Most of the time I write about the psychology of patient, consumer or provider adoption. This is not an accident. The psychology of adoption is the next big hurdle for connected health to overcome. We have good evidence that connected health solutions can be engaging and sticky for patients, leading to improved self-care. Likewise, we have evidence that enriching data coming from patients to providers can lead to better care decisions and that these decisions, made and delivered in the moment of need, are the other half of the magic of connected health. Further we have a sense that those patients who are not interested in the level of engagement that connected health demands often have worse outcomes and therefore cost the system more.
But today, I want to talk about technology. Most of the time, I write from the perspective of a technology vision that includes continuous (or near continuous) sensing of multiple physiologic signals. These signals are flawlessly transmitted to a computing environment where decision support can be applied to aid in improved communication with patients and improved decision making by providers. The state of the art today is not so elegant. We use multiple different sensors, both wired and wireless, communicating via a large variety of aggregator devices that then transmit the sensor outputs to us via the Internet. The environment is both user-unfriendly and error prone, which increases the technical support resources required. We have the strong sense that some individuals drop out of programs because the technology is too challenging for them, so we miss them before we can turn them on to the benefits of a connected health experience.
The marketplace for sensors is changing in a number of exciting, dynamic ways. First, a number of sensors are coming to market that have embedded mobile chips right in them. They are sold in the same way as the Amazon Kindle (the wireless connectivity is bundled in the price of the device).
The Center for Connected Health is working with a number of manufacturers to evaluate a range of health devices. Examples we’ve been using, testing or looking at lately include the Bodytrace wireless weight scale, the Telcare wireless glucometer and the latest version of Vitality’s GlowCaps device. All of these devices can find the mobile network automatically when the sensor is triggered and transmit their relevant data to the Internet with no patient intervention.
A related approach is to create the wireless home hub that finds all of the sensors within a certain radius and send their information seamlessly via the wireless Internet. An example of such a device is the Medapps Healthpal. I am not sure which of these competing architectures will win the day when all is said and done, but they are both such an improvement of today’s complex, hard-to-use set ups, that they seem like nirvana at this point.
The second dimension of change I find exciting is that we are able to sense more and different things than we could in the past. A pioneer in this effort is BodyMedia, whose armband sensor uses an algorithm to calculate activity, caloric output, sleep quality, etc. The Proteus Biomed Raisin system pairs a small, embedded wireless chip in each tablet you swallow and a disposable band aid that one wears on the chest to capture the effects of the medication as it flows through one’s system.
Perhaps the most interesting area of sensor innovation is in the non-physiologic area. Cogito is a company whose product analyzes an individual’s voice on the phone and can predict their mood with a high degree of accuracy. Affectiva has two technologies that analyze emotional state by facial recognition and by a simple armband sensor. We’ve had too limited of a view of what we can collect from patients remotely and these emotional sensors add a whole new dimension to the objective data part of the connected health story.
You can tell I find it exciting to follow the success of these companies. We are busily working at the Center for Connected Health to put them all to work in real patient context in order to sort out their strengths and weaknesses in hopes of extending them into our programs as soon as we can.
I’m curious as to your thoughts. Are the examples I chose the best ones? Are there ones out there that I should know about but weren’t mentioned?