Jacob Krive, University of Illinois

Is the Healthcare Industry Ready to Embrace a Consumer Wearable Device Revolution?

By Jacob Krive, Ph.D
Jacob Krive, University of Illinois

The success of these technologies also relies on simplification to target certain patient populations, ensuring secure data transmission, and that operational models are built to make effective use of the data.

The proliferation of digitized patient records and the development of smart consumer wearable devices will create innovations and new synergies in healthcare services. These innovations were often seen as inevitable, with time and technical sophistication serving as the two main factors that would impact personal data and healthcare information technology. Before examining the opportunities and challenges that can arise from the combination of these technologies, it is important to determine whether both of these sides are ready for this healthcare partnership.

Electronic medical records (EMR) initially failed due to lack of buy-in from clinicians. Later, these types of records became viable—propelled by stronger government incentives and new jobs that helped cross the lines between clinical and technology areas of the healthcare organizations. Few of these EMR applications are perfect and complaint free, but many are mandatory and have a large user following, ensuring the majority of patient information and clinical guidelines are digitized.

Having reached the plateau of development as far as the conversion from paper to electronic goes, medical and information technology professionals are wondering what lies next. Although this technology didn’t meet all of its predicted convenience expectations, this wave of digitization has still arguably increased efficiency and improved safety. Most importantly, this digitization initiated follow-up developments—from data warehouses to health information exchanges to mobile computing and predictive analytics—possibilities that would not have existed without digitization.

Consumer mobile devices have gone through their own waves of innovation aside from medical records. From addressing basic communication needs, to smart devices with many combined features, to physical minimization of all this sophistication into a tiny package that we now call a wearable device, the developments in this technology have made a large impact.

There are at least two categories of such devices in existence today that relate to healthcare. One type is a consumer wearable, such as a fitness tracker or a smartwatch that has been amended with health tracking features largely controlled by a device manufacturer. Another type is a patch, a device that can be attached to various parts of the human body to collect designated information, typically vital signs.

One challenge of consumer wearables is that the devices are limited in disease-specific algorithm development on an open source platform offering limitless innovation opportunities aimed specifically at patient care. For the monitoring body patches, they face the issue of being costly disposable hardware that is required to transmit data to a nearby mobile device that needs to remain within a close range. All of these devices—consumer level and proprietary body monitoring equipment—also face security and privacy scrutiny, including having to comply with HIPAA and FDA Part 11 privacy regulations that impact not just the device but also the data it carries and how that data is being transmitted.

While these challenges within devices and patient data are complex, there remains the lingering “so what?” question. Technology industries are known for projecting excitement about device engineering without a clear linkage to how these new technical innovations will actually improve patient health outcomes. Even the promise of discharging a patient who is using a wearable device that can transmit basic vitals data is huge progress compared to a weekly home health nurse who visits a patient and documents his or her condition, but is often too late to take a decisive medical action before the patient’s condition deteriorates.

Even with the clear benefits of wearables, hospitals and medical groups are not currently operationally structured to receive, observe and process the constant stream of patient-reported data made by wearable devices. Additionally, there is little to no insurance reimbursement to incentivize healthcare facilities to implement a wearables strategy—except for indirect cost savings in programs or situations like value-based care payment structures or individuals being able to avoid costly hospitalizations thanks to immediate measures taken upon receipt of the health reports collected from wearable devices.

Forward-looking health executives will embrace wearable devices capable of producing meaningful patient care data. Even if that’s before the point when reimbursement structures catch up with technological process, these executives envision that maturation of the operational models around wearables will ultimately leave them in the driver’s seat when everyone else will be looking to replicate. True innovators who use this technology will look beyond operational models and health and financial benefits, and dig deeper to discover how new predictive models that incorporate a steady stream of data can identify developing disease patterns. As deep machine learning not only ingests processes and reports data, the ability to build comprehensive predictive analytics models will also allow this technology to analyze trends, incorporate genomic data, and generate insights into earlier disease development and progression.

While present innovation is focused on the production of better wearable devices capable of detecting body changes and transmitting information securely to care coordinators, the real future of wearables is in what happens in the background after the data is generated and transmitted. The biggest winners will be those who are able to incorporate these new streams of patient data into comprehensive predictive patient care engines that are able to detect, categorize and act upon risk variables. These engines could go as far as generating new medical insights and discoveries via enriched data sets to obtaining real-time information from patients directly. This could allow for data engines to take care of all or most of the patients’ healthcare needs, effectively serving as a patient’s medical home.

The success of these technologies also relies on simplification to target patients like elderly individuals, ensuring data is transmitted securely, and that operational models are built to make effective use of all of the data. Ultimately, analytics built around enriched data collection models can truly advance medicine. This year promises to be an exciting time for healthcare giants and startups alike in terms of the operational adoption of these devices. The real winners will likely partner with progressive healthcare organizations to derive more than operational value from wearables through advances in data science that provide the ability to build new predictive models using expanded data set capabilities. Another critical variable in these devices’ success is the capability to plug these models into the patient care operations, therefore going beyond theoretical and small experimental advances typical of the academic exercises, and delivering scientific discoveries that arise from a wearable’s data. These possibilities are not solid healthcare payer reimbursed models, as revolutionary technological developments like these have no immediate incentives attached but the promise of better care delivery and eventual cost efficiency. Still, the groundbreaking developments in patient reported data collection and analysis from wearables will make a resounding impact across the healthcare landscape.

About The Author

Jacob Krive, University of Illinois