Last week, AliveCor and Practice Fusion announced a partnership that would enable strips captured through the AliveCor Heart Monitor to be imported directly into Practice Fusion’s cloud-based EHR. On the face of it, this capability should not be all that exciting.

It’s certainly not a huge technical leap forward. However, in the context of a heavily siloed health IT world, it approaches revolutionary. For clinicians, this partnership raises interesting questions and opportunities for day-to-day practice.

Given the sheer number of emerging consumer health devices, one of the key questions will be–how do we find the sweet spot between patients collecting valuable information that disappears into the cloud and a thousand streams of data leading to paralyzing information overload?

As the quantified self moves forward, there are numerous traditional and novel direct-to-patient personal health devices becoming available. These include iSonea’s wheeze monitor,  as well as blood pressure cuffs, weight scale, body fat analyzers, pulse oximeters, pedometers, sleep monitors, and much, much more.

Partnerships like that between Practice Fusion and AliveCor — as well as programs offered by traditional EHRs like Allscripts and Epic — suggest that data from these devices will increasingly find its way to clinicians. This capability could certainly open a number of opportunities such as enabling healthcare professionals to essentially construct personalized telehealth programs for individual patients.

Take, for example, an average patient with the usual picture of metabolic syndrome. In practice, I often suggest a combination of calorie counting with any number of apps or websites, activity tracking with pedometers (i.e. walk more than 10,000 steps per day), a weekly weigh in, home blood pressure monitoring to support medication titration, and so on. With these devices seamlessly integrated into my EHR, it’s not hard to imagine how I, a PA/NP, or nurse could spend some time reviewing, say, weekly reports and providing feedback and support to patients.

However, there is also a potential downside here–data overload. Consider, for example, the average primary care physician who may care for a panel of over 1,000 patients. It is not hard to imagine how things could get out of control. If each patient has a single device that generates a monthly dataset (e.g. a blood pressure spreadsheet), that’s over 30 reports per day.

In addition, the meaning of some of the data being captured is not entirely clear. Take, for example, iSonea’s wheeze monitor – the meaning and utilization of the wheeze rate remains fairly undefined. In the absence of some algorithm or framework with which to interpret and act on, the data being collected will make it that much more challenging to manage a patient panel with highly varied streams of information coming in.

There are several ways that healthcare professionals could approach the availability of this data. One strategy is to require healthcare professionals to opt-in to receiving specific feeds from a patient. In the example I outlined above with the typical patient with metabolic syndrome, it is generally those first six months or so where such active management would make sense. Considering reimbursement options available to support such activities, I may ultimately find that it’s feasible to monitor 20 patients at a time. Or perhaps 40 patients on a biweekly basis. An opt-in approach would give individual clinicians flexibility in developing strategies that work in their practice.

Another approach is to use support staff with algorithms for interpreting and reacting to information various patients transmit. There may be a big opportunity here for automated platforms that respond to this data–think an app store within the EHR where clinicians can download tools relevant to their practice. For this to work, we’d need validated, off the shelf tools that are minimally resource intensive. As an example, a variety of studies have looked at using automated, adaptive SMS systems for everything from medication adherence to weight loss. Overall, the type of integration demonstrated by this partnership is likely to be an increasingly common sight in the future. It’s important that clinicians anticipate this and help lead the way in defining how we will incorporate the ever increasing amount of data being captured by consumer health devices into day-to-day clinical practice.