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How AI is Transforming Prescription Efficiency

A simple Tylenol order can require up to 62 clicks for approval in certain medical systems. 

Inefficiencies like this can extend far beyond inconvenience and even result in fatal outcomes.

The National Academy of Medicine reports that computerized prescriber order entry medication errors are the most common type of error in healthcare, contributing to approximately 7,000 deaths in the U.S. each year.

With more patients and doctors relying on technology for accessing and delivering care, streamlined systems are more critical than ever before. 

Could AI offer a solution to this challenge?

Join us on the latest episode of CareTalk Podcast: Healthcare. Unfiltered., where David E. Williams and guest Emmanuel Bilbault, CEO of POSOS, discuss AI's role in prescription fulfillment and their innovative system enhancing healthcare efficiency with voice ordering and personalized patient treatment recommendations.

Episode Transcript:

How AI is Transforming Prescription Efficiency

David E. Williams: My guest today is Emmanuel Bilbao, co-founder of Pozos, which is partnering with Microsoft's Nuance division to allow clinicians to prescribe by voice and get point-of-care treatment recommendations personalized to each patient. Welcome to Cure Talk, Emmanuel.

Emmanuel Bilbault: Thank you, David. Very happy to be invited today and and share with great news about the partnership with Microsoft.

David E. Williams: Outstanding. Well, before we dig in, I'd like to encourage everyone to join the Fast Growing Care Talk community on LinkedIn, where you can dig deep into healthcare business and policy topics, access Care Talk content, and interact with the hosts and our guests. And please be sure to leave us a rating on Apple or Spotify while you're at it.

All right, I'll give people a second to do that. Now, Emmanuel, I want to start in the big picture and understand what are some of the challenges in electronic prescribing. I've been around long enough to hear, you know, when they used to talk about the, the prescription pad and nobody could read the, you know, the doctor's information.

Now we got to electronic prescribing. Why are there problems?

Emmanuel Bilbault: Yeah, for two reasons, actually. Prescribing is a, is a very complex process for for the medical reason. First, the clinical reason, because prescribe a script for patients is making clear that every drug is compatible with each other.

It's like one by one, you have to check if it's compatible with the other drugs that a patient takes. You have to make sure that. Each drug is compatible with the patient condition probably the patient renal function been patient this is this is, sorry these are function. Then you have to adapt the dosage to the patient with patient age.

So it's, it's a very complex process from a clinical perspective. But as you said when software were developed to help physician prescribe electronically. At the end it's even more time-consuming because the best, the very best EHR on the market is even like a crazy a waste of time.

You need at least 18 clicks to prescribe one medication, but you have to take into account that for the aging population, the average number of of of drugs in each script, it's seven. So seven time seven times you have to decide which drug and clicks on the drug name, then which Gallinic form.

So click on the one you want to select, click on the strength, because there is different strengths and dosing for the, for each drug and then formulate the dosage, which means like how many tablets you take in the morning for lunch, for dinner, for how long. So imagine like a long prescription for, for for your grandmother or grandfather, it could take more than 10 minutes.

That's why we, we had We need to do, we need to do something and and that's why we, we collaborate with Microsoft to help physicians save 10 minutes by patients and

David E. Williams: explain

Emmanuel Bilbault: later why.

David E. Williams: I mean, the thing is, if I, if I consider, sometimes you hear about, well, doctors don't like technology, but I think a lot of it has to do with.

They do like technology. They, they're big adopters of iPhones. They use a lot of things electronically, but I think it's the difference between what they see every day in their, in their life outside of work versus work. So you mentioned even if it's 18 clicks. You know, I go to Amazon and they're famous for one click, you know, I fill my shopping cart, you know, boom, boom, boom, doesn't take me 10 minutes to spend a ton of money.

And so I think some of it is just like sort of, you know, the disconnect and I'm trying to understand I still I get it that it's complicated, but it could also be complicated to buy something from Amazon. So, I mean, and Tylenol is pretty basic if you even have to prescribe it instead of just grabbing it off the shelf.

I mean, how could it possibly take. That many clicks. I mean, where, where does that really come from? You did explain it in a way, but I only counted up to about the possibility of like 20 clicks, but how could it possibly take that many?

Emmanuel Bilbault: Yeah, that's, that's complex interfaces. Actually. I won't blame the software publishers for that.

Because there are so many different kinds of prescription. I talked about tablets, but for some it's infusions and and you need so many options, so many possibilities to to cover the, the whole possibilities of a, of a script that interfaces are very complex. And that's why most of the medical software are moving to the, to the voice.

Because with this voice you can shortcut this process of clicking everywhere on the software. And that's maybe the only solution. The other one is AI trying to predict which drug you will prescribe for a dedicated patient. And maybe generate options, suggestions for prescription. In a bleak physician also to save time sometimes somehow with puzzles.

We are combining both both ways in one solution.

David E. Williams: So let's talk about AI first. One of the reasons that there's so much excitement about AI and healthcare is that there's a lot of really challenging problems. And if you, if you look at all these problems of, you know, workforce, errors, quality. Cost we've been working on these things for, you know, for decades and you look at them to say, well, we've made some progress, but I think overall, it's gotten worse rather than better in some ways.

So you say, maybe I can be a solution and it seems like that's possible. So if we look at a I. First and let's put voice off to the side for a moment. What is the biggest opportunity with AI in prescribing or how does it address, you know, some of those main issues that you talked about before?

Emmanuel Bilbault: I think AI is very interesting for us for one reason that somehow nobody really mentioned in a, in the conversation around AI in health is that medicine is not a perfect science somehow.

And for exactly the same patient. You will see 10 physicians prescribing, like, for instance, 10 scripts that are somehow different. And that means that there is no one solution or one great solution. There are many options. It's the same for diagnosis. For the same patient, you have like various diagnoses sometimes.

And that's scary. But that's where AI is great because AI is great in like processing the probabilities. Providing the best solution. And that's also why not only for diagnosis because we talked a lot about diagnosis for AI, but also prescription there are great opportunities to suggest the, the, the most tailored prescription for a dedicated patient taking into account, like all the patient history comorbidities Current treatments and making the best possible treatment for, for him or her.

David E. Williams: Well, I talked before about the transition from a paper-based prescribing to electronic prescribing. And while you had, you resolved certain issues, like it didn't, you could read the prescriptions handwriting because it was now filled in electronically. You started to have new problems that you didn't have before.

For example, with a pull-down list, I didn't have to worry that the, I might have to worry that the physician would write it. Something I couldn't read and it looked like something else, but you didn't have the issue that, you know, because of alphabetical order, they would pull down something, you know, one further down in the list that had nothing that they were nothing to do with what they were actually trying to do.

Are there new problems like that with AI in prescribing? So it maybe it's going to suggest something that nobody would suggest because it doesn't make any sense. Are there new kinds of problems that come up and how do we, and if we don't know that yet, how do we know and how do we become confident?

Emmanuel Bilbault: The way you talk about AI like means you're thinking about all the generative AI models somehow.

And those models are very effective when those using it can just verify quickly that it's right or it's wrong. If you can't verify quickly the accuracy of the results, then you shouldn't use it.

So that's basically the same for prescription. If the physician is is well trained it will be able to quickly identify that the options are So that's the way or so we, we think about it.

It's not like generating a prescription automatically and physicians just need to click and approve, but it's more about like suggestion, suggesting options that the physician will check and select, but options means right treatments get any forms and, and dosage at the same time. More than just like generate a prescription automatically.

And that's something close to the to the maps that we use and that we're like transforming to a GPS sometimes in a, in ways, for instance you have two or three options and you select the one that makes more sense for you. That's the way we, we think about like a. The best AI for, for medicine, letting the physicians always make the final decision.

David E. Williams: Okay. So if I use your ways example, I know that I want to go from point A where I am to point B. And I know what point B is. Maybe it's a certain restaurant or a store or a person's house in your, in your analogy here. Does the physician have to know the point B that they're going to? So for example, do you take as a given that the diagnosis that they have is the correct diagnosis and you're only saying given that diagnosis, here's these other ways to do it?

Or is it a broader decision support where you're saying, given all the characteristics about this patient, you're writing a prescription for Tylenol. Okay. But, but maybe it should be something else because maybe Maybe you should be treating something else. How far back does it go? How, how does it, you know, tie it into the, to the Waze model there?

Emmanuel Bilbault: Yeah, we, we usually think about a patient with one pathology, one drug, but the reality is like a patient has several pathologies and several drugs in parallel.

So, When you want to prescribe one additional drug for a new diagnosis you met then we think about your usual drug you prescribe for every patient because you trust this drug.

But then you discover with the AI that the drug you are used to prescribe is not compatible with the other medication of the patient. So it's not compatible with this it's comorbidities.

Then you have two options. For instance, I'll just give you an example. choose another treatment for the new diagnosis or switch a previous medication to another one.

So that's all. medication are compatible. This is a great example of two ways you have, and you will have to decide which one you want to, you want to select. That's the analogy, the best analogy I can tell you for, for the ways, but physician has also is trust with one or several medications.

Just like you have a trust to one direction or another to to go to your best restaurant.

David E. Williams: Sounds good. All right. So the danger of analogies is taking them too far, but I'd probably rather go to a restaurant where you live than where I live. So maybe that would be a good thing to put that together next time.

Let's talk about voice. So you mentioned AI and voice as both being ways to get over some of these challenges of all the clicks. What, separate from AI, what makes voice a good technology? Why have physicians adopted it?

Emmanuel Bilbault: I would say that That shortcut, the, the complex interfaces where you have many options and you've seen in the, in the last [00:11:00] conferences even outside of a healthcare that people think about, like switching from one software to just like something you, you would hold or keep in your pockets.

That's somehow the same way, like EHR publishers, EHRs being like electronic health records are thinking about the future. So voice is everywhere. Voice Enable you to to control your, your software. But for the prescription side, because that's why I'm interested in I'm a pharmacist.

I may be like too much focused on on medication that will enable just to dictate the prescription, your way of think about it, and always have this assistance of an AI. telling you that you are prescribing the right drug, the right dosage, or there may be options to optimize a prescription, or there is a huge risk in what you, you've said, and and, and you should absolutely correct it.

So The way it works in terms of a user journey that you prescribe, you get the control based on your patient profile, you validate, and you send it to your software or you correct and you send it to the software. But there is always this this step where physicians can correct or validate the prescription.

The prescription, sorry for that, before he sent it to to the electronic health record. Yeah.

David E. Williams: So if I think about, so you have this new collaboration with Microsoft and it's their Nuance division. Nuance started off as really a transcription company, you know, they used to take in the dictation when it was analog and then they would, you know, type it up and they would provide it back and then they evolved and then it was eventually acquired by Microsoft and they do focus, I mean, they're integrated into you know, they're integrated with this co-pilot kind of an approach.

So they're using AI in the first place. So why, why did Nuance need to work with you? Why does Microsoft need to work with you as opposed to just including within Dragon your functionality?

Emmanuel Bilbault: Yeah, the, the prescription part is probably the more, the most complex step in In the, yeah, I would say that the use of your software you will use your software indeed to to write down the report of your patient physicians conversation.

You will click in the software to select the diagnosis you met, but prescribing is a long process, as we said before, but also the most difficult step in time of, adapting and making the right choice. So here adding puzzles to their portfolio somehow of solution, I like partners Microsoft is able to cover the, whole patient journey in a physician office from the first discussion with the physicians.

Also the control of the software we call pilots and at the end, the prescription, which is always the last step of a, of a consultation. So it's, it's a way for them to cover the whole patient journey. I think that the first reason, and this very clinical part of the, of the patient journey and, and the physician interaction with the software.

It's very hard for them to cover just like Microsoft competitors, because you've seen that are many, many competitors now on this copilot technology. And what you need or so is is a drug database to pair from that, because the way we manage this AI system for prescribing is to have a worldwide universal database covering all medication from all countries, covering all languages making it possible to correct the prescription that anyone in the world will will make.

So. That's the way or so Microsoft think about the future, being able to scale every solution, the partner with and I think we are the best position in the worldwide to to enable that. And that's why we decided to collaborate with us. It's more for the It's not only for the use case we provide, but also the possibility to scale the solution everywhere.

And I will tell you one funny story. I had last week a conversation with a Swiss EHR publisher asking if we can cover the three languages in Swiss. Which are French Italian, and Swiss German. And Swiss German is a very interesting language because it's written exactly like German, but they pronounce it totally in a totally different way.

So we can manage a German part in our software, but we are not able to cover the Swiss German way of pronouncing the prescription. So also from our side, Microsoft. Was the best partner to make it possible to use poses everywhere.

David E. Williams: Oh, that sounds good. Well, so, you know, Microsoft, obviously a global company and you're talking about the ability to scale and what the needs are.

And at the same time healthcare markets and, you know, the healthcare system is pretty different from country to country. Certainly the U. S. versus Europe broadly, but even France versus Germany. You have Switzerland or other countries in Europe, and then Asia are different. What do you find is the same?

Is it very universal what you're doing, or are there certain things that need to change, not just based on like, you know, Swiss German, but based on the actual structure of the healthcare system or the financing of it?

Emmanuel Bilbault: Yeah, the, the clinical part is the same everywhere. You will decide to prescribe one drug or another.

You will Diagnostic pathology the same way in the US in Europe and in Asia. So the drug database we've developed for instance is not like populated with text fields, but only international codings, which are the same everywhere in the world. So the clinical information we have is available and and compatible with every country.

The big difference is of course, the patient journey in the system and also the price sensibility or reimbursement sensibility. And somehow Coming back to the Waze analogy, you in Waze, sometimes you also have different options based on the traffic. So here, I think for the U.S. for instance, you will have also to select which one drug or another based on the possibility for the patient to be reimbursed or the possibility to afford this prescription. So it's like, an additional type of data that you have to process in your in your prescribing assistance taking into account a patient insurance coverage, for instance.

But at the end the act of prescribing is really similar. From one country to another, if you just take into account this economical points as well.

David E. Williams: You mentioned your background as a pharmacist, and one of the things pharmacists are typically concerned about is medication reconciliation, which is a big challenge.

And I'm wondering if you could just describe from your perspective, you know, what are the issues with medication reconciliation? And can AI be helpful there, whether or not Pozos is doing it?

Emmanuel Bilbault: I'm not sure it's a, it's a, it's a very difficult different process to, to the one we described before for prescription.

The issue, the main issue actually, that physicians or pharmacists are partially blind. Because they are not fully aware of what the other physicians have prescribed or what the other pharmacist has has given to the, to the patients. And so that the decision they make in terms of prescription or giving a medication to a patient could be wrong just because of his blindness.

I'm not sure that the AI is the right solution for that. It's more about like sharing information between all health system or. Physician hospitals and together with pharmacy. So once we will have a common electronic cash record shared with every clinicians, I think this reconciliation problem won't stay.

So it's a question of sharing information to have the whole picture and then the right prescribing assistance to take into account this wider Information that will be available.

David E. Williams: Emmanuel, my final question for you is about the vision that you have for the future. You've obviously just had this very exciting announcement of a collaboration with Microsoft, the potential to scale.

Where do you see Pozos in five years?

Emmanuel Bilbault: Wait, yeah, I announced this Microsoft collaboration, but I didn't talked about the, the collaboration we have with EHR publishers worldwide. There are already 18 EHR publishers working with Pozos who embedded the solution into their interfaces. So the vision we have in five years to, to have not only the.

The only direct database that is really like universal, as I said before, this capacity to prescribe with puzzles with a voice or with your, still with your keyboard, with a suggestion to automatically populate your prescription. But really we want to be there, the central point, like enabling not only ESR publishers, but also.

Every medical app, every medical software to scale because using puzzle database, using our APIs is a great way to have the same software everywhere in the world. And that's the huge problem. Currently software are country based because they are based on a country-specific database where software are basing their technical stack on a, on a solution that can scale by itself.

Then they can have a real SAS model and Replicating their solution everywhere in the world. So we think about a more intentional point more intentional software and that will be available for every physician in the world. That's not only Pozos, it's Pozos together with a partnership, but we, we, we provide the great technical product for that.

David E. Williams: Well, great. That's it for yet another episode of Care Talk. We've been speaking today about AI and drug prescribing. My guest has been Emmanuel Bilbeau, co-founder of Pozos. Emmanuel, thank you for joining me.

Emmanuel Bilbault: Thank you, David.

David E. Williams: if you like what you heard or you didn't, please make sure to subscribe on your favorite channel.

Watch the full episode on YouTube:



CareTalk is the only healthcare podcast that tells it like it is. Join hosts John Driscoll (Senior Advisor, Walgreens Health) and David Williams (President, Health Business Group) as they provide an incisive, no B.S. view of the US healthcare industry.



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