Power of AI in Achieving Patient- Centric Communication

Transcript

[Greg]

Welcome to the digital health transformers podcast, where we dive into groundbreaking innovation, shaping the future of healthcare.

I’m your host, Greg and today we’re exploring the power of artificial intelligence and achieving patient centric communication. The healthcare industry is undergoing a transformation with AI emerging as a key player in improving the way providers engage with patients.

AI can enhance communication, streamline processes, and ultimately improve patient outcomes. Brynn Lord or Dr. Lord, um, is the founder and CEO of empathic AI. And she’s joining us today to share her insights on these exciting developments.

So, um, empathic AI is at the forefront of harnessing AI to optimize patient communication, bridging the gap between technology and compassionate care, which I love that. That’s awesome. Brynn, welcome to the show.

[Dr. Grin] 

Thank you. Thank you for having me.

[Greg]

Uh, it’s our pleasure. So yeah, so great. It’s definitely a pleasure to have you on the podcast.

Can you start by telling us a little bit about your background and what led you to empathic AI and how you came to focus on AI and healthcare in general?

[Dr. Grin]

Yeah. Um, so I’m a clinical psychologist by training and, um, I worked actually at Harborview medical center, which is managed by university of Washington. And we were just talking about this, uh, straight out of college.

Um, and was actually part of counseling patients after they came into the emergency department after a drunk driving accident or some accident that involved substances and we were, I was actually a research psychologist at the time, a research counselor where we were part of a trial that was looking at the effectiveness of different styles of communication, uh, to patients in that environment. And one of the things we found was that, uh, when we compare to treatment as usual, when providers show more empathy and compassion for their patients, um, by doing very specific things in listening, um, that the outcomes for those patients are way better than treatment as usual. And in this case, in this trial, they actually had a significant remission rate, um, from alcohol abuse.

And I think it was like something like 33% remained completely absent after, uh, just a brief 15 minute counseling session with this style of empathy coaching. And, uh, there was also a massive drop in, um, readmission to the hospital for future accidents. So the treatment as usual was telling people what to do.

Um, you know, saying here’s some resources for you. And again, when we listened to the patient, allowed them to make a choice about whether they wanted to change in their life, um, didn’t show judgment or push them in a particular direction and really weighed, you know, their ambivalence, uh, showed compassion, uh, major improvement. So that was like kind of my first experience in learning about patient communication and how, what doctors say and do can have tremendous impact, uh, even in brief interactions.

Um, and from then on, I went into coaching that style of communication, um, and also really promoting, uh, empathy as a form of accurate listening, uh, throughout my career. And that led me eventually into AI because, uh, we were part of a drug coaching study where, um, a national study where we actually, uh, took providers and had them record their interactions with patients over the And then, um, this is again with drug counseling. And then we would listen to that and then say, here’s your feedback.

Here’s ways that you can improve. Um, you know, maybe you should do more open-ended questions in the beginning of your call, things like that. Um, and we would, uh, give that feedback to the providers.

They would improve. And they kind of had this like, almost like coaching a sport, um, uh, type of feedback where we’d listen to the recording, give them feedback, listen to the recording, give them feedback. Um, that was great.

It had a huge impact on their behaviors, way more than a two-day workshop in terms of like trying to shift these like, um, kind of entrenched styles of communication. Um, so that was the first time we thought maybe this is something that AI could do. Maybe we could be these recordings into a system.

And instead of having this long delay where psychologists, you know, listen to the recording four weeks later, they review the tape with this provider. Is it possible that all those annotations and times that we marked the good and bad things that happen in conversations, could that be fed into a machine learning algorithm and could it output, you know, this is how you need to improve your communication. And so actually from 2008 to 2017, we worked on building the first, um, first speech signal processing pipeline for medical conversations to automate feedback, um, at university of Washington in collaboration with some other universities.

And, uh, yeah, that’s how I got into this intersection between psychology, patient communication, natural language processing, ML, things like that.

[Greg]

I mean, I think it’s amazing because it seems like such an obvious thing, right? And what I would ask you personally was, you know, this was an obvious bottleneck and clearly, you know, AI stepping in empathic is, is really sounds like at the forefront of this kind of empathy based care. Um, what was the, in your opinion, I mean, obviously you’re learning, what was the pushback?

Like, is there a reason why doctors weren’t just doing this naturally? Is it because they were, you know, too much caseloads or just personality? What was the kind of overarching reason there?

Yeah.

[Dr. Grin]

It’s funny because I’ve been quoted on other podcasts saying things like, Oh, humans aren’t very good at empathy or like, but it is true that, um, what we found in psychology, um, is that there are specific things around forms of listening, specific skills that you can use that aren’t really intuitive. And we’re not really trained to do that in school or in medical school. Um, things like letting the other person talk more, just as a simple example, doctors are trained to come in and run through a list of diagnostic kind of closed questions, or at least when I was training, that was the case.

Now I think it’s worse because there’s actually a managed care component where you’re looking at a computer, not even looking at the person, but at the time it was that people were coming in kind of doing rule out, like question, question, question, like trying to figure out and test a hypothesis rather than doing one open-ended question, like, and sitting back and listening and listening, and then doing small reflective kind of pings to direct that, that was at the time when I was, um, you know, we were looking at these styles of communication, that was kind of revolutionary and it was definitely not trained. It was all about the doctor is the expert.

The doctor comes in and lectures you, the doctor gives you information. That’s why you you’re there. You’re not the expert on yourself.

They’re the expert on you. Um, and their job is to show like, and to rule drill down in these specific ways. Um, now there’s been a huge transformation in that.

So, um, I think doctors do spend more time and have more training in how to listen, but to your point, there is a lot of time pressure. There is a lot of like other, um, you know, procedure based billing, like they’re not getting money for listening to people, so it’s, it’s a hard, the incentives are a little bit misaligned to teach people to take time and understand and that that leads to better clinical care. Um, I think sometimes folks are wanting to be fast, efficient and demonstrate their knowledge.

So, um, again, we’ve seen in substance abuse counseling, there’s been a major shift and some forms of trauma care. We have seen that as well, that this style of listening and empathy has grown, but yeah, there’s a lot of, uh, uh, pressures on doctors to do things, um, fast differently. Um, it’s hard to, to make that space.

[Greg]

That’s obviously a perfect place for empathic to come in and do that work. And it really helps everyone out and everybody’s for the better. So, uh, speaking of that, um, doctor AI has made its way into various aspects of healthcare and in your view, how can AI truly transform patient communication and what’s it, what makes empathics AI, uh, approach unique, would you say?

[Dr. Grin]

Yeah. So what we do is we take in a recording of a doctor talking to a patient and we look at everything that’s happening in that conversation from, um, what is occurring, you know, are they consenting that patient or are they doing some sort of, um, diagnosis or checklist as well as how is that conversation going? Is there empathy and rapport being built?

One of our measures is actually this unconscious measure of, um, synchrony between two people. And are they starting to use the same words as each other? Are they using the same tone, things like that, unconscious markers of the conversation going well.

Um, we objectively rate that in the same way across, um, you know, every recording that we give, so unlike a human who might have biases or come into the conversation with, um, you know, a certain presumption, like they don’t like this doctor, it’s a difficult doctor. Our AI doesn’t know anyone and has built up of, you know, uh, thousands of expert providers, uh, giving input on hundreds of thousands of recordings. And, uh, so, and we have different languages, different cultures, different backgrounds.

So the AI is synthesizing all of that. And it listens differently than a given human because it can hear these minor vocal things, um, the synchronization that I talked about, as well as pulling the insights from not like one human brain, but thousands of human brains when it’s doing this recording. So what we’ve found is that it’s less bias when it listens.

Um, and then it gives that feedback to the providers directly and gives them coaching, like here are your strengths. Here are areas to improve. Um, here’s an alert, like notice there was suicidal ideation and this, like this needs to be escalated, things like that.

So it kind of has an oversight quality as well as a training quality, uh, that can, you know, happen at scale. And so providers can use this themselves to improve in their own communication, um, without necessarily having a supervisor, uh, listen to every recording they’ve ever done. Um, and it can be difficult for supervisors to, uh, you know, they don’t have a lot of time.

They may hear impressions, like a patient complaint may escalate and then they may have to go resolve that. But this is a much more, um, you know, almost extending the learning into the clinic. They can continue to improve and grow, um, just by reviewing those insights themselves, or if they want to do that with a supervisor.

[Greg]

I mean, you almost, I mean, you didn’t almost, you kind of answered my next question. My next question was around communication, but it’s okay. I mean, I, I just, I love that because, you know, my next question essentially was traditional communication methods and healthcare can sometimes feel impersonal or disconnected.

And then I was going to go into how does AI help create more patient-centric experiences, you know, particularly improving personalized care. And I think you say that basically it’s, it’s that training that empathic kind of helps, you know, providers kind of see this is the way you’ve been doing. And this is what we noticed so that they can, is that essentially what we’re saying?

[Dr. Grin]

Yeah. And then they can see their trends evolve over time. Uh, we do have, um, some pilots, uh, with hospitals as well, where we’re trying to track that with some more objective measures like length of stay and patient satisfaction.

Um, and some other fidelity metrics that are used in surgical settings, um, around accuracy and communication. Um, so there, there are ways to, to correlate this, not just to improvements in style that we think are good, like, Oh, you should be, you know, a certain way. It’s like, are the people that are behaving in certain ways actually having better patient outcomes?

Um, one other thing that we did experiment with was a real time feedback, uh, mechanism. So in telehealth, we have a bot that can come into the, uh, calls and give nudges around like, Oh, Hey, you’re asking too many closed questions in a row and they haven’t had a chance to respond, or you just did a confrontation there. You’re like, um, you know, giving them direct advice in a way that feels confrontative, you know, actually pinging the provider.

Um, and we found that that wasn’t very effective in behavior shaping because people would be distracted by the pings. Um, but that was like kind of our, our ultimate, you know, implementation. And now this isn’t in our company, but I’ve seen it in the wild things like, you know, meta has the Ray-Ban glasses.

And I think, um, Apple AirPods are coming out with real time translation. Like there’s no reason that we can’t have, you know, wearables or assists that could vibrate or create an alert or things where if things are going very wrong in a conversation and there’s misalignment and conflict that that can be detected and prevented early on. Um, we find for our technology, the best application is like a retrospective review or in, in chat, because there’s a little bit more of a lag time.

You can actually, Oh, let me read that. Let me do that. But I’m excited for a future where, um, the technology is here presently.

We have it today. So it’s just a matter of like, how do we implement this and disseminate it, that people’s real-time communication that they can learn that on the fly. They don’t need to go to medical school.

They don’t need to go to a workshop. They don’t need a review. There can be real-time nudges to make things more empathic.

So again, we see that right now, um, and implementations in the chat based function with agent assist. Um, and, uh, there is a company in, in England, um, ISO. That they used to do this for, for therapy.

They had real-time kind of nudges and coaching. So there are models out there and, and I’m excited for that with AI is essentially like the democratization of, um, access to experts in communication where every single person can have an expert in their pocket, learning that, you know, and that’s, that’s part of the daily kind of communication that’s happening in medical spaces.

[Greg]

Oh, that’d be beautiful. Um, speaking of those challenges, um, are those, or what do you feel like are the biggest challenges health care organizations are facing when trying to engage patients effectively? Like, and how does your platform empathic address those challenges?

[Dr. Grin]

Yeah. Um, that’s a big question. I think, uh, there are a lot of challenges around patient engagement.

One thing I just actually had a conversation about this today. I think digital health and, um, applications like app based, um, you know, interventions we’re supposed to solve some of this problem. Like that was kind of the vision of digital mental health is that like we have a provider shortage or patients aren’t engaged, they can’t make it to the clinic.

There are so many barriers, like let’s just give it to them in their pocket, like at home, um, on telehealth and, you know, post COVID telehealth, you know, massively increased access to care and reduced barriers and adherence. But I still, I don’t, I’ve also seen a lot of digital health companies kind of revert to more quote unquote, traditional offerings of having in-person care and coaching and like moving away from based dissemination. Um, yeah, I mean, if you look at some of the larger, um, first movers in the digital, uh, mental health space, um, support space, uh, in like coaching, let’s say calm and headspace, you know, originally a lot of their offerings were online.

Well, actually originally they had the kind of a hybrid and then they went fully online to like, use this module to go to sleep. Now they’re, they’re in coaching again. And, um, you know, doing this augmentation of use the app, but also you’ll meet with a coach.

And so we’ve seen kind of this back and forth where a lot of, uh, SAS based, um, digital health has added a service component because I think people still want like a human, uh, touch and not necessarily from an empathy standpoint, but also just from an accountability standpoint, you know, someone waiting for you at an appointment that cares about you and that’s going to talk to you and wants to work on your mental health or your, um, follow-up on your medication, whatever it is in terms of patient engagement, having a person in a coach, uh, I think has shown to have more effectiveness than like a reminder on your phone that you’re just going to turn off, like really relies on you and your internal motivation to be accessing things in digital spaces. So, um, so I could go on a tangent about that, but what we’re trying to do at Empathic is more around those human moments. Like how do we make those more effective?

So we can certainly coach apps to have more empathic communication. Um, but how can we help these humans that are in telehealth or are, you know, reaching out in rural areas and have huge patient caseloads, how can we make their communication effective and, um, you know, again, improve patient outcomes and doing this type of coaching can help with that. Um, so, uh, barriers to medication adherence, things like that really do pull from a lot of the same four elements that we focus on in Empathic, you know, listening, reflective listening, building rapport and engagement as a primary piece.

Um, so yeah, it’s a very complicated question to answer, but I, I think that’s where a lot of the future.

[Greg]

What you’re saying is, yeah, sorry, I didn’t drop, but yeah, what you’re saying is so key. Like it’s always, that’s always going to be a piece almost is that thinking that that was going to go away was probably part of the problem in doing what you’re doing in Empathic is what really makes more sense on the healthcare kind of support side, right? It’s just helping those humans do a better job, taking a little bit of the load off of them so they can continue doing that good work.

So you’re, yeah, you’re actually right. That piece is just never going to go away. And how do we make it better?

And, and, and this is definitely one of those ways, I think if I’m, if I’m reading what you’re saying correctly. But yeah, I, yeah, I think you’re exactly right. So what are the key benefits of AI is the ability to automate tasks and processes, but how does it ensure that patient communication remains empathetic and personalized, right?

[Dr. Grin]

Again, I think it’s about taking expert knowledge and disseminating it at scale. So if we are experts in communication that know how to coach someone into building trust and rapport by improving their listening skills, doing more reflections, more open-ended questions, noticing when people are doing well and reinforcing that there’s like so many behavioral patterns that we can train providers to do, but I’m only one person. So if I can use AI, again, like in a system like empathic, where it can take a recording and instantaneously give feedback to that provider on how to improve, that really allows the experts in communication to reach a lot of people very quickly.

So I think that’s what it’s all about is this economy of scale and taking improvements in that. And then what’s great with AI too is that when you have human in the loop, when you have feedback from the providers saying, well, this worked really well for this patient, or I noticed when I did this skill, this improved, they can also start to train the AI system and personalize it to their style or to their patient population. It may not be that there’s this one way of listening that works the best.

And the AI, especially again, if there’s a human rater in there that’s saying like these patients are doing better and the provider that had this style is better matched to these patients, it can kind of personalize to each individual interaction and learn from that interaction over time. I think provider matching is a big kind of overlooked factor in a lot of the ways that traditional teaching has gone, where it’s just like do this one thing to help your patients when in reality, it may be that the style of one doctor and the style of one patient should be matched to each other, and it could be that that style is hyper-directive and it isn’t open-ended questions. It’s that that person wants to hear it direct, like they want confrontation, like that’s what they want.

And so it may not be this putting everyone into one style. It may be that like particular skills work better and those outcomes are better. And the AI can adapt as long as it’s listening and getting that feedback around, like, actually, we want to select for this skill.

And we find that with our customers. Not every customer wants to just have empathy in a particular way. They say, like, these are the things we notice and these are the things we want to coach our providers in.

This is our checklist for what we expect our doctors to do. We don’t care so much about these and this, like we want that. So they tailor it to their own style, but ultimately it’ll be the connection between the patient and that feedback loop around, well, what does that actually make change?

So AI and I’d say software makes this data more accessible in a way where people can get feedback and adapt their style. And then it can also serve as a great record so that, yeah, if you are a clinician that’s having outstanding outcomes, people can kind of say, OK, well, what’s going on in those relations, like, let’s take a look and see what’s happening over time there rather than it just being kind of like hearsay and, you know, well, we like this doctor. We don’t like this doctor.

Well, why? Like what’s going on?

[Greg]

Awesome. Let me say it was just for the greater good. But yes, like you said, it’s all, you know, the the core of it is it’s about active listening and making sure that you’re applying those.

And it just seems like that’s just what we’re talking about throughout the entire process. Pretty awesome. So would you be able to share, Grenn, an example of how Empathic has helped a health care provider enhance patient engagement through your solutions or satisfaction?

[Dr. Grin]

Yeah. Yeah, I think one of the newer areas that we’re well, so we work in three different areas right now. So one, we work in life sciences, which is actually in clinical trials where we’re looking at communication between doctors and patients or therapists and patients in that setting.

One of the ways we’ve made an impact there isn’t so much on the engagement end, but on the fidelity monitoring end where we’re looking to see, are those therapists and doctors doing what they’re supposed to be doing? And an impact we’ve had there is actually we’ve been able to detect risk and misconduct that’s happening in those large scale trials. So this is a little bit different than like, how do we help people be more empathic?

It’s more like how do we prevent things from going really wrong?

[Greg]

Oh, yeah. I hadn’t considered that. That’s that’s wild.

[Dr. Grin]

Yeah. So that’s one area. Another area we’re looking at is in the coaching space in medical clinics where they are using techniques like motivational interviewing to help with decision making.

Around like, should I have this procedure? Yes or no. Or we’re also working in neurology where the doctors may be talking to patients after a brain or spinal surgery and they actually don’t have a lot of memory for what’s happening in those visits.

So just having the recording itself is useful there for the patient, you know, no matter what, but then also for the provider to kind of get feedback at the same time on like how they’re speaking to the patient and improving their clarity in there. So these are, again, like very concrete examples of like not just how to be empathic, but like, how do you communicate when you have, you know, certain disabilities or problems after a clinic or very severe mental health challenges? How can we give feedback to that provider to to do that well?

At scale. So those are just a couple of the examples. We also have some case studies on our website from, again, like the clinical trial space of times where our AI was seven times more accurate than a human in detecting some of these issues around suicidal ideation where it can be missed by a provider.

So I’m not trying to say that AI is better than humans. It’s more of that having an AI assist can help when humans are fatigued or doing things for long periods of time or have a lot on their plate. And, you know, that is unfortunately the case with a lot of doctors.

So we’re in that kind of crisis setting and then we’re more in the like clinic setting for helping people on a performance and to really embody the spirit of empathy, motivational interviewing, some of these interventions that they want to be implementing to help their patients.

[Greg]

Yeah, I think it’s that’s clearly what you’re saying, I mean, I don’t you know, even though we’re talking about a lot, it’s not even about a I think, you know what I mean? It’s just about how do we use this thing? And it’s really about the person and an application and and finding a way to really just make sure we’re looking at everything rather than just a single thing.

So this is awesome. And I think empathic is obviously showing us that. Wow.

So, Grant, health care. Next question. Health care providers often struggle with administrative over.

Manual communication, how does empathic platform help ease these burdens and allow providers to focus more on patient care?

[Dr. Grin]

Again, I think there’s been like a boom in AI in the productivity space, and I think that’s been really helpful for doctors. I’ve seen this mostly with tools like ambient note taking where hoarding happening. And so it’s removing the burden of implementing the note into the EHR or, you know, sending an order.

We see it also in medical imaging where, again, there’s AI assist in diagnostics when there’s lots of, you know. Let’s say radiology, like things to review and then the AI is like, hey, look here. So, in general, I see AI helping to remove some of these administrative burdens. On Empathic’s end, what we’re trying to do is not just improve workflow and administrative burdens, but actually add in some of the parts of health care training and support that were removed during this transition to managed care.

So, again, most doctors do not get training and supervision and feedback once they leave medical school in any meaningful way. So we talked to a group of surgeons where they have a kind of a type of a grand round situation where 12 surgeons will come and watch us, do their surgery and get feedback. And that can happen maybe once a year after they’ve reached the professional stage.

So, in the therapy setting, we also we have this joke, we call it the magic door, where once the door is closed in the therapy session, everything must be good that’s going on inside. What’s happening? And it’s like, that’s not true.

But like, therapy doesn’t have a lot of oversight compared to other positions, at least with a surgeon, there’s seven other people in the room. So there are these certain professions where the training and feedback elements have been removed from care. And we’re trying to add that back in a way that is unburdensome.

So, no, you don’t need 12 people coming in and watching your session and giving you feedback. All you need to do is go online, review it, see like, OK, here are my three things I need to address. From the supervisor’s end, I don’t need to go watch 50 different clinicians in my clinical trial or in my clinic.

I can see this provider’s struggling because the A.I. is telling me, OK, in the last 10 sessions, they’ve had all of these problems. That can go missed for years or not at all because supervisors just aren’t able to be everywhere at any one time. So this kind of oversight training, that’s what we’re trying to help with and really remove that burden from doctors that are having to oversee things.

Again, one of our main use cases is in augmentation and medical monitoring and clinical trials. And those are recordings, sometimes 50 hours of recording per patient that have to be reviewed by a doctor to see what’s going on in that setting. It’s just not something that human doctors should be doing right now.

They have enough like we have enough things for them to be doing and provider shortage issues that medical monitors and doctors should be spending their time in patient care and not reviewing these long recordings. So this is another area where A.I. can be coming in, taking off that burden and targeting the approach. Like, look at this section of the video.

Look at this section of the recording. Pay attention to these supervisees, stuff like that.

[Greg]

Wow, that’s awesome. I mean, because that’s really just I mean, obviously, within patient care, it’s super huge, but you can really look at this from any industry. But, yeah, that ongoing kind of just reminders and just making sure anybody can use that at any time and even doctors.

So this is that’s awesome. Wow. Thanks for that.

Let’s talk about the future of A.I. and health care communication as A.I. continues to evolve. What future trends do you see emerging in the realm of patient communication and how is empathic positioning itself to stay ahead of these trends?

[Dr. Grin]

Yeah, there are a number of companies that are specifically focused on helping patients to stay engaged in their care, like in primary care or again in clinical trials where the A.I. is actually replacing a human that would be calling to do that engagement or doing a med check, following up and things like that. Again, that’s not so much our focus at Empathic because we’re really focused on helping humans to make better connections in the few moments that they have together. But I do want to say that that is like just a major area is kind of the A.I. agent, A.I. chat bot implementation in health care. You’re seeing it everywhere. You know, you go to a website, you call someone like there’s increasing automation and that is getting smarter and smarter and better at adapting to different patients and their needs and being more responsive because no longer is it a decision tree. It’s a generative A.I. model and L.L.M. responding to you. So it can pull from so much context and materials to like really engage the patient and give patients answers that they need in a way that just didn’t exist before. So for us, again, with patient communication, we’re trying to keep the human and A.I. that actually was our motto for a long time and say, yes, all this automation is happening. A.I. is happening. It’s making things more efficient. But in the design of those tools or in the design of where humans are engaged in the conversation, let’s make that connection meaningful and train those bots or humans to have the best connection possible. By giving them feedback on what is working and not working in their communication.

So for us, it’s more about quality and scaling quality than it is about removing humans and making things faster. We know that doesn’t work for patients. Like how many phone lines have you been on waiting with your payer or trying to get into a clinic where it’s like that’s supposed to make things easier and all you want to do is talk to a human.

So it’s like we can do this form of automation and moving to A.I., I think, in a better way. But it is going to have to it takes companies like Empathic that understand good communication and use that expert domain expertise into all of these A.I. systems.

[Greg]

No, absolutely. I mean, you may have already answered this, but yeah, I completely agree. It’s you got to keep the human in there for just a number of reasons, but it just it’s going to give you the most reliable results ultimately.

But, Gwyn, what advancements, if any, or new capabilities are you most excited about in the next few years for patient centric communication using A.I.? I think tailoring to different cultures and languages is just going to be massive.

[Dr. Grin]

So when I used to work at Harborview in the clinic, I used to work in the ICU there. And when we needed to call a translator and like there wasn’t a translator available, we had this like phone translation system where we would say something in the phone and hand it to the patient.

[Greg]

I remember that.

[Dr. Grin]

Yeah. Okay. So, you know what I’m talking about?

Oh, my gosh. And like, how many times would you just be like, oh, I’m going to find the nurse that speaks that language? Because like, this is like so painful for everyone to go through and to like sit there.

That stuff is like in the past again, like Apple’s coming out with and I’m sure other companies are coming out with this like real time translation services. Not only will we be able to translate languages, but we will be able to adapt culturally to different styles. So technology like mine, again, when you have that human in the loop, it’s going to be coaching you on not only here’s the language to speak, but this is how to speak to this person.

This is resonating.

[Greg]

That’s awesome.

[Dr. Grin]

You know, that’s kind of like the sci-fi future that is very close.

[Greg]

The Star Trekification of healthcare.

[Dr. Grin]

It’s here. And it’s all about how do we get healthcare systems to adopt this technologies in ways that make sense? How do we align incentives?

Like, who’s going to pay for that? Like, these are the questions. I’m, you know, in the healthcare space, but healthcare startups are, they can be a heavy sell when people say, well, the way that we’re going to pay for this is through the payer to reimburse.

It’s like, they’re not going to do that. So like, innovation has to happen in a certain way, but it’s definitely here. And I think barriers to disseminating that technology and adoption in that technology are more where more innovation needs to occur.

Like, we have the tech, we have the problem, like, how are you going to connect the two? But that’s the thing I’m the most excited about is that, like, these very painful things of the past, you know, that really disadvantaged certain groups and patients. It’s like, that doesn’t need to exist anymore.

Like, everyone should be able to get high quality care. And at a minimum, we should be using AI systems to oversee that. There’s another startup in Seattle.

Oh, my gosh, I’m going to blink on the name. It’s called, like, Health X or something. I’ll have to come back with the name.

But all he does is ingest hospital and clinic records at scale to see if there are discrepancies in the way care was provided between different ethnic groups. And he can immediately in real time say, whatever is happening on this unit, or whatever is disadvantaging this group, like, they’re getting slower response times, they’re getting less pain medication or whatever. That’s a dynamic real flow of information.

And no, that’s not like AI, like LLMs, but that is using data at scale to do real time feedback in a way that wasn’t possible, you know, in the last five years. So these are the things I’m the most excited about is this kind of like, you know, AI in the loop to help reduce, you know, inequities and give everyone the best possible care.

[Greg]

This is awesome. And I think, I mean, I’ve learned so much today. I think what you’re doing at Empathic is amazing.

It also helps me just understand, like, how much more we can do. You know what I mean? Like, even in some of the solutions you were providing today, there are layers and layers on top of that, that you could keep going on.

And you guys are really positioned in the right spot. And I just thank you so much, Grin, for joining and sharing such valuable insights today on the future of AI and patient communication. And again, it’s clear that AI has the potential to significantly improve the way patients and providers interact.

And Empathic is certainly making that happen, you know, and making healthcare more efficient and personalized. So I just want to again, thank you. And to our listeners, thank you so much for tuning in.

Stay with us for more inspiring conversations with the innovators transforming healthcare like Dr. Grin Lord. And we’ll see you next time on the Digital Health Transformers. Thank you so much.

And that’s it. I appreciate it. That was so awesome, Grin.

I love what you guys are doing at Empathic. Especially for someone, I’m down here in South Florida. You know, such a diverse community, like you said, we have so many hospital systems.

And I just see, you know, like my sisters in nursing, I have a lot of family in nursing, and I know how inundated everybody is. And again, that’s, this is just wow, it’s just great to see that you guys are out there and so many platforms like you. So I really appreciate this time.

[Dr. Grin]

Oh, cool. No, I appreciate hearing your story. And I hope I can’t wait till our technology is more ubiquitous.

And like all these innovations start to really trickle down. I think we’re right at this transition.

[Greg]

It’s coming sooner than we think. Yeah.

[Dr. Grin]

Yeah. So, thank you.

[Greg]

All right. Well, thank you. And Christina, thank you so much for your help.

You guys have a great day. It was such a pleasure. We’ll talk to you soon.

Take care. Bye. You too.