In Part 1 of this post I presented the argument that there is currently no true social layer in health. I think that a true health network layer (the Health Graph) will evolve that organizes users based on their health profile from multiple data points and will allow groupings based on a high degree of similarity between users (and thus relevance).
Personal health data exists today in many digital silos, and increasingly, APIs are being opened to allow app developers to provide services to users based on their personal health data. Microsoft Healthvault and Google Health were designed as platforms from the outset, and recently, EMR vendors (such as PracticeFusion) have announced intentions to open APIs allowing development of personalized applications for physicians and patients.
While current applications built on these platforms have some degree of personalization based on an individual’s data, they are still mostly one-to-one connections between one app and one individual. With the addition of the Health Graph, ‘social health applications’ will become possible across these platforms, greatly increasing value to users.
I know the common Health Graph API and Login are a long-shot, but it is the way things should be, so I hope it will happen someday.
Applications of the Health Graph
I think the most immediate application of the Health Graph is the ability to target content or questions from one user to another based on a higher degree of similarity and relevance than is possible in today’s one-dimensional “health networks”. However, I think the real value of this Health Social Layer will come from the aggregation and sharing of personal health data and health content among personalized peer groups that are based on an individual’s health profile. The Health Graph will allow applications to automatically segment users into meaningful peer groups, based on what the app is trying to accomplish. Below are a few thoughts on features and applications that could be built on the Health Graph:
Rather than posting a question to the appropriate forum or site, users will have health questions targeted to users with very similar health histories and characteristics increasing the relevance of the response. A response will carry more weight to the user if they know what they have in common with the respondent. For instance, a response about a potential therapy for depression is valuable. But a response about this therapy from another user who has the same symptoms, history of depression, age, gender and sequence of therapies is much more relevant. An analogy from standard Q&A sites is the value of Quora or Aardvark versus Yahoo! Answers. I am an expert about my own body and health history, so if we share a very similar health history, my ‘expertise’ is relevant to you, and vice versa.
Surveys and Statistics
In addition to the type of Q&A described above, users will be able to ask simple quantitative questions that can be targeted at the appropriate audience. Again using a depression example, if a user wants other users to rate the effectiveness of a given treatment, it is useful to have a result from a broad audience, but it is much more relevant if the answers are filtered to only users highly similar to the initial user. Using this approach of targeted quant questions, an app could generate highly relevant data and statistics of how a given user responds relative to their peer group, and relative to the population as a whole.
I remember reading a series of Q&A posts about a certain drug from a popular health Q&A site. User #1 was asking if this drug was appropriate for them, and if others had experience with the drug. He listed a pretty detailed medical history in his initial post. After numerous posts on whether the drug worked or did not work for respondents, but without context, another answer jumped out. This respondent went on to describe a very similar medical history to User #1 (which was quite complex), and then gave a very detailed description of their trial with the drug (which was positive). The relevance and weight of that post was very impressive. The Health Graph will enable this level of relevance for all questions and recommendations.
It seems to me that it is a lot more fun to compete with others when I know a bit about them. I think FitBit has done a nice job on this front by allowing users to see group data on activity level, but allowing filtering by important characteristics: age, BMI, etc. This peer group is relevant to me, but comparing my activity level to an 18 year old marathon runner probably is not. True peer groups open many opportunities to add game mechanics into health apps.
Social Tracking – Longitudinal Data Collection by Peer Group
I think one of the most powerful outgrowths of the Health Graph and ability to define highly relevant peer groupings will be the ability to track specific pieces of data over time. I have written previously about the rapid rise in health trackers facilitated by smart phones. While most of these trackers are for the individual, the power of social tracking far surpasses individual tracking. With a well defined and relevant peer group, I think the value of tracked data will grow exponentially. In addition to seeing how you are trending relative to your peer group, the Graph makes it possible to compare two groups who are essentially equivalent, but differ on one key characteristic: drug A vs. drug B, migraine with aura vs. without aura, frequent exercise vs. infrequent, etc. With enough data on each user, over time these could become well matched and well controlled studies.
Please let me know of your thoughts on these concepts, or other potential uses for the Health Graph …