Making resistance data available for clinicians to improve patient care and conserve antibiotics for future generations

BILL RODRIGUEZ: Well, when it started, every company, every NGO, every organisation, every software developer thought, oh, I need to develop a proprietary system, either because only I know my data, only I know my system, and I know my clients, and my customers, and my partners, and my ministries of health. So, I’m going to just do it myself and link it. And that quickly led to this mushrooming approach that was really fragmented. You used the example of Cepheid who had probably the first large instal base where data needed to be taken from remote instruments and sent back centrally for both quality control purposes, but also for epidemiology and public health purposes.

And here we are, six, seven years later, and it’s still not working, and it’s broken. So, I think the reality is, it’s hard to put the genie back in the bottle when this fragmented system is rolled out. But I think what we’re seeing now are other innovations in digital health that involve clinicians and health systems using digital tools and using mobile health tools and apps to aggregate information about clinical care, which wasn’t necessary how this started on connectivity for laboratory instruments. But now, they’re demanding that that information be integrated in their systems.

And so, I suspect what’s going to finally drive standards and integration of data are what the clinicians need to do patient care and all the tools that are now being developed for them that didn’t start as, how do you get data off an instrument, or how do you get epidemiologic data into a database, but it’s really about, how do I provide better care for patients? And that’s where you’re going to see all of the diagnostics manufacturers, all of the middleware providers, standardising against what those apps and those web-based tools and those digital tools need in order to integrate all the information they’re collecting, which goes beyond diagnostic data, right? They’re getting patient data, and demographic data, and pharmacy data.

And I think that’s where we’re finally going to see the field standardise a bit. And it’s not that different than what we’ve seen with electronic health records in high income countries especially, it’s the same challenge. All these proprietary systems that don’t talk to each other and lack interoperability. And now, slowly, it’s starting to integrate, because the clinicians need to speak to each other and they need to speak to different systems. And I think that’s where we’re finally going to see it happen, not because diagnostics companies can drive it themselves.

Policy tends to be late in the game, right? It follows the innovations that happen in clinical care and in public health. Because the policies need to be brought in general, and it’s hard for policy to be out in front and be in the lead. So, again, what I think is really going to make the difference is some entrepreneurial effort based on software and based on mobile health apps is going to see the need and respond to it.

So, as you and I both know, we train in a day where when you were taking care of patients and you had an infection, you had a little card that had the latest data on what’s the susceptibility for Staph or for Enterococcus, and a whole table of antibiotics. And you could pull it out of your pocket, and that was our tool, and it was outdated, right? And we knew what the data were from a couple of years ago. They were maybe from our hospital, but it was the best we had.

Now, we can actually maintain real time data, and it’s going to be someone who sees, well, I can tap into these data sources that are coming out now in these surveillance networks, in part driven by COVID, but like you said, and in other databases. And say, I’m going to pull that data on antimicrobial resistance patterns. I’m going to aggregate it, and I’m going to have it regional or local. And I’m going to offer it to clinicians who can just say, oh, I’ve got a patient with a Klebsiella UTI. And let me see. I’m going to look at my app, and it says, here’s the resistance pattern in your region. And it will be totally imperfect, right?

The first versions of that are going to be off. But it’s bad data, as in this case, probably the first step to good data, and is better than no data. And I still think it’s going to be that entrepreneurial effort that’s going to drive the change that we need to see in getting and making real change in reducing AMR. Because it’s really only going to reduce when we start having targeted use of antibiotics for the right condition, for the right patient, and the right pathogen. And you can envision that tool being in a clinician’s hand, just like that card was in our hand 20 years ago.

And slowly the health systems will catch up, and slowly policy will catch up to what’s now being widely used. I think that’s how it’s likely to roll out. And then I’ll make one more comment, which we’ll also see better hardware, right? So, it’s hard right now in most low and middle income countries to ever get a report that says, this is Klebsiella growing from your urine. But we’re going to see new diagnostic hardware that makes that affordable and accessible in global settings. Not tomorrow, not next year, but certainly in the next 10 years, and probably in the next 5 years.

And it’s those systems and the availability of pathogen identification that will then allow clinicians with the appropriate tools and apps to be able to say, OK, now I know how to treat this. And that’s where I think we’ll see the first wave of real meaningful change coming out, when those two systems come together.