Dr. Brigitte Piniewski is convinced that mobile and wireless technologies can bring the kinds of improvements in population health that policymakers can only dream of. “I really think that’s where the vision is at,” she says.
Two weeks ago at the eighth annual Healthcare Unbound conference in San Diego, Piniewski said that the biggest trend she’s seeing in mobile and wireless health is connectivity. Now, in an interview with MobiHealthNews, the Portland, Ore.-based chief medical officer of PeaceHealth Laboratories discusses why m-health and crowdsourcing of data can be the gateway to what Europeans often call “high-definition living,” where people keep tabs on their own health, not just rely on healthcare professionals to detect illness.
“Normal doesn’t really mean optimal,” says Piniewski, who has wide latitude to experiment with technologies, so she makes it clear she is not speaking for the PeaceHealth provider network in the Pacific Northwest when she muses about mobile and wireless healthcare.
“We don’t really have a good measure of health. We can measure illness,” Piniewski says. She believes that 60 percent to 80 percent of common health outcomes are more related to lifestyle than genetic predisposition. That’s where mobility comes in.
Today, we have a “low-definition health system,” according to Piniewski. Take breast cancer. “We used to think it fell out of the sky and hit us,” she says. “But now we know [much of it] is lifestyle-driven.”
Lots of people wear pink ribbons and raise money in the fight against breast cancer, but those efforts only advance science marginally. In the future, people might be cognizant of “health experiences” such as how much activity they need to generate by their 10th, 15th or 20th birthday to marginalize the lifestyle contribution to the disease. “We all have accelerometers and put information into community data commons,” Piniewski envisions. “It allows communities to not go blindly into the future.”
Lest the notion of community data commons conjure up images of Big Brother monitoring every move, Piniewski says that repositories of de-identified can be part of existing social and work groups, such as schools, churches and employees. And the data should be simple and benign. Think physical activity, not HIV status.
Today, according to Piniewski, data sets are “islands of data.” A person’s weight taken in isolation really isn’t all that useful in the big picture. “Understand co-occurrences,” Piniewski, a family physician, says. What if it’s someone’s birthday at the office and there’s a cake? Two people could have lost a little bit of weight during the day, but the person who works out in the morning and then eats a healthy breakfast would likely be much better off after having a piece of cake than someone who skipped breakfast to save calories.
“Not all weight loss is health-promoting,” Piniewski explains. Yet, information about “high-yield health contributions” such as working out before heading to the office shouldn’t go into a vacuum anymore.
Piniewski started realizing this after she came to the U.S. about a decade ago. She trained in her native Canada, at the University of British Columbia in Vancouver, and later at McGill University in Montreal. North of the border, family physicians delivered babies unless there were complications that required an obstetrician. “I had this erroneous idea that if we did the first nine months right, you’d be fine for the next 90 years,” Piniewski tells MobiHealthNews.
Then she moved to Boise, Idaho, where she cared for with many underinsured and uninsured patients, including performing physicals for the JobCorps vocational training program for low-income youth and young adults. Piniewski saw how poor diet and exercise habits took a toll even on young people, setting them on an irreversible course toward unhealthy lives.
“They have very, very low expectations,” Piniewski says. “They will be underemployed and underinsured for the rest of their lives.” And yet they don’t use the healthcare system until they are truly sick. “We have this model that completely misses everything,” she notes.
“So, how would you solve this?” Piniewski asks. “You would need a scalable solution that fits in every household.”
And there needs to be a rules-based measurement such as personal insulin resistance burden, (the opposite of insulin sensitivity) according to Piniewski. “You can start to build a standard unit of health,” much as the British thermal unit measures energy, so there can be real-time and forward-looking analysis rather than the current model of retrospective analysis for epidemiological studies.
“There’s this space above illness,” Piniewski explains. “It’s just chock full of conventional wisdom, and that conventional wisdom is wrong.” This includes both the episodic approach to healthcare and the sick-care model that predominates. Connecting the islands of data will go a long way toward breaking the mold.
“Give groups of people an accelerometer to see if they’ve cycled to work,” Piniewski suggests. A wireless scale helps monitor weight change over a more significant period of time. Each member of a group would need to take a few drops of blood every few weeks for analysis, too, so people can know their insulin resistance burden and thus whether they are addressing the lifestyle causes of Type 2 diabetes.
And start early to address lifestyle. “Adjust the 10-year-old by a degree and you change the 90-year-old by many degrees,” Piniewski says. “You start to see a future where all of Chicago [or any other big city or small town] is starting to pull in an insulin resistance level.”
With this information in hand, communities can start funding programs to address real needs at the local level. “You can know in the space of months rather than within the space of decades,” Piniewski says. Having large cohorts of patients helps do more than just chip away at problems.
“Build the technology infrastructure so that health is the innocent bystander,” Piniewski says, taking the concept of “passive monitoring” a step further. “It’s the currency by which you get other things done,” such as earning free parking for employees or a climbing wall for a youth group to use.
This model may sound familiar to online gamers, a community that trades in meta-currencies and alternative currencies. “Mobile health needs to learn from the successes of the gaming industry,” Piniewski says, since the prevailing fee-for-service paradigm makes innovation difficult on the supply side of healthcare.
It might also sound familiar to drug dealers or users. At Healthcare Unbound, Piniewski joked about following the “cocaine model” to get people interested in mobile technologies. “If I want you on cocaine, I’ll give it to you free and get you hooked,” she explains.
(Piniewski discusses the supply side more formally in a paper she co-authored this year for the European Commission’s Institute for Prospective Technological Studies about crowdsourcing data and persuasive technologies to drive behavioral change.)
In any case, things do have to change. “Normal has almost no utility in a world that’s no longer accidentally well,” Piniewski says. The evolution won’t come from the top down or bottom up, but rather the middle out, similar to Wikipedia in that it gets many people contributing. The social app, with people competing against each other, is an early form of this vision, according to Piniewski.
Because there is a social aspect, mobile health is a “global phenomenon,” Piniewski says. It should enable activity to sync up to the developing world, where there’s still time to intervene because people don’t eat as many processed foods as Westerners. “Use data to benchmark our way out of poor health and allow developing countries to leapfrog our experience,” Piniewski advises.