Unlocking Data: Moving Beyond Insight to Diagnostics, Prescription, and Treatment

March 28, 2019 Becky Hurt

Authored By Gene Dantsker, Qualcomm Life

At the 2018 HORIZONS conference, PAREXEL Informatics hosted a presentation by our colleague from Qualcomm Life, Gene Dantsker. Following are highlights of his remarks.

As a technologist working in the pharma industry for about two decades, I think it’s quite fantastic to see that so many different participants in healthcare are on the same page when it comes to technology adoption. It’s a very exciting time to be in this field, not only because of technology, but also because of its transformation to patient-centricity, just as other industries are becoming consumer-centric.

Everyone has taken a separate journey to get here, but this is how technology companies like Qualcomm have arrived. One could argue that the electronic underpinnings of mobile devices – semiconductors – are even more foundational to this new world of interconnectedness than the software we’re all using today. Semiconductor companies have enabled the integration of electronics to introduce the various communications subsystems – the so-called generations of cellular phones, starting with voice, then text, eventually adding data. Now, these devices have essentially replaced other means of communication, extracting data from our environments that are now essential to our daily lives.

Yet as we move toward intelligent healthcare thanks to the ability to extract value from data, there is a foundational problem: We don’t always have the necessary data to make an unambiguous diagnosis. We are moving down this intelligence pathway whereby care is becoming more and more inclusive of the information that’s out there, but has been locked in – sometimes in the physiology of the patient, in individual devices, or in a silo. The information has not been able to interact in a suitable care model. However, as healthcare models evolve toward outcome-driven objectives, where the ultimate deliverable is a healthy patient, there’s a strong desire to decentralize – for instance, to move the point of care from the doctor to the patient. Therein is the opportunity for personalization, because once you’re focusing on the patient, you can come up with healthcare solutions that are appropriate for that individual. And all this is driven by data.

The Internet of Medical Things

To that end, Qualcomm Life created an ecosystem almost a decade ago that we call the “Internet of Medical Things.” It’s a large and growing ecosystem of data streaming from medical devices that enables remote monitoring of patients whether they are in the hospital, the home or on the go. . These devices generate the data necessary to extract value that, in aggregate, our customers can use to drive their business models. This is where things get exciting, because regardless of where the data comes from, once it’s made HIPAA compliant and secure, the intelligent-care algorithms take over. Those algorithms are ultimately the ones that extract real, prescriptive diagnoses associated with the patient’s condition, or triage the patient population into those who need certain types of treatment.

For the patient, engagement is much simpler than pairing a Bluetooth headset. Basically, it works on the principle of plugging a small gateway hub into the wall; the hub wakes up, electronically sniffs the air, looks for nearby medical devices assigned to this patient, and starts collecting data wirelessly. Then it sends the data to the cloud through a cellular connection. The gateway can also be the patient’s phone or tablet.

It's important to note that the ecosystem is technology agnostic; it works with almost every phone or tablet on the market. We also have a program through which we add very low-cost chip modules to drug-delivery devices like respiratory inhalers or injector pens for diabetics. Every time a patient uses the device, the data such as time/date and dose goes through the patient’s phone to the cloud, and a machine on the back end examines the data. If a patient’s weight, for example, has been going up too fast, that could mean they’re not taking their $5 diuretics, which could result in a $10,000 admission for emergency care. This is a robust way to monitor patients and more importantly, to target those who need care.

For this informed connected-care model, one must be able to follow the patients throughout their journey up and down the acuity spectrum of their lives. For that reason, the ecosystem must be able to seamlessly transfer from the home setting to the mobile setting to the hospital setting and back again.

Outcome Management in Clinical Trials

To bring the discussion into the biopharmaceutical arena, the evolution of pharma has been described as an evolution in drug discovery. During the early stages of the drug discovery journey, there were no outcomes to measure. There might be 20 million albuterol inhalers out there, and that’s 20 million black holes of data. With a chip in them, you can see how the patients are doing. You can find out which ones are compliant and which are not, and thereby address them economically. That’s the world we’re in right now: a world of measurable adherence and compliance. Drugs are becoming connected. And with electronic prescription tools, there is more connectivity between the pharma company, payer and provider and their patients through use of modern tools by which other industries connect to their customers.

In clinical trials, that journey has commenced in outcome management by supplying patients with tools that can extract the data associated with whatever physiological condition is being treated. Qualcomm Life works with all the top pharma companies and contract research organizations (CROs), including PAREXEL, monitoring the outcomes of patients through the evolving ecosystem of medical devices I mentioned. The digital genie is out of the bottle; the data is out there for those who want to utilize it.

For pharma companies and CROs, digitizing clinical trials begins with decentralizing. The patient stays at home, and you supply them with medical devices that extract physiological and compliance data and transmit it through a gateway and ultimately to the trial sites. Let’s take, for example, a respiratory trial, and add therapeutics. You would use a subset of the devices that monitor diagnostics such spirometry in combination with a technologically enabled drug-administration device – in this case, the inhalers. Between the therapeutic data and the diagnostic data, you can conduct a trial allowing the patient to be monitored, determining whether they’re actively participating in the trial or dropping out, and of course, extracting value from the data. And now, by the way, instead of using the data only at the end of the trial, sponsors and CROs are starting to utilize it to potentially modify the trial or even fail fast midstream.

Once this combination of devices and therapies comes on the market, instead of a sponsor or CRO receiving the data, it could go to the payer, and to the patient, as well. The payer can monitor both compliance and outcomes associated with the patient’s use of their therapy, and ultimately deploy these products not as devices, but rather as patient-oriented disease management systems. As we move down this intelligence pathway, inclusive of a full spectrum of data, we can use it to drive not only insights, but diagnostics, prescriptions, and treatments.

 

 

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