Healthcare Sensors: Data Science Meets Medical Science
A look at the advancement of healthcare devices and how technology has changed the industry in the past year.
January 14 , 2013
Devices that collect personal medical information are growing both prolific and inexpensive. As part of the burgeoning “internet of everything,” more and more of them come with wireless capability that lets them transmit data over either Wi-Fi or cellular network.
At the recent mHealth Summit in Washington, there was even a $199 case from AliveCor that fits on the back of an iPhone and helps medical professionals monitor electrocardiogram rhythms. Simultaneously pushed by technology, economics, and the requirements of the Affordable Care Act, electronic medical records (EMR) are finally becoming a widespread clinical reality.
In an ideal world, patients and doctors would agree on the use of these home medical devices and the stream of data they provide would automatically find their way into the patients’ EMR, where they would be available for monitoring by providers or computerized analytics.
Unfortunately, the day when this sort of thing becomes general practice is still a ways off. In fact, in the year since I last looked at this subject, there has been a great deal more progress on the two ends of the problem—the devices and the EMRs—than on the networking infrastructure that is needed to connect them.
This is not a simple problem to solve. Medical communications have to jump through all sorts of regulatory hoops designed to protect privacy, security, and reliability. For example, don't expect that AliveCor unit to send EKG data from home to your cardiologist any time soon; for now, the device is only approved for use by health care professionals.
At last year’s mHealth Summit, Verizon announced a Digital HealthCare Suite that would provide, among other things, communications between home-based sensors and doctors. A year later, it was showing videos of its Converged Health Management system in action, but they carried the disclaimer that it is still awaiting regulatory approval in the U.S. Ryan McQuaid of AT&T’s mHealth Platform unit says the company’s goal is to “provide the entire solution,” but it currently offers only pieces of it, from cloud based systems that allow providers to share data to apps that can collect customer information from devices such as FitBit monitors and networked scales.
Qualcomm is somewhat further along with its Qualcomm Life 2net Platform, which is listed with the U.S. FDA as a Medical Device Data System (MDDS) and can link biometric data from patients’ devices to doctors’ systems.
Qualcomm is also going beyond the idea of using new technology to support the existing medical system to encourage disrupt[ive] innovations allowing us to more easily make reliable assessments that ‘de-skill’ medicine.
Teams of highly skilled engineers operated the first computers, but today’s smart phones (which have infinitely more computing power) can be operated by a ten-year old effectively. Why not apply such technology to healthcare?
To promote this disruption, companies are creating incentives for the development of more advanced home medical sensors. Qualcomn with the XPRIZE Foundation, is offering the $10 million Tricorder X PRIZE to the team that can come up with the best devices to monitor biometrics and diagnose diseases. Nokia is sponsoring a more modest effort, a $2.25 million X PRIZE competition to develop the most technologically advanced sensors for diagnosing and monitoring a variety of diseases.
Still, some of the biggest challenges lie not in collecting and transmitting the data, but in building the backend systems that can make sense of it. Dr. David Delaney, chief medical officer for SAP HealthCare predicts that players who can figure out how to apply real-time analytics and prediction to the streams of information being generated by mobile devices in health care “will have a huge competitive advantage. They will be the people who not only survive but thrive.”
One of the major improvements Delaney sees coming is in the metadata that allows the categorization of clinical information. The current system for coding conditions and treatments in use in the U.S. mainly serves the needs of health care payers, but is of limited value to clinicians. A new system, known as ICD-10, does a much better job of linking the data to actual treatment options. Current federal regulations require providers to move to ICD-10 by the fall of 2014.
The proliferation of sensors generating medical information and the growth of back-end systems to process it and EMRs to store it are creating the meeting of medical science and data science. If managed correctly, the outcome should be better health care for everyone.
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