Clinical vs. Consumer Data: Why Does It Matter?

The practice of medicine has historically been focused on the episodic treatment of acute problems, but today’s healthcare system is being reoriented toward a more preventive model. New technologies that allow patients to monitor and manage their own physiology are providing a boost to this shift. Technology is opening up exciting new opportunities, but the patient-generated data revolution is fraught with complexity that we are only now beginning to address.

Until quite recently, the act of measuring any aspect of a patient’s physiology was difficult, complicated and costly. For example, obtaining an EKG reading would require trained technicians to connect a U.S. Food and Drug Administration (FDA)-certified device to the patient in a doctor’s office. Such a test would be ordered only if there were a specific medical justification to incur the expense and inconvenience.

Today, however, there are a number of consumer devices that can collect an EKG reading on demand by the patient themselves for minimal cost and essentially no complexity. Sensors are far less expensive and, therefore, more widespread. Patients routinely use wearable devices to collect their own physiological data that are aggregated on their mobile devices and sent to a vendor’s proprietary cloud service.

This is not to say that data generated at home are as accurate or valuable as data collected by medical devices. The price we pay for ubiquity is lower accuracy and validity of the data. The sensors are engineered to a lower level of accuracy, and the conditions and methods of data collection are less rigorously controlled. Consumer-collected data are generally used for informational — but not diagnostic — purposes.

Recent advances in consumer technology — driven by big data and machine learning — are beginning to bring patient-generated data up to the level of clinical quality. At the same time, clinical devices are being deployed to more settings, including patients’ homes. Clinical and consumer technologies are starting to converge, and it is beginning to get difficult to draw a clear distinction between clinical and consumer technology.

This is an optimistic sign for the future, but in the near term, it introduces a number of vexing problems.

Interoperability

Consumer-generated data tend to land in a cloud-based service that is proprietary to the device vendor. As the number of silos grows, pulling the data together into a single view becomes problematic, especially considering that clinicians really only want to see patient data in one place: their electronic health record (EHR). The challenge is compounded because consumer device data tend to have a proprietary format and methods of transfer that are not commonly used in healthcare.

For providers, the solution to integrating and aggregating consumer and clinical data is to invest in a flexible, capable interoperability platform that can handle the novel data types introduced by consumer-generated data and standard healthcare data formats and protocols.

Reliability

Clinicians are familiar and comfortable with the accuracy and limitations of data collected from clinical devices, but it can be difficult to interpret data collected by patients because the device measurement capabilities differ and the conditions and methods of collection vary. For example, an EKG collected by a technician in a doctor’s office is performed on known equipment according to a standard procedure. The results can be interpreted consistently. An EKG collected by a patient on consumer equipment has unknown accuracy and an unknown testing procedure. Whether or not the data are accurate, they will be treated as suspect by a clinician.

Consumer device manufacturers could include more meta information with their results that describes the conditions under which the data were collected and the known characteristics of the device. This would carry through to the display in the EHR so that the clinician could tell the difference between an EKG measured by a cardiologist in a lab versus one generated from a patient in a living room.

Security and Privacy

HIPAA applies to patient information created in a clinical setting, but it does not apply to data that a consumer collects or generates themselves. As data begin to straddle the boundary between these two settings, data governance becomes increasingly difficult. Clinicians and patients are not (usually) lawyers, and they might have difficulty determining how data are controlled and managed.

Data aggregators, such as the patient’s hospital system, need to set out clear, consumer-friendly policies that describe how data are to be combined, used, reused and managed. Consumers need to have control over where and how their data are shared and used.

These are significant, novel problems, but similar issues have been surmounted in other industries. There will certainly be a lot of hard work to do, but it’s definitely worthwhile, because this is a once-in-a-generation opportunity to permanently transform healthcare by involving patients in their own wellness and increasing their level of engagement. The hard work we do today will be the foundation of the next generation of healthcare innovations that improve outcomes and lower costs for everyone.

Drew Ivan is executive vice president of product and strategy for Rhapsody and Corepoint Health.Get the best insights in digital health directly to your inbox.

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