The Tip of the Iceberg

Apr 27, 2007, 12:14 PM, Posted by Steve Downs

One of the aims of Project HealthDesign was to turn the concept of personal health records (PHRs) on its head.  The idea was to start with end user applications (i.e. the tools people can use to manage their health) and see how they could build off access to one’s medical record rather than start with the medical record itself and see how it could be enhanced.  We anticipated that many of the applications our grantees would design would involve capturing data from end users in the flow of their lives, outside of their contact with the medical care system. 

In a previous post, I raised the question of what constitutes one’s PHR – what’s the relationship between the data in one’s “official” medical record and the data one generates outside of the care system.  The most recent design workshop, which brought our grantee design teams together, gave us a glimpse of the different nature of these two types of data. 

While most PHR offerings today provide an option for some “patient-sourced” data as a complement to the care-generated data that dominates the PHR, our experience at the workshop suggested that the “official” data is merely the tip of the iceberg.

The Project HealthDesign grantees are all contemplating recording personal observations.  These include information on diet (e.g. diaries or photos of meals), activity (e.g. diaries, pedometer readings), weight, blood pressure, glucose levels, pain levels and locations, medications taken, general symptoms, and sleep. The frequency of these observations is not limited to encounters such as physician office visits and clinical lab tests, but - depending on their intrusiveness and the intensity of a person’s self-management activities - can occur multiple times daily and even around the clock.  Add them all up, and they quickly dwarf any information contained in a traditional medical record.

Of course, the idea of simply pouring these data into one’s medical record and expecting a clinician to sort through them is absurd, so the ability to develop and apply sophisticated analytical tools becomes paramount.  Those tools will be even more powerful if they can analyze patterns across the different types of data (e.g., correlating sleeplessness and physical activity, blood pressure changes with medications).  That need in turn suggests a need for a common, extensible structure for storing and organizing data from personal observations, open interfaces and standard approaches for developers to query the data.

But these are just technical considerations.  The real excitement comes from imagining what can be learned from research that mines this richer, deeper picture of people’s health and how that might offer new opportunities for changing medical practice.  Just think about crossing these data with your genome and it gets pretty interesting.