5 Best Practices for Using Connected Devices in Clinical Trials

What are connected devices and how can they be best used to advance the treatment of chronic conditions?

In this on-demand webinar, health information technology subject matter expert, Rick Strobridge, provides his insights about best practices for using and implementing connected devices in healthcare and clinical research.

Drawing on his experience spanning two decades, Rick will take you on a tour of the latest and greatest tech, and how it's changing the way we provide care every single day.

Plus, he'll uncover what makes certain technologies rise above the rest by uncovering the 5 best-kept secrets for ensuring new technology is adopted and accepted.

 

Full Transcript

[00:00]

MODERATOR

Hello, good morning or good afternoon, everyone, depending on where you are tuning us from, and welcome to today’s webinar. My name is Earnest Attoh from Business Review Webinars, and I will be your host. It is our pleasure to have CRF Health with us today, who will be discussing on the webinar entitled “Five Best Practices for Using Connected Devices in Clinical Trials.”

Today’s guest speaker is Richard Strobridge, a Vice President of Healthcare at CRF Health. Rick is a recognized expert in healthcare information technology, medical imaging systems, and medical technology integration. He implemented some of the first video networks in the US, and his career includes many firsts in the video networking field. Rick co-founded Infomedix Communication Corporation, now Stryker Communications Division, which enabled the centralized control of sophisticated video and medical devices in more than 5000 operating rooms in 500 facilities worldwide. Rick also founded Healthcare Technology Corporation, HealthcareTec. Rick continued the development of innovative operating room designs to provide safer and more efficient surgeries. In 2007, he co-founded Entra Health, which is now CRF Health, with Larry Mahar. Rick is also a graduate of Colgate University in New York.

So without further ado, please allow me to welcome Richard. Richard, over to you.

RICHARD STROBRIDGE

Great. Thank you very much, Earnest, I really appreciate it. And everyone on the line, thank you for spending an hour of your day with us today to hopefully have a good rounded interactive discussion about using connected devices in clinical trials. I don’t profess to know everything about using these devices, however I have been using connected devices in clinical research for about ten years in about 75 countries. And as you all know, this technology is changing on a daily basis.

So what I’d like to do is spend the first few minutes of the time today talking about connected devices in general, a little bit about the Internet of Things, and then a subset called the Internet of Medical Things. And then I’ll get into what I consider to be five best practices for using connected devices in clinical trials based on my experience. After that, we’ll spend a couple of minutes talking about what I’m looking at in terms of the future of clinical trials using these connected devices. And then hopefully we’ll have a good interactive Q&A session.

So really, what are connected devices and what is the Internet of Things and the Internet of Medical Things? Of course we all know connected devices such as smartphones, tablets, and computers. But beyond that, and especially with the advent of the new 5G cellular mobile technology, there will be a low-bandwidth spec in the new 5Gs they’re rolling out to include really an Internet of Things, so things with IP addresses that can just sit there and actually sit dormant, they’re designed to have battery lives of up to ten years, and sit there passively collecting data and then transmitting it as necessary. And then the subset of that is the Internet of Medical Things, and the Internet of Medical Things are these same kinds of devices but really oriented toward collecting healthcare data and data about individuals’ health.

So what is a connected device? We all use smartphones and tablets. Other connected devices such as Bluetooth speakers. There are others, like home motion sensors and those kinds of things, things that are connected directly to the internet or via an IP address to some network. And then the Internet of Medical Things includes things like that Class I, II, and III medical devices such as glucometers, spirometers, even unregulated devices such as activity trackers, cardiac monitors, body temperature devices, things like that. In fact, just yesterday I was reading about a new device that actually sort of looks like a hearing aid, if you will, it goes in your ear and it actually collects data on activity, body temperature, pulse oximetry—so blood oxygen level—as well as blood pressure. So we’re finding that these combined devices are also hitting the market as well, and they will all be connected, they will no longer be standalone. So connected medical devices are wireless, wearable or implantable digital technologies used in healthcare and clinical research to collect data about a patient’s health. And these devices may be regulated or not regulated.

[05:23]

So Earnest, I’d like to do a poll now, if you don’t mind, from the audience. So I’ll pass it back to you.

MODERATOR

Thank you very much, Richard. It’s now time for our first polling question that will be running in today’s session. Please select the answers that are relevant to you, and then click “submit.” The question reads: What kind of remote data collection are you interested in? Please mark the top three that apply to you.

Richard, what are you expecting here from the audience?

RICHARD STROBRIDGE

Well, depending on their positions and the kinds of companies that they’re from, I would expect quite an interesting combination. I think probably the most common devices that we’re all familiar with are potentially weights, scales, and activity devices. But as I said, there are many other regulated connected devices for collecting biometric data passively.

MODERATOR

Great. Thank you very much for your answer there. Let’s reveal what the audience have chosen.

RICHARD STROBRIDGE

Oh, medication adherence, very interesting. Yeah, very interesting, thank you. Yeah, activity is one at the top, but the medication adherence selection actually surprises me a bit. And I assume that, based on this audience, medication adherence regarding and relating to protocols is of interest. So that’s great, thank you very much for that.

So we all have a medical selfie. That medical selfie is very similar to what Google and Amazon and others understand about our shopping habits. And that medical selfie comes in the form of devices connected to smartphones, smartwatches, which are also collecting a lot more than simply activity. In fact, Fitbit announced last month that they were now connecting to the Dexcom continuous glucose monitoring device. So the lines between what we would normally call wellness devices and what we would call medical devices are certainly beginning to blur. And then the data is collected on apps on our phones and databases, in electronic health records on pharmacy databases such as those at CVS and Walgreens. And all that information combined right now is very much siloed. And I think the evolution that we’re going to see with these connected medical devices is really the ability to collect all that information to create a medical selfie for each of us.

And that data will be much easier to collect. If any of you are familiar with some of the new voice control technologies out there, such as the ones from Google, Amazon, and the new one from Apple, the ability to collect this data and to ask for information is really increasing quite rapidly. These are all voice controlled devices and they’re relative smart voice controlled devices, some are better than others, but they’re constantly learning new skills to create this view of the population and a group of health outcomes relating to a particular population or segment of a population.

I’ll give you an interesting one that I as just reading about last night. There is a respiratory, a CPAP company here in San Diego called ResMed, and just this month, Resmed collected information on more than one billion nights’ sleep from a worldwide population of about four million devices. Now these CPAP machines sit on the bedside table and you use them at night. Only a few years ago, none of these devices were connected. Now they probably will never ship another one that is not connected. And you can imagine what you can do in a population health scenario when you talk about big data. The population health scenario would have information on one billion nights’ sleep.

[10:00]

In addition there are many interesting new apps that are out there, not only for therapeutics, but also for diagnostics. Here’s a particular example from Duke University, which is a facial recognition system and app that’s used on an iPhone to actually diagnose autism. And, as many of you know, the earlier the diagnosis, the earlier the treatment and intervention, the better the outcomes. So this particular app is used in children as young as 18 months for a diagnostic tool that can be used independent of being in a clinic or being in a doctor’s office.

In addition there are many many wearable technologies—Apple Watches, Fitbits, they seem to come out with new models every week with more features. And some of the features that some of these devices do are quite interesting, in that the data is collected and put together into your medical selfie, you can see that there are a lot of opportunities to better understand how a particular drug or how a particular other intervention is acting on the individual.

One that I really like and actually use every day is this cardio device from AliveCor it’s a single ECG that’s very simple, it attaches to the back of your smartphone and gives you an ECG, and then automatically, with no other medical intervention, looks at your ECG and gives you an all-clear or hey, you better look at this a little bit more closely. We’ve actually had a number of users on our system, because we use this in ours, that have detected AFib from only using this device, and then have been able to get to a physician quickly to better diagnose that and treat it.

One thing that we see in almost every clinical research protocol is a 12-lead ECG. I have not done a project where we’ve used a 12-lead ECG at home. However, this is one that is there and available. This is a full 12-lead ECG that is a chest strap type device, that creates Einhoven’s Triangle and gives you a transmitted 12-lead  ECG from home.

Just to round out a few other things that are out there, and I’m not promoting Apple by any means, but I just went on and looked at some of the different types of apps that are available. Asthma, COPD, concussion apps, diabetes, of course, are all available from an app standpoint and they almost all come with some form of connected technology.

We’ve already talked about voice control and response technology. This is interesting because we can not only receive things like the earlier slide talked about medication reminders and things like that, but also provide information into the clinical trial database using voice control and voice response technology. And these things, because they’re being driven by consumer trends and don’t have to be really deployed based strictly on clinical trial, you can see that the installed base of these will certainly give us an interesting new dimension to the kinds of the data that we can collect.

And you can add skills to these. So if you add skills to one of the voice controlled devices from an app, and you can have symptom trackers, workout trackers etc., all of which are getting more and more sophisticated. And there’s some really interesting ones on there. Not too much in clinical research yet, but that’s certainly an area where this tool set could be really effectively used. And it’s an open architecture, meaning that these devices can be controlled by apps that are written by others and then selectively deployed.

So, from therapeutic to diagnostic. Using connected devices and instituting the whole concept of the medical selfie obviously points to the fact that treatment should be proactive and not reactive. We want to be able to treat patients continuously in an automated way, not only when there is a disease or condition to be treated. Obviously we’re going to treat the disease or condition, but the idea of really getting out there and understanding what’s going on with the population long before the disease is diagnosed.

[15:18]

And an example is, there are about 30 million people diagnosed with diabetes in the US, and they think at least another 8 million that are undiagnosed or unaware of their diabetes. Dexcom and Google have publicly announced a project that creates a disposable patch to help diagnose diabetes or pre-diabetes without any clinical intervention. And so as you can see, as that data is becoming more and more available, there are some very interesting things that can be done on the diagnostic side as well as the therapeutic side, so we need to expand our thinking as we’re designing protocols and looking at using these kinds of devices, about the information that will already be out there.

So, 46 billion Internet of Things devices by 2021. This is a huge number, one that makes folks like Qualcomm very happy because they’re leading in some of the Internet of Things chip devices. But these are going to be very low-power, low cost communications devices that will be built into many many things that we otherwise would not expect them to be built into, and will give us the opportunity to collect data that was never before available. We talk a lot in this industry about bring your own device. In a few years, once 5G technology is implemented, I don’t think we’re even going to be worried about bring your own device, I think the data will just be automatically available and passively collected. And then it really becomes a question of, in this big data scenario, what do we do with that data, how do we get to the data that we want, because I think that there will be many many additional parameters, which we’ll talk about a little bit later, that could be very interesting in use in clinical research.

So, to cloud or not to cloud. Actually this is sort of a rhetorical question now, because I think everything is going to be collected to the cloud. I actually have my paper medical record from when I was born until I was about 13 years old or 14 years old, I guess. That record is sitting at home, that information is not available to anyone. And it can’t be really used for anything. Nowadays with electronic health records and especially the push here in the United States for really getting electronic health records in use, and Medicare actually came out with a new QPP MACRA final rule just last Friday, talking about additional financial incentives for remote patient monitoring for the Medicare population, which of course Medicare pays for about half of the healthcare costs in the United States. And so they’re incentivizing this remote patient monitoring because the intuitively know that this data collected by these connected devices and Internet of Medical Things kind of devices are really going to give a much clearer picture on what the population is doing, and then what me or you as an individual, and how we fit into that population and how we compare that population.

So that’s the quick update from my side on the kinds of data that can be collected. As I said, it’s changing almost on a daily basis, and we can all be interested in this technology, all be very engulfed by the technology. But what I’d like to share with you now is what I found to be five best practices. Again, they’re probably not all the best practices, but they’re the ones that I have found. Five best practices in implementing connected devices in clinical research.

[20:00]

Those five best practices include:

Reliability, and all of the things that go along with reliability. We’ll dive into that.
Usability of the device, and how it can be deployed.
Fit for Purpose. There are all levels of data collection and there are all prices associated with those levels of data collection. And what I have found is, we should be using the devices that are most efficient for that data collection and for that particular endpoint that we’re interested in doing.
Efficiency. The efficiency around cost efficiency, and the efficiency around how it can be deployed and where it can be deployed and what it takes to deploy it.
And then finally of course, Regulatory, and making sure that you understand, as you’re looking to deploy a connected device in clinical research, you understand what the regulatory and other government regulations surrounding deploying that device in the particular geographic areas that you’re looking at doing it.

So let’s dive into this a bit.

Reliability is the first one. So these are some of the kinds of things that we want to look at when we’re looking at selecting a best device from a reliability standpoint for use in a clinical trial. Wireless versus wired. Of course not everything is wireless nowadays. And we’re all moving in that direction. But there are many devices that are out there that connect to the cloud still via a wire and trying to figure out and think about how that device will actually be used. We use many wired devices in our clinical research, simply because they’re the best devices to use, they’re the ones that are fit for purpose, which we’ll talk about in a few minutes. The different kinds of wireless technology—Bluetooth, Wifi, Zigbee and near field communications—are all out there. They all have pros and cons. Bluetooth and Wifi of course are the most prevalent. And really, the advent of Bluetooth low energy has really made this thing of automated connection much more real. We have been deploying remote patient monitoring kits, as I said, for about ten years. And of course we were using the Bluetooth classic. We had a lot of issues—you know, anyone that’s tried to connect your phone to an older pair of headphones or something like that, with pairing and authentication and then reconnecting, can be really a big problem. With Bluetooth low energy or Bluetooth 4.0, that automated connection built into that spec has become much better. And in addition, standards around connecting medical devices in a standard protocol are also built into that Bluetooth step.

Battery life and then of course battery access if you need to change those batteries, the Internet of Things and as I mentioned the 5G communications low bandwidth spec, 5G will be faster and faster top-end speeds. But the interesting thing about 5G is also that there will be a low bandwidth spec which allows for the data collection of very low, very small sized data packets over an extended period of time, which can lead to very long battery life. And then of course the reliability pieces around price and performance for that price.

So this is an example of a very ambitious company called Scanadu that went out there with a device called Scout. And it was really a nice device, made a big splash in the remote connected health industry, because it was a combination device and collected body temperature, blood pressure, pulse oximetry. And what they found was that it was the implementation was not done properly with Scanadu, and ultimately the device itself was shut down by the FDA. So we’ve got to be very careful when we’re looking at the reliability of devices to not only thing about how that device works in an office environment or something like that, but once it’s deployed, what are the implications of that deployment and what is the ability to recover if something like this happens to you.

[25:00]

Of course the Internet of Medical Things includes not only wearables and connected devices such as an external blood pressure cuff or something like that. It’s also starting to incorporate implantables. This is a company that we’ve worked with called Endotronix, that has a pulmonary blood pressure sensor that’s placed in the pulmonary artery, this is for Stage 3 heart failure patients. And when that reader on the right is placed next to the chest, it’ll read the pulmonary blood pressure 20 times per second and then it reports it back to the platform which is of course connected to the cloud. So not only wearable devices, but also implantables. This is not FDA-cleared yet, they’re going through animal testing. But this is a device where they are very focused of course on reliability because it’s very difficult to get back once you implant it.

So device-to-device connectivity, Part 11 compliance, we’ll talk a little bit more. But the reliability of being able to deploy a kit and then know that you will be able to collect that data every day for a long period of time.

Second best practice, usability. What we’ve found in our clinical trial work and also in our healthcare work is that these are the kinds of things that create a best practice around usability for these devices. Is it easy to use? Is it something that has a size, weight, and appearance really that will make patients want to wear it and use it? Is it a continuous or episodic kind of data collection? Is it discreet and comfortable? And is it something that will reliably collect that data?

So here is a new entrance from Google Verily, the healthcare Alphabet company, is this watch. Very usable. Actually usable by all age groups. And it’s a platform that tracks activity along with time and date, believe it or not. But it collects and can create gait analysis, heart rate, electrocardiograms, movement data over the course of a day, as well as measure electrical conductors in the skin. And it also detects ambient light and sound, for looking at sleep quality and that kind of things. So this device, which looks like a normal wristwatch, can collect a great deal of information about the activities of daily living surrounding a particular subject or individual.

Designing around patient centricity is very important. Localization, really listening to the patient’s voice, and really understanding the population that you’re deploying in, whether it’s a teenage population, a middle-aged employee population, or a retired or chronically ill population, we really need to focus, as a best practice on listening to the patient’s voice, listening to the individual’s voice, and really getting good feel for what will work for them in their normal life.

We’ve worked with a number of continuous glucose monitoring options in our business, and the wearable aspects of continuous glucose monitoring make the usability of these devices incredibly good and incredibly easy to use and sort of wear it and forget it. Sometimes the implementation of the devices and the application of the devices can take some training, but they have certainly in our work proven to be a very usable option for entry into continuous glucose monitoring.

So that was usability. The next one in terms of five best practices is: Is the device fit for purpose. And fit for purpose to us means: Is it accurate? Is it precise? Is the data valid? Have we looked hard about the data security, the cyber security of that data, is the data set that we’re collecting the correct data set or do we need something different from another device or a different kind of device? And then of course the things that we typically maybe overlooked when we’re looking at a new device that looks really cool like that Verily watch, is that technology void of errors and omissions surround the data collection that we want to do? And certainly is it safe to use?

[30:40]

So fit for purpose for us incorporates all of these kinds of things, which really need to be looked at across the entire device and the entire technology. But like I said, there are many many different devices out there, and you know, it’s really becoming hard looking at the kinds of devices that are out there, and not just looking at the surface about the functionality of that device but really digging in and finding whether it’s fit for the purpose that we want to use it.

And a good example of that is actigraphy. I talked to many folks, and some of you I’m sure that are on the call, regarding actigraphy. This is an area that’s of great interest in the clinical research space now. And what I did here was just put the Fitbit down—Fitbit of course as a product line of activity trackers that include a variety of other options, other kinds of displays, that kind of thing—versus an Actigraph watch which is sort of an industry standard for actigraphy. So depending on how you are using that device in your clinical trial, a Fitbit may be a fine option. And you know, a Fitbit of course is much lower in the price than an Actiraph device. The Actigraph device does much more than say a Fitbit Flex or a Fitbit Zip, that kind of thing. But if you’re interested in simply tracking steps as a secondary endpoint or tracking daily activity—how active are they during the day and when—a Fitbit can be a very viable option and a much less expensive option to deploy. And an easy option to deploy. We’ve done studies with both of these sets of devices, and in selecting those devices we want to make sure that the device we select is fit for purpose.

Connected spirometry is another good example. A home-based spirometry and the collection of these different devices—not only spirometry for COPD and asthma, but also inhaler sensors—we’re seeing a lot of interest in being able to do spirometry, FEV1, and collect that on a daily basis. I use the Cohero Health device that’s on the upper righthand corner of this slide on a daily basis to measure my FEV1. It measures three times, using an app. That data is transmitted and then charted in a remote patient monitoring dashboard. There are other devices that are out there. CRF quite often uses the Vitalograph device. And then an interesting one that has just entered the market is the one in the lower right hand corner called GoSpiro. These are relatively inexpensive and easy to operate devices. They can be used effectively by all ages with a little bit of training. They’re all wireless, and then they can connect to an internet-connected device, either a hub, a tablet, or a smartphone. But the selection again really is, what are we really trying to do, what data are we trying to collect, is that data that we’re collecting fit for the purpose and is the device fit for the purpose that we’re using it.

The fourth best practice is efficiency. And what does efficiency mean within this context to us? It means, does it fit within the clinical workflow? Is it easy to use? Is it easy to deploy? Is the device readily accessible? Does it have electronic health record connectivity if needed? Does it have a standard API, or is the API based on the Continua standard for medical devices? Does it connect to the ecosystem—we’re going to talk about ecosystems in the next couple of slides, where you can write to a single API and collect data from many many devices with one data collection API. And again, is the data set standard?

[35:25]

So we have found again, you look at a device and say, this is really neat device, and this is something that we’re interested in using. Well you really have to look at getting that device into the workflow and the ability and the ease of collecting that data and getting it where it needs to go.

So connectivity and interoperability is very important and it’s really gotten a lot better, as I mentioned, with the new Bluetooth 4.0 or Bluetooth Smart. They were difficult to pair and keep working. That has changed radically just in the last couple of years. So automated connectivity is really becoming a game changer where these devices will automatically connect to a device that is internet connected and then automatically pair and then exchange that data. So this is one of the things that I’m talking about when I mention efficiency, is: can that data be connected without any intervention by the patient. And that’s really what we’re shooting for, we’re shooting for a world where if they know how to use the device, and they use the device, they shouldn’t have to worry about how to get that data to where it needs to be. that should be done automatically. And all of our work starts off with that as a premise. We don’t always achieve that, of course, depending o the devices that we’re using, but I would say that, right now, 90-95% of our devices that we’re recommending to our customers are using this kind of auto-pair, auto-transmit functionality.

I talked a little bit earlier about ecosystems. There are several ecosystems that have popped up over the years. Probably the most broadly known in the remote patient monitoring field is Qualcomm 2net. Of course Qualcomm is the mobile chip company. They have a division called Qualcomm Life and they have an app and also a plug-in-the-wall hub, which is called 2net. And that device is really an ecosystem that allows many different wireless devices to connect directly to that hub and then transmit data to the internet. And that hub can be in the lunchroom or cafeteria of a business, and constantly collecting data from the devices that it comes within range of. It can be something that can be at home. It can be something that’s in the doctor’s office. But it’s just a very simple, easy way to collect data from spirometry, blood pressure, weight scales, and many of these other connected devices. So that’s direct device connectivity.

In addition to Qualcomm, there are also a couple of very interesting ones out there that I’m sure any of you have heard about. One is Validic. Validic is basically a consolidator of data that’s already on the web for these different kinds of devices. And Validic has collected, or written APIs, to many different databases such as Fitbit and Under Armour’s MyFitnessPal app and things like that. And they’re now also focusing on medical devices. And so you can write one interface to Validic and then Validic will take care of going out and getting the data associated with that patient regardless of what kind of wearable they’re actually using. Validic’s been very successful in that and they’re also really working hard to get electronic health record interfaces as well. There’s also another one that we work with called Redox. Redox does for electronic medical records what Validic does for device data. And so it allows data from a variety of different electronic health records to be collected through a single interface, and as these connected devices really continue to expand, we will see that getting some of this data out of electronic health records is going to be one of the parameters that we’re all going to want to use. 

[40:05]

And the fifth and final best practice is regulatory and government associated. This is of course more common sense, but certainly something that has to be looked at. If you’ve got a device, you want to use that device, you’ve got 35 countries in your clinical trial, is that device cleared in all of those countries from a regulatory perspective? What are the regulatory implications? And beyond that, since these are wireless devices, do they have the wireless radio licensure? We’re working on one now where it’s a wireless glucometer that wants to be used in Japan. And we have to go and get the Japanese version of the FCC radio license for that device. And of course that takes time. And that’s got to be built into the whole design of the clinical trial and the scheduling of the clinical trial. In addition, labeling is very important, of course localization and language is very important. Import and export requirements for these different devices and how to get them typically in kits or as standalone devices, into and out of the countries that are in question, as well as collecting that data. And of course everything around patient privacy and patient data security and keeping in mind with constantly changing international regulations regarding that. Tariffs and duties and that are also a significant cost impact if those are not thought of in advance of deployment in a country.

So those are our five best practices. In addition, what I’d like to add is what we’ve seen regarding 21 CFR Part 11 compliance data submission for clinical research. A validated system is key. Many of the data systems that are being used out there are not backed by an audited quality system or Part 11 compliance. And this is something that as you’re looking at using these different kinds of data systems and different kinds of devices, it’s very important to look at the Part 11 compliance in a comprehensive way.

So the future of clinical trials. Hopefully what I’ve given you is a good overview of where we are with connected devices, the Internet of Things, the Intent of Medical Things, along with five best practices—reliability, usability, fit for purpose, efficiency, and the regulatory and government implications—of deploying the devices. What’s going to happen in the future? I think I gave you a little bit of a hint with the whole concept of BYOD, because my particular feeling is that, although of great interest now, BYOD is going to become less and less important over time because we won’t need to bring your own device, won’t need to bring any device because these data collection devices, the biometric devices that we want to use are already going to be connected to the internet, so it will just make it much easier to collect that data. But in addition, using integrated data models, which we’re looking at hard now—of course the top one, biometric—we’re dealing with and doing on a day to day basis in our practice. But then looking at some of these other data sets that are available—genomics, in relation to precision medicine, are becoming more and more important and the databases surround genomics are becoming much more prevalent and available. Clinical databases, claims databases, data that’s coming out of electronic health records, seamless connectivity to that, now that the whole evolution of electronic health records, particularly in Europe and the United States, has really come to a point where much of this data is being collected and being put into electronic health records, where before, for instance when we started working with connected glucose data in 2007-2008, there was no place to put daily glucose data in an electronic health record, it was there as a lab result, which was an episodic thing. There was no way to visualize it. Now, the electronic health records are much better at being able to deal with this “patient-generated data.”

[45:21]

Behavioural and behavioural health and all of the things surround that, I think really is the elephant in the room that is being ignored, both on the clinical research side and on the remote patient monitoring side, in relation to all of our health. You know, we can be physically healthy, but if we’re not mentally healthy and if we’re not doing the right things, it can be very difficult. So I think that there’s going to be increased focus on behavioural health as we move into the future.

And then of course socioeconomic. This is data that may be collected at the physician’s office—maybe. But that data will be where we are, what our level of income is, what we’re buying, what we’re eating, where we’re going, those kinds of things. And the socioeconomic data, I think will be an interesting data set that will be mined more and more aggressively in the future in the same way that Google and others’ pop-up ads based on our internet searches, we’ll be collecting this data in a much more comprehensive way and using it in a much more comprehensive way.

So that’s just a peek into where I see clinical trials going. The lines will blur between wellness kinds of devices and actual medical devices, Class I, II, or III, in the US, as well as these massive data sets that are being collected from a population health perspective and how those data sets can be used in interesting and effective ways to really see what the outcome and what the effect of models is.

So hopefully I have given you a background on that. I’d like to turn it now back over to Earnest, to get an idea of what the audience is thinking of implementing these kinds of data sources.

MODERATOR

Thank you very much, Richard. And that brings us to our second polling question that we will be running in today’s session. Please select the answer that is relevant to you, and then click “submit.” The question reads: When do you foresee implementing additional data collection from sources such as genomic, social media, location, electronic record health data, etc? The following answers that we have available are: within a year, 1-3 years, 3-5 years, never, or I don’t know. Richard, what are you expecting here from the audience?

RICHARD STROBRIDGE

Well this is an interesting question because what I’m looking for here is not that people have really thought about this as much. I think location and geofencing is of increasing interest—I know we mentioned them in previous slides. But we all are hearing about genomic and precision medicine. We all use some form of social media typically, and the data that’s in electronic health records are becoming richer and richer. So it’ll be interesting to see how this particular audience feels about the timeframe when these might be implemented and used.

MODERATOR

Great. I also agree with you, it would be interesting to see the results.

RICHARD STROBRIDGE

Wow. I’m surprised. Within a year, that’s very interesting. And good to hear. I think that the 1-3 years was sort of what I expected. With the evolution of this technology happening so rapidly, I think that we are all going to be swept up in it, if you will, in the same way that the implementation of cellular technologies and now the implementation of the Internet of Things technologies will just be there. None of us can stop that progression, and really the question is whether or not we want to use it. And so I think 1-3 years, and “within a year” for 60% of the audience, is good. You know, the “I don’t know” is also as expected because we’re all trying to see really what’s going to happen and how that particular data can be used. Significantly is that 0% say never. So I think we’re all on the same page, at least in this audience, that never probably is not an option, we’re going to have to collect this data, and if nothing else the regulators are going to force us to collect data that is out side of our typical protocols.

[50:54]

So this formula for change is one that I like to use. And hopefully what I have given you today is a framework within which you can start thinking about using these kinds of technologies. Five best practices that we have used over the ten years that we’ve been doing this in implementing these technologies. And just for fun, this formula for change: dissatisfaction with the current method of doing things, times the vision of an alternative (which hopefully I’ve given you today), times taking the first step. When dissatisfaction, vision of an alternative, and first step multiplied are greater than the resistance to change, that’s when change happens. And we are interested in being part of that change, we’re actively out there innovating, we know you are too, and we think that this scale is being tipped as we speak.

So I’ll close with just some information about what we do at CRF Health. We have a CareMax platform, we collect clinical-grade data using the five best practices, and we use that clinical data both in the healthcare side of our business—so in remote patient monitoring for both well people and chronically ill people—and we also collect that data using our validated TrialMax platform.

MODERATOR

Great. Thank you, Richard. Thank you once again for tuning in. And I hope you all have a great day. Thank you.

[END AT 53:02]

 

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