It is well-known that the healthcare industry tends to be slower to adopt new technologies, even though it often stands to be among the biggest beneficiaries. Nowhere is this more the case than in the use of big data.
While big data from social media has already shown itself to be invaluable as an early warning system for disease outbreaks, there are other potential uses that speak more directly to the individual. For instance, what if your healthy lifestyle choices would translate into lower insurance premiums?
Insurance companies are showing increasing interest in the so-called Quantified Self (QS) movement and apps, which involve incorporating data acquisition into aspects of a person’s daily life, including foods consumed, blood oxygen levels, physical activity and even the quality of the surrounding air. It usually requires self-monitoring or self-sensing technology. Aetna’s CarePass is an example that moves in this direction. Here, the health insurer is using IT to help people choose apps to monitor progress toward a healthier lifestyle. It is yet another indication—along with discounts for not smoking—that health carriers might be ready to follow the lead of auto insurance companies and reward responsible lifestyle choices with lower premiums, just as safe drivers get breaks. Ultimately, this may well lead to healthier people and the real Holy Grail—lower healthcare costs.
While an attractive thought for many, it requires—like most potential data advances for healthcare—a willingness by individuals to share personal information. In general, people don’t like to share their data and in fact there are relatively strict laws that protect them from having to do so. But part of that resistance may stem from the fact that insurance companies are not transparent about how they will use the data when they get it.
There’s no doubt that the lack of transparency around data usage is one obstacle that is slowing its application in the health arena. A recent study revealed that 20 of the most popular health apps are sharing data with more than 70 advertising and analytics companies. Did the consumers who bought them know that would happen? Perhaps the tech savvy among them guessed as much, but these were being sold to a much broader public that was unlikely to suspect. And while the developers might argue that the intelligence would help them perfect the product and more effectively market, it doesn’t seem unreasonable to ask that consumers be told in advance about where their data will go.
If the QS revolution is to be adopted en masse, where almost everyone’s data is collected 24/7 in a passive and non-intrusive fashion, resolving privacy issues and upselling the benefits of these devices to both healthcare providers and patients are paramount. But the level of distrust may make it a tough sell initially.
John Wilbanks, Chief Commons Officer at the open science biotechnology research institute Sage Bionetworks, suggested in his TED talk that we should “pool our medical data,” that apps can make discoveries much more quickly and cheaply than traditional research. For example, with a data set of millions created in just a few months, the diet app Eatery found that people rate their own food as healthier than other people’s. This reveals a lot about attitudes and perhaps why dieting is so difficult for some. Yet, without being able to integrate this finding with existing research its benefit is minimal.
The Indiana Health Information Exchange and Predixion Software have been working in partnership to use pooled data to reduce patient readmissions to hospitals. The Institute of Medicine advocates the use of big data to ensure earlier detection of disease outbreaks and as such allow better targeting of required services. Who can argue that any of these are bad goals?
When given the opportunity people are willing to go through a much more complicated routine to share their blood and even their organs, yet they are not given the opportunity to share their data, which is much less invasive procedure but potentially as useful. At the end of the day it will take a lot of education of both the industry and the consumer to fully incorporate big data into health.