Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 1 of 3)

Attendees at each Connected Health Conference know by now the architecture of health reform promoted there. The term “connected health” has been associated with a sophisticated amalgam of detailed wellness plans, modern sensors, continuous data collection in the field, patient control over data, frequent alerts and reminders, and analytics to create a learning health care system. The mix remains the same each year, so I go each time to seek out progress toward the collective goal. This year, I’ve been researching what’s happening in these areas:

  • Validation through clinical trials
  • Advanced interfaces to make user interaction easier
  • Improved data sharing (interoperability)
  • Blockchains

Panel at Connected Health Conference

Panel at Connected Health Conference

There were a few other trends of interest, which I’ll mention briefly here. Virtual reality (VR) and augmented reality (AR) turned up at some exhibitor booths and were the topic of a panel. Some of these technologies run on generic digital devices–such as the obsession-inducing Pokémon GO game–while others require special goggles such as the Oculus Rift (the first VR technology to show a promise for widespread adoption, and now acquired by Facebook) or Microsoft’s HoloLens. VR shuts out the user’s surroundings and presents her with a 360-degree fantasy world, whereas AR imposes information or images on the surroundings. Both VR and AR are useful for teaching, such as showing an organ in 3D organ in front of a medical student on a HoloLens, and rotating it or splitting it apart to show details.

I haven’t yet mentioned the popular buzzword “telehealth,” because it’s subsumed under the larger goal of connected health. I do use the term “artificial intelligence,” certainly a phrase that has gotten thrown around too much, and whose meaning is subject of much dissension. Everybody wants to claim the use of artificial intelligence, just as a few years ago everybody talked about “the cloud.” At the conference, a panel of three experts took up the topic and gave three different definitions of the term. Rather than try to identify the exact algorithms used by each product in this article and parse out whether they constitute “real” artificial intelligence, I go ahead and use the term as my interviewees use it.

Exhibition hall at Connected Health Conference

Exhibition hall at Connected Health Conference

Let’s look now at my main research topics.

Validation through clinical trials
Health apps and consumer devices can be marketed like vitamin pills, on vague impressions that they’re virtuous and that doing something is better than doing nothing. But if you want to hook into the movement for wellness–connected health–you need to prove your value to the whole ecosystem of clinicians and caretakers. The consumer market just doesn’t work for serious health care solutions. Expecting an individual to pay for a service or product would limit you to those who can afford it out-of-pocket, and who are concerned enough about wellness to drag out their wallets.

So a successful business model involves broaching the gates of Mordor and persuading insurers or clinicians to recommend your solution. And these institutions won’t budge until you have trials or studies showing that you actually make a difference–and that you won’t hurt anybody.

A few savvy app and device developers build in such studies early in their existence. For instance, last year I covered a typical connected health solution called Twine Health, detailing their successful diabetes and hypertension trials. Twine Health combines the key elements that one finds all over the Connected Health Conference: a care plan, patient tracking, data analysis, and regular check-ins. Their business model is to work with employer-owned health plans, and to expand to clinicians as they gradually migrate to fee-for-value reimbursement.

I sense that awareness is growing among app and device developers that the way to open doors in health care is to test their solutions rigorously and objectively. But I haven’t found many who do so yet.

In the next segment of this article continues my exploration of the key themes I identified at the start of this article.

About the author

Andy Oram

Andy Oram

Andy Oram writes and edits documents about many aspects of computing, ranging in size from blog postings to full-length books. Topics cover a wide range of computer technologies: data science and machine learning, programming languages, Web performance, Internet of Things, databases, free and open source software, and more. My editorial output at O'Reilly Media included the first books ever published commercially in the United States on Linux, the 2001 title Peer-to-Peer (frequently cited in connection with those technologies), and the 2007 title Beautiful Code. He is a regular correspondent on health IT and health policy for He also contributes to other publications about policy issues related to the Internet and about 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.