Decoding Health Data for Real World Clinical Applications
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Decoding Health Data for Real World Clinical Applications

By Dave Mihalovic, EVP of Experience & Innovation, Evoke Health

Dave Mihalovic, EVP of Experience & Innovation, Evoke Health

In the last year, there has been endless debate regarding the implications of patient health data and its potential to impact the future of healthcare. Innovation in consumer wearables and investments in clinical sensor technologies have made great strides in advancing the amount of personal diagnostic data that is now available on a per-patient basis. However, the issue we face is not about the data itself, rather its finding ways to collect and connect relevant data in a way that provides a holistic view of the patient.
In other words, the promises of health data rests in the ability to standardize a patient-centric data model — a model that is universally accepted by professionals, payers, and patients alike. This standard should transform volumes of unstructured data into a portable, relational model that tethers together all patient biometric and behavioral factors. But first, in order for this to happen, the industry must focus on developing data management models that adhere to privacy and consent policies.

So Close, Yet So Far

The consumer market for connected health platforms is slowly driving adoption of devices and apps. But the fact remains that of the 100,000 mobile health apps available in global app stores, 85 percent are for wellness, while the remaining 15 percent are for medical purposes. It’s clear that innovation in consumer health devices will not drive clinical adoption. Adoption will happen beneath the surface where obstacles like interoperability, security, analytics, and patient consent will be overcome at the most granular level.

Solving these challenges requires going back to the source—the individual patient data. The end goal is fundamentally simple; we need to derive meaning from aggregated patient data that’s collected from devices and a multitude of providers in a way that impacts clinical decision making. The complexity arises in understanding which providers need to see what data and when to make informed decisions.

When it comes to accessing patient data, privacy laws and patient consent rules add additional complexity to the problem. Providing the right data to the right doctor is one thing, but granting that doctor access adds significant complexity that can impact clinical outcomes.

"The promise of health data rests in the ability to standardize a patient-centric data model—a model that is universally accepted"

Governance of a patient data model based on complex rules becomes a critical requirement for any app, device, or platform seeking access to patient information.

Additionally, we can't forget the ever-present interoperability concern. Finding ways to connect and provide data procured from disparate systems, and the ever-growing landscape of connected health devices presents the largest roadblock. Meaningful use requirements have given rise to Health Information Exchange and efforts that pursue Interoperability and Standards across systems. But device-based information is additive to these efforts. It doesn't help that government policy created a cottage industry of EHR and patient portal start-ups, muddying the waters of interoperability.

The Physician Factor

The single greatest obstacle rests with the medical professional community. Physicians carry a lot of overhead on their time. A study conducted by the Annals of Internal Medicine found that physicians spent 27 percent of their day on direct face time with patients, and 49.2 percent of their office day on EHRs and other desk work. Adding additional overhead and complexity will only prevent adoption. Any effort to incorporate new ways to access and utilize patient data must be seamless, limiting additional time for both the physicians, and their staffs.

A New Hope

Complex problems are often solved when well-funded, great minds come together. In the case of designing and implementing standards for patient data, technology companies are partnering with clinical organizations to affect change.

Companies like Validic are working with all stakeholders, from health systems to payers, to drive both standards and governance related to the exchange of health data.

Alphabet’s life sciences unit, Verily is developing a “study watch” designed to gather complex, standardized health data from patients in a clinical setting.

And Apple’s foray into developing clinically tested sensors to track standard biomarkers for diabetes shows promise.

Conclusion

Any effort to develop a standards-based patient data model must start with the patients themselves. Focusing on data alone often leads to a solution, searching for a problem.

Standards must be designed to be usable by all patients and doctors. Data must be used to guide patients in managing their health, while helping clinicians focus on the best medical outcome. For now, consumer devices will drive patient behaviors; but in the long term, efforts in Health IT and technology will solve systematic standardization and compliance. The best outcome is that the way we provide the data, it intersects with the needs of the patient.

Check out: Top Healthcare Simulation Solution Companies

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