkable account of a remarkable career. It’s also carefully written (with a couple sophisticated co-authors) to highlight the important points and group insights in ways that make them easy to remember. I highly recommend it.