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With a plethora of wearable devices hitting the market that can track everything from a patient’s heart rhythms to walking to blood sugar levels, why can’t implants do the same? That’s the heart of the idea behind Canary Medical, which implanted its first sensor in a patient last year. The company has developed an integrated sensor that works with Zimmer Biomet knee implants. The sensor, the size of an AA battery, can track a person’s steps, walking speed, stride and range of motion.
Bill Hunter, a Vancouver doctor, founded Canary in 2012 with the idea that if smartphones could track health metrics, such as steps and heart rate, the technology could be incorporated into implants, to track progress. a patient after surgery or to warn if an implant is failing.
Earlier in his career, while finishing medical school, Hunter founded a company called Angiotech that combined drugs and medical devices, creating products like drug-coated stents. Now, with Canary, he is focusing on integrating sensors into medical devices, starting with the knees. He spoke in an interview about how the technology works and his long-term plans for the company.
This interview has been edited and condensed for clarity.
INDUSTRY DIVING: One of your first products is a knee implant made with Zimmer Biomet. Why start with the knees?
BILL HUNTER: When I was studying, I had a background in coronary stents, so it was all in the cardiovascular field. And, in fact, the [implant] itself probably most closely resembles a pacemaker. Technologically, it was consistent with what we had been working on for many years. [Now], the company works on all sorts of different devices. But five or six years ago, the technology was still at the beginning of its evolution, we needed space, and the knees were big enough for us to put one of these little things. It’s not that we woke up one day and said, ‘You know what? Orthopedics is going to be the killer app. It was that the total knee joints were pretty big, and we had to use a pacemaker battery and telemetry, and it all took up space. [In] a coronary stent, it was not technically possible. In electronics, the speed of innovation and miniaturization is spectacular. So we’ll end up getting into other stuff, but initially the knee was what we were able to do.
How it works?
It’s like a pacemaker battery: we’re asking it to last 10, 20 years, and we can’t recharge it. If I transmitted [the data] in real time, so you could look at your smartwatch or phone and see your stuff in real time, we would be out of power within two or three days. [So] the current is only on from seven in the morning to ten in the evening. Most of the time, the device is in low-resolution mode, collecting data at 25 observations per second. It’s for counting steps, cadence, stride length, gross activity, if you will. And then three times a day it goes to 800 observations per second. That’s when we do the gait, range of motion, very precise measurements, but we only do these measurements three times a day and we only do them for short periods of time because it takes Energy. And then, last but not least, you have to pass that data, which also takes power. We only do it once a day. So if you want to see your data, you will only see what you did yesterday.
During the first year, we collect data every day, but we go back in the future. We only collect them a few months a year for the next few years because at that time your doctor is just looking to see if there are any changes from the baseline. We do all of these things to save energy. The biggest part of this is the battery, so the less power you need, the smaller you can make the implant.
What are your ambitions for Canary? What other types of uses do you hope to achieve with this technology?
In my opinion, from the first day of the first patient, you can become a surveillance company. From day one, we can start counting your steps, cadence, stride length and walking speed. As a clinician, we can tell doctors if my patient is active or inactive? And, you know, recovering from a joint replacement, that’s pretty valuable information. But that’s all you can tell people at the beginning, because you don’t have a frame of reference. As we receive hundreds of patients, we begin to understand the difference between normal and abnormal. We have a cohort of patients who have recovered really, really well, and we’re seeing what their typical data pattern and progression looked like. Then we’ll have patients who fell behind or did poorly, and we’ll see what their patterns looked like. The goal is to be able to tell the clinician not only that my patient is active, but also that they are recovering as I expect, or is my patient falling behind?
Once you have 10,000 patients, you can hopefully tell them why they are falling behind. My patient is falling behind because he has soft tissue abnormalities, a tightness, or maybe he looks like he is at high risk for developing an infection. The final step… if I have 50,000 patients, am I beginning to understand best practices? Am I beginning to understand what types of rehabilitation centers work, or what type of surgical techniques work with what types of patients? What is the best way to care for someone after an operation?
Our hope is that as we gain this experience, and as we have more and more data sets from more and more clinical conditions, we can really contribute to the discussion by giving the clinician diagnostic and potentially prognostic information. And each of those things is a separate submission to the FDA.
How long do you think it will take to provide doctors with data-driven insights?
It’s really the more common the event, the faster you pick it up. In program development, the rarer the event, the longer it takes and the longer you will need to track those events. During the first year, we will probably understand the normal versus the abnormal. During the second year, we will begin to understand why it is abnormal. Is it an infection? Is it a contraction? Is it a release? Is it something like that? Because it’s a bit of a numbers game.
What does physician and patient adoption look like? I know it’s still pretty early.
We are currently in the early adoption phase. We have a few clinicians who put in a lot, so we learned from those people. What we’re doing right now, Zimmer calls it a limited release. The goal is to collect as good a set of data as possible, so right now it’s not about how much we’re trying to sell to how many people, it’s about trying to get the best possible results, because ultimately these results will determine the quality of the product which will ultimately determine how attractive this product is to other physicians.
On the physician side, how do you manage that so that it’s not just a bunch of data every day?
Giving the clinician more and more work is really not the point here. There is a graph of how the person is [performing] and are they above or below average, so doctors can quickly see how patients are tracking. If you want to hover your cursor over it and see exactly how many steps someone has taken, all the data is there, but we don’t bombard people with that stuff.
We did not submit this part [to the FDA] Again. But what we’d like to happen is a situation where you look quickly, you’re like, okay, this person is totally normal. Excellent. Or this person is behind in the following areas, is this someone I need to review? [We have] tried to make it very visual, something you can look at in two seconds and know what’s going on.
Do you plan to use this same system for other implants?
We currently have patients with multiple implants. Then yes. It was actually fascinating to watch because we could see someone recovering really well and all of a sudden the person stopped and we tried to figure out what was wrong and it turned out that she had returned to have her other knee redone. All of a sudden we had two knees on the grid from the same patient.
Let’s talk reimbursement: what avenues do you envision, both for the device and for the time doctors spend looking at the data?
CPT redemption codes already exist for data mining. So, to monitor your patients and use that data to come up with a treatment plan, remote patient monitoring codes already exist. The device was designed to comply with these codes and allow physicians to be paid for monitoring patients remotely. Use this as diagnosis [tool] or using this to predict complications, these will all be separate CPT codes that will be based on data.
As for the device itself, even that is TBD. The knee falls under a complete procedure code under a [diagnosis related group] where you get paid for patient care from pre-op to 90 days post-op. And so it potentially fits into that. However, as you can see, the device operates from day 91 to 20, which is well outside of this DRG. So I think even the device itself can end up with its own specific refund.