Storytelling with Data

Alex Trahey — October 20, 2015

At Wellframe, we have a strong belief that successful healthcare delivery is driven by engagement. Patients are uncertain about their health status, and the traditional, low-touch environment just isn’t sufficient for their needs. Our solution seeks to use existing human relationships to deliver the information patients need and to strengthen those relationships over time.

The question is: in a world where both patients and caregivers are inundated with data from various sources, how do we provide useful, meaningful information? At Wellframe, it begins with the Big Data approach that so many organizations have begun to take, and grows into telling a story with the data we collect. We have the unique advantage of being able to monitor how exactly a patient is interacting with their care plan, down to the minute that they report taking a medication. This opens the door to asking new questions and gleaning new insights, but it doesn’t do much for our patients or caregivers on its own.

We’ve written here before that technology is most effective when it allows high touch, and we try to follow the same principle with data. Providing statistics to a patient or caregiver will be interesting, but humans can relate to stories far more than data. We want patients to truly understand their health, and we want caregivers to be able to guide their patients’ healthcare narratives with timely and effective interventions.

I’ll give you an example of the beginning of one story we’re telling; Medication adherence is a big concern for doctors and patients alike, and lack of adherence is one of the primary reasons why complications happen. Patients don’t always know when to take a medication, and research has shown that patients take frequent “holidays” from their medications for a variety of reasons1. At Wellframe, we tried to dig into that issue at a more fundamental level. When do patients have the most trouble, and is there a way to effectively mitigate those troubles?

Let’s call our typical patient A. She enrolled in cardiac rehab about 3 weeks ago to recover from a stent procedure, and is committed to getting better so she can continue to spend time with her grandkids while they grow up. She takes her morning medications around 8am and her evening medications around 7pm. Every Friday, she goes to play bridge with her friends in the evening. She receives the reminder on her phone, but she ignores it because she is not at home. Sometimes, she comes home, goes straight to sleep, and forgets to take her medication.

This is a scenario that’s pretty easy to imagine, and it’s one we see pretty starkly in the data. Patients take their medications very consistently from Monday morning to Friday morning, but record completions about 20% less on Friday evenings and Saturday mornings. With Wellframe’s platform, we can see this pattern, and inform the caregiver that Alice needs a gentle reminder to take her medication. The caregiver can send the reminder or a personalized message to Alice that will show up at a different time than usual on Friday. Learning from the data pattern, these reminders will encourage Alice to take her medication more often. It’s an easy solution to a fairly simple problem, but one that requires understanding the problem at a more basic level than we have in the past. In the one or two office visits Alice might make per month, the problem of skipping Friday night meds will almost never be mentioned. But now with Wellframe, we can give the caregiver the information she needs to understand her patient, and she can come up with the best solution to it.

That’s what high-touch really means, and it’s an example of what we do everyday at Wellframe. By allowing the data to tell a story about the patient’s real experience, we strengthen the human relationship between caregiver and patient and highlight the importance of providing more than just statistics. The context matters, and it’s what will make the difference for the provider and patient alike.

1 Vrijens, B, et al. Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ 2008; 336: 1114-1117.

Alex Trahey

Alex Trahey

Data Scientist

Alex is a Duke University trained data scientist and economist, and he is currently studying for his MBA at MIT Sloan.