Health inSite: Small worlds need a u-turn


Where we’re going with electronics…

As we look at the new innovations coming out of the Consumer Electronics Show in Las Vegas, NV this year (#CES2014 on twitter and elsewhere!), I find myself questioning what we are trying to accomplish in HealthIT trends when it comes to interacting with our technology.  Largely, this was prompted by a recent article about the formation of the recent Google-led Open Auto Alliance, a collaborative effort between nVidia, Google, Hyundai, Honda, GM, and Audi.

Humorously enough, or not, the article referenced above for the OAA includes a banner for Door-to-Door Organics (a local-to-online-to-local organic produce company from which I get a weekly delivery for my lunch each day) as the sponsored banner advertisement.


As a Google fan-boy of sorts (I’ve been a Gmail user since 2004, helped start a company in 2005 based on Google search technology, and chose Android for my first [and every subsequent] smart phone), I have long-lauded the value of Android integration into the many facets of life.  I rely on this technology in many ways day-in and day-out to help curate my experience in the real world.  But, I have to express some dismay at Google’s focus on making cars that are our “ultimate mobile device” as it goes against another important soapbox of mine – the future of our online lives actually exists in augmenting our offline ones.  See here: Google Glass, augmented reality, and even Google Goggles which just earlier this year helped me identify additional information for my mom about an artist while I was in a museum!

IBM makes a startling announcement!

Early in December, IBM released its predictions for the next 5 years of which, one in particular, was jarring and interesting to consider – that mobile will enable the local buyer.  While it may seem to be bold to suggest that companies like Zappos and Amazon are going to lose the buyer war, I think one would be foolish to overlook the trend toward buying local in many ways.  I don’t think IBM is trying to suggest that buying local is going to be true for bulk/large purchasing – as it’s going to remain significantly cheaper to buy many electronics direct from the warehouse for many people who don’t need the concierge services of an electronics store – but smaller, everyday purchases will remain daily purchases.  For that reason, I would concur that mobile has the potential to enable local purchasing in a way we’ve never experienced before.

Enter Small Worlds (both literal and figurative)

If my study in Network Science and Sociology has taught me nothing, it’s that when looking at trends in social design, if one ignores the general trend of the people that have to operate in a system, for glitziness, that company is losing out on finding the niche in which it is most likely to thrive.  Minidisc lost out to MP3s, Blue Ray is just now catching on against DVDs and that was a multi-billion dollar campaign, and more importantly, there is a certain level of social responsibility that should come with technological advancement.  For that reason, I ask a simple question: when we know that we are facing so many epidemics within the health of our populace, why would we focus on generating tech for the machine that most clearly has helped to create our epidemic.  Let me explain…

The greatest impact that we could have on the overall health and wellbeing (the optimally-performing self) is by leveraging the interpersonal experience that we have with one another.  There is no greater way, in my view, to leverage the way that we as humans interact with one another.  Network Science explains how we are the product of the many interactions that we have day-in and day-out.  This creates the product known as ‘small worlds’ in which we exist.  If you were to look at a graphing of your daily social interactions, you would find that there are a number of individuals (nodes) that have a significant impact on your daily life.  Some of those people (hubs) have a greater impact on other clusters of people, but ultimately, our interactions occur with about 150 people regularly.  Despite that, our impact on the health and wellbeing of a great number of other people through our interactions allow the network to act in a much more comprehensive way.  In effect, it’s not just our actions, but the actions of those we interact with, that ultimately impact the way in which the network perceives and reacts to those behaviors.

It’s time to focus on our communities, not ourselves

In many ways, the single most destructive piece of technology that we’ve created is the automobile. As of 2011, the average US citizen travels over 36 miles each day in their vehicle – which no doubt is why Google is looking at trying to maximize their time with their users in this environment.  However, from a society-design perspective, this also encourages obesity and ultimately social-distancing.  It enables us to live in communities that are often disparate and incompatible with building social structure.  While it has become a mainstay in our social psyche, it has also enabled a technology-subordinate population.  Our cars have created the perfect self-encapsulated environment that removes us from the need to interact positively with one another in meaningful ways.  My city of Denver, CO receives a walk score of 56; check yours here!

If only Google was spending more time on creating the “public square” instead of the “private car” of the future, we might see some pretty significant impact on mobile health technology and more importantly, helping to engineer with the social network in mind to create a healthier population.

Know of something Google or anyone else is doing toward this goal?  Other insights you want to share?  Tweet at me or comment below!

To our health,

Ryan Lucas
Manager, Engagement & Development
Follow me on twitter: @dz45tr


Health inSite: Decision Support, Games, and making people healthier


I’m a bit of a trivia nerd. In fact, I play trivia with a group of friends every week. We do alright, and obviously there are good weeks (I mean, we keep going back) and then there are bad weeks. I play team captain for our group. The responsibilities of team captain are to record our progress (each question gets a wager based on our confidence in our answer) and recording the success or failure of each question in a running total, and helping to marshal the resources of the team (points, knowledge bases of the players, ranking answer likelihood, etc.). The final trivia question of the night is a challenge. Each team is given the question that requires four answers in rank order, usually. When turning in one’s response, a point value between 1 and 15 is assigned to the answer. If any part of the question is wrong, the wager is subtracted from the team’s total score. If the answer is 100% correct, the team gains the wagered points. So it’s no surprise that I would be really intrigued by Watson, a supercomputer that was able to best two of Jeopardy’s greatest champions in a tournament back in 2011. Research into Watson is really interesting.


Watson was trained to respond only when a certain threshold had been met in the likelihood that Watson was correct in its assumptions. This confidence was determined based on cross-referencing the available answers and identifying the highest scored answer based on a number of algorithms. While Watson is not right 100% of the time, its significant domination of the final score ($77,147 vs. 2nd place’s $24,000) is no small feat for a computer responding to natural language, searching natural language information, and culling a response to an “open-domain” question.

Game State Evaluation

Part of the programming behind Watson required not just an understanding of the likelihood that Watson was right, but also what the potential for gain or loss in relation to the other players might be. Because Jeopardy includes wagering for daily doubles and Final Jeopardy, Watson had to strategically wager in relation to the likelihood not only that it was right, but also what it would mean if the other players were right or wrong. This is well-illustrated by the final wager that Watson placed in response to the final jeopardy question, which was $17,973. This is a statistically-determined wager based on total game state evaluation at the time of this final question using the above variables.


While there is plenty of room for argument as to whether or not Watson is thinking, there is absolutely no question as to whether Watson is logical. As I’ve mentioned before in a couple of articles related to the work of Daniel Kahneman, (if you haven’t, make sure to check out Thinking Fast and Slow) human rationality is very rarely very rational. This is due to a number of intervening variables that interrupt our ability to make rational decisions all of the time. These “biases” can be intentionally or unintentionally applied during the decision-making process. While Watson has a number of heuristics, no-doubt, built into its logical processing, it is probably not as likely to respond to cognitive biases such as anchoring, duration neglect, and certainly curse-of-knowledge as seen in its commanding performance in the Jeopardy games.

Decision Support Systems

Watson is now being used in a number of healthcare applications assisting in the support of clinicians as diagnostic support. Watson is not making decisions, but it is able to cull the plethora of information available in the medical field to provide confidence-rated responses to data that is provided regarding a patient. This marks a big step for the advancement of Health IT as we can standardize clinical response to symptoms, and stabilize health information as it is consolidated into big data stores. And because Watson is able to learn as it answers and receives feedback as to success and failure based on those responses, Watson can only get better at diagnostic prediction and likelihood of treatment success or adherence based on the results of those treatments.

What does this mean for making people healthier?

There are some obvious benefits to these advances in Health IT, but one of the things that may not be fully clear yet is the application of Watson to understanding more about human behavior. While Watson can absolutely tell a clinician the likelihood of a set of symptoms’ association with a given disease, I’ll bet Watson can’t tell you how the patients’ family impacts their overall wellbeing through behavior reinforcement. If Watson knew who the patients’ workout buddy was, Watson might be able to help identify with a high confidence whether that workout buddy was a statistically-sound partner in the overall health management of the patient. Further, Watson would be able to weigh in on the evaluation of treatment adherence based on real-time data pouring into the health record for the given individual.  This is the game state evaluation of the health of the individual in a real and meaningful way.  With this, a total and complete understanding of the long-term treatment of chronic conditions (and even more important to the salutogenic framework that I’ve discussed previously in this blog series, total health production) through the understanding of actual human behavior devoid of the clinical separation from reality is the “social human” version of epigenetics that will become more useful in the coming years.  This is where the data comes to life.

To our health,
Ryan Lucas
Supervisor, Marketing
To stay ahead on topics related to this, follow me on Twitter @dz45tr