Point Of Care Testing Expansion Poses Data Management Challenges

Posted on November 3, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

With the advent of remote monitoring and other mHealth tools, the treatment process is again moving out towards the perimeter, perhaps not with a full return to house calls, but certainly a far greater emphasis on providing care in the field. And that will pose some new data collection and management challenges with their own unique character.

Collecting results from these devices won’t be difficult in and of itself. But we should think about how testing results vary from other types of physician-generated and patient-generated data before we pour it into existing oceans of clinical data.

A revolution in the wings
While you may not be familiar with the point of care diagnostics market, it’s definitely worth a look. The POC diagnostics industry, which includes both professional point of care testing and consumer options, should be worth almost $40 billion within five years, according to research firm Markets and Markets.

Over the next five years, a wide range of new POC options are likely to emerge, in categories that include ultrasound and other imaging, blood tests, cardiovascular imaging and more, Markets and Markets reports. And the devices that fuel this revolution are far more capable than a testing strip in a box; they’re emerging in a world where health advances are almost always found somewhere along the digital spectrum.

Want an example? Consider Scanadu Urine, a urine test kit designed to help monitor maternal and women’s health. The product package does include an old-fashioned paddle to dip in a urine sample, but it doesn’t stop there. Once the user dips the disposable paddle into the sample, they use the Scanadu app and their smartphone to read and interpret color changes on the paddle. Then, they can display, store or share the results via the app. Like many of its competitors, parent company Scanadu hasn’t gotten FDA approval for this or its other health monitoring devices, but that’s in the works.

Other niches already have multiple FDA-approved entrants, such as the mobile ultrasound category, but also emerging smartphone-based competitors such as Clarius Mobile Health. Like Scanadu Urine, Clarius isn’t FDA-approved yet, but the company reports that approval is pending.

As long as these devices remain unapproved by the FDA, they’ll stay in the background. But once devices like these get approved and start hitting the market, they should shake up the healthcare industry. After all, they don’t just empower consumers doing routine tests, they should also make it possible for patients to share important, reliable testing results to telemedicine doctors more or less in real time.

Managing POC data

Eventually, POC diagnostics data – even devices aimed almost exclusively at consumers — will become a completely standard part of the clinical diagnostic process. This much seems obvious. After all, if we want patients to engage with their health, putting powerful, reliable urine testing devices in their hands makes as much sense as giving them a connected glucose monitor, doesn’t it?

That being said, managing and integrating this data into patient data warehouses poses some unique challenges.

For example, how do providers weight the importance of various data streams when integrating them into databases?  After all, some devices are FDA-approved and some are not; some tests are administered by consumers and some by mobile professionals; some data comes from hospital- or clinic-provided remote monitoring devices and some from consumer-grade wearables or sensors.

Another question is how we’ll integrate these results. Even if we were to treat all data as equal (consumer- and professional-grade testing devices alike) do we have to integrate it in real time? Do we only do analysis and data dumps POC data into a big pool, do we pair it with other relevant data as needed or ignore it unless it seems immediately relevant?  We need to figure this out.

Bottom line, it’s probably smart to handle these data streams differently, but figuring out how to do so will be a challenge. We’ll have to develop algorithms for sorting this data soon, or risk being overwhelmed.