Latest / Becker’s Healthcare Podcast / AI Driven Solutions for Scheduling, Prior Authorization and Practice Efficiency with Xavier de Gracia

Transcript

AI Driven Solutions for Scheduling, Prior Authorization and Practice Efficiency with Xavier de Gracia · Becker’s Healthcare Podcast

• 0:02
  - The strategic agenda facing hospital and health system
• 0:04
  - leaders right now is not a short list.
• 0:07
  - Growth in a consolidating market, sustainable margins in
• 0:09
  - a reimbursement environment that rarely moves in your
• 0:12
  - favor,
• 0:12
  - a tech investment cycle that demands both urgency
• 0:15
  - and discipline,
• 0:16
  - and a workforce story that is still being
• 0:19
  - written.
• 0:20
  - Eker's fourteenth annual CEO CFO roundtable brings together
• 0:23
  - more than 1,500 executive level attendees and nearly
• 0:27
  - 400 speakers across four days of sessions that
• 0:30
  - are designed to help c suite leaders benchmark,
• 0:32
  - pressure test, and sharpen their priorities.
• 0:35
  - Not in the abstract, but against the real
• 0:37
  - decisions they'll face heading into the next year.
• 0:39
  - It's the kind of event you leave with
• 0:40
  - a clear sense of where you stand and
• 0:42
  - what comes next.
• 0:44
  - So join us November second through the fifth
• 0:46
  - at the Hyatt Regency in Chicago,
• 0:48
  - and come ready to do the hard thinking.
• 0:50
  - Register on our events page at beckershospitalreview.com
• 0:54
  - by clicking on the events tab in the
• 0:56
  - upper right.
• 0:58
  - This is Scott Becker with the Beckers Health
• 1:00
  - Care Podcast.
• 1:02
  - I'm thrilled to do... Today to be joined
• 1:03
  - by a brilliant founder,
• 1:05
  - the founder of Prosper AI,
• 1:08
  - Javier De Gracia.
• 1:10
  - And Javier, in in addition to being the
• 1:12
  - founder, cofounder,
• 1:14
  - is a Harvard Business School alum as well,
• 1:16
  - a brilliant person.
• 1:18
  - Javier, tell us a little bit about
• 1:21
  - how you got started with this, how you
• 1:23
  - ended up in the health care world.
• 1:25
  - You had done so many other things that
• 1:27
  - are interesting besides the MBA,
• 1:29
  - had been at Handy HQ,
• 1:32
  - which seems quite different than being in Prosper
• 1:35
  - AI in health care, also been in ENGIE
• 1:37
  - services and and in sector leadership.
• 1:40
  - Tell us a bit about yourself and and
• 1:41
  - what Prosper AI does and and how you
• 1:44
  - got started in this.
• 1:45
  - Yeah. Well, first of all, Scott, thank you
• 1:47
  - so much, for having me here today. Very
• 1:49
  - excited for this conversation. So,
• 1:52
  - look, I grew up in a family of
• 1:53
  - physicians. So both my parents are doctors.
• 1:56
  - Back in Spain, they got, private practice. My
• 1:58
  - mother is a cardiologist. My father is a
• 2:00
  - pulmonologist.
• 2:02
  - And,
• 2:03
  - their work was a big part of our
• 2:04
  - life.
• 2:05
  - And so I remember, you know, seeing their
• 2:07
  - dedication to patients, but also their frustration
• 2:10
  - when operational challenges get their patients from the
• 2:14
  - deep treatments they need. And so, you know,
• 2:16
  - after, as you mentioned, I I was,
• 2:19
  - able to, like, come to The States. I
• 2:21
  - went to Harvard. I worked at Bain and
• 2:22
  - Company for several years.
• 2:24
  - And then I were... Had several operational roles
• 2:27
  - in, tech companies in New York.
• 2:29
  - You know, I,
• 2:31
  - thought that AI was gonna have a massive
• 2:33
  - impact
• 2:34
  - in helping practices like my parents, and so
• 2:36
  - decided to leave my job and start Prosper
• 2:39
  - AI.
• 2:40
  - And how anxiety ridden? How exciting was it?
• 2:42
  - What was the movement to starting your own
• 2:45
  - company?
• 2:46
  - Because, obviously, you've had this brilliant career, been
• 2:48
  - at Bain,
• 2:50
  - some great companies, but starting your own thing
• 2:52
  - is a very different thing.
• 2:54
  - What was that like, and and where did
• 2:56
  - the energy come from?
• 2:58
  - What made you decide to do it at
• 3:00
  - the time you did?
• 3:01
  - Yeah. So at the time, you know, I
• 3:04
  - had unofficially been the COO of my parents'
• 3:06
  - practice for several years,
• 3:08
  - and I felt that we were in a
• 3:11
  - turning point on technology with technology,
• 3:14
  - and that we would have the opportunity to
• 3:17
  - have a big impact in practices like my
• 3:19
  - parents. And so,
• 3:21
  - I felt that it was the time if
• 3:24
  - I wanted to have a
• 3:26
  - further impact than I was having with, like,
• 3:28
  - my job at the time to
• 3:31
  - start a company and try to,
• 3:33
  - help
• 3:34
  - at scale. And so
• 3:36
  - it was nine months without a a salary,
• 3:38
  - I would say, until, like, we fundraised and
• 3:39
  - we were able to, like, explain further our
• 3:41
  - vision.
• 3:42
  - And, where we are today is we've built
• 3:44
  - essentially these AI agents that
• 3:47
  - pick up the phone, answer the patient calls.
• 3:49
  - They're able to book with the right doctor,
• 3:51
  - but also
• 3:52
  - we're able
• 3:53
  - to check insurance real time through the clearinghouses.
• 3:57
  - We go into the payer portals,
• 3:59
  - get the right information
• 4:01
  - on coverage,
• 4:02
  - and,
• 4:03
  - calculate the cost estimate for the patients ahead
• 4:05
  - of their visit. And so we are serving
• 4:08
  - over a 100,
• 4:09
  - specialty practices today covering a 150,000
• 4:12
  - providers
• 4:13
  - across 27 specialties.
• 4:15
  - And so I guess, the vision of being
• 4:17
  - able to get there is what really got
• 4:19
  - me, excited to to do all those jobs.
• 4:22
  - And so far, working with a 150
• 4:24
  - different practices you said, I think... What was
• 4:27
  - the most
• 4:28
  - challenging part? Because there... There's multiple different parts
• 4:31
  - in getting started what you got started.
• 4:34
  - There's
• 4:34
  - building
• 4:35
  - whatever the tech is that's gonna work with
• 4:38
  - your customers in the... In in in the
• 4:40
  - engineering side. There's the fundraising,
• 4:42
  - and then there's actually battle testing it, testing
• 4:45
  - it with real customers.
• 4:47
  - Do they really want this? Is something that
• 4:48
  - they really want?
• 4:50
  - What was... How do you how do you
• 4:52
  - sync with... Sequence those? How do you think
• 4:55
  - about those, and am I missing one of
• 4:57
  - the big things?
• 4:58
  - Yeah. No. That's a that's a great question.
• 5:00
  - So the the way that we started is
• 5:02
  - I knew from my parents' practice that, you
• 5:05
  - know, staffing the front desk consistently was a
• 5:08
  - challenge. No?
• 5:09
  - The time that that I started, they had
• 5:12
  - over 20
• 5:13
  - front desk staff members that were picking up
• 5:16
  - their phones to speak with patients, schedule them.
• 5:19
  - And then in Spain, also, they... There's also
• 5:22
  - private insurance. So it works quite similar to
• 5:24
  - here where for every patient, you need to
• 5:26
  - make sure that you check eligibility and that
• 5:29
  - you understand
• 5:30
  - whether the patient has coverage or not and
• 5:32
  - what is the actual cost of that visit.
• 5:34
  - And so I already had the insight from
• 5:37
  - my parents' practice that that was a big
• 5:40
  - issue.
• 5:41
  - And so what we did is we built
• 5:42
  - an initial product,
• 5:44
  - very bare bones, that was an AI agent
• 5:46
  - that spoke spoke on the phone like a
• 5:48
  - real human.
• 5:50
  - And initially, we test it out with,
• 5:54
  - a practice that was friendly that we knew
• 5:56
  - in New York, actually.
• 5:58
  - And from there, we scaled it. And so
• 6:00
  - the first ones were difficult. We now did
• 6:02
  - to, like, picking up the phone and speaking
• 6:05
  - with,
• 6:06
  - patients and also with the insurance companies to
• 6:09
  - check eligibility information.
• 6:11
  - And then we created the product with our...
• 6:13
  - That first customer
• 6:14
  - until we felt that it was ready to
• 6:18
  - continue and, scale it further.
• 6:20
  - And I think you asked also about the
• 6:22
  - fundraising.
• 6:23
  - It's always like a chicken and egg,
• 6:25
  - process. But once we had initial proof points,
• 6:28
  - we were able to, like, go and fundraise
• 6:30
  - and
• 6:31
  - get, funding from from great funds like Emergence
• 6:34
  - Capital, Y Combinator,
• 6:36
  - and Andreessen
• 6:37
  - Horowitz. Fantastic. And a and a great great
• 6:39
  - list of funds. What are you finding the
• 6:41
  - most satisfying now as you work with customers,
• 6:44
  - you work with practices? And talk a little
• 6:46
  - bit more as you work with practices,
• 6:48
  - what do you do for the practices or
• 6:50
  - with the practices? Just give us a little
• 6:52
  - more depth on what Prosper AI does to
• 6:54
  - help practices.
• 6:56
  - Yes. So what we do is we work
• 6:58
  - mainly with large ambulatory specialty groups. So think
• 7:03
  - of dermatology, gastroenterology,
• 7:05
  - ophthalmology,
• 7:07
  - cardiology groups.
• 7:09
  - And what we do is we come in
• 7:11
  - and we handle end to end the previsit
• 7:15
  - process with AI agents. So most of these
• 7:18
  - practices will have a call center that will
• 7:21
  - pick up the phone for any patients. 80%
• 7:24
  - of scheduled visits happen on the phone today.
• 7:28
  - So we pick up the phone. Our AI
• 7:30
  - speaks like a human. It uses, like, a
• 7:32
  - similar technology like a chat, GPT, or cloud.
• 7:36
  - We are connected into the EMR, so ModMed,
• 7:40
  - Athena, ECW,
• 7:41
  - Greenway. We can read the existing schedules of
• 7:44
  - the patients... Of the providers,
• 7:46
  - But also, we capture
• 7:48
  - all the SOPs and the knowledge base that
• 7:52
  - the existing front desk live reps,
• 7:54
  - have. Right? So,
• 7:56
  - any kind of booklet or policies on how
• 7:59
  - to schedule a specific,
• 8:00
  - doctor or appointment type, we capture that even
• 8:03
  - if it's an unstructured way. Right? So,
• 8:05
  - whether, like, a certain provider doesn't want to
• 8:08
  - see,
• 8:10
  - pediatric patients under the rate or if they
• 8:12
  - only take certain capital insurance companies,
• 8:15
  - we get all of that and put it
• 8:16
  - into, like, our AI so it can schedule
• 8:19
  - correctly.
• 8:20
  - And then once the patient is scheduled, we
• 8:22
  - check their eligibility
• 8:24
  - for
• 8:25
  - the
• 8:26
  - for the benefits. So we
• 8:29
  - check,
• 8:30
  - through APIs, like clearing houses,
• 8:32
  - but also we go into the payer portals
• 8:34
  - with AI. So we log in into the
• 8:37
  - payer portal,
• 8:38
  - check whether a patient
• 8:41
  - is covered and the details of that coverage
• 8:44
  - for
• 8:45
  - UnitedHealthcare.
• 8:46
  - And we even do an outbound call to
• 8:49
  - the insurance company
• 8:51
  - on behalf of
• 8:52
  - the provider
• 8:53
  - to get additional details about,
• 8:56
  - coverage of that patient. Many times, around 20%
• 9:00
  - of the times that someone goes to a
• 9:01
  - dermatologist,
• 9:03
  - the benefits team of the practice will need
• 9:05
  - to call the insurance companies
• 9:07
  - to get details on prior auths, whether it's
• 9:10
  - required or not, or whether certain CPT codes
• 9:12
  - are covered or not. And so our AI
• 9:14
  - is able to do all those steps from
• 9:16
  - the first call of the patient to schedule
• 9:19
  - to actually checking the benefits and driving the
• 9:21
  - prior auth.
• 9:23
  - We're able to complete
• 9:24
  - up to 60%
• 9:26
  - of those workflows end to end.
• 9:29
  - And so the impact that we drive for
• 9:31
  - the practices is not only that as they
• 9:35
  - get attrition of their staff, they don't need
• 9:36
  - to hire, but mostly that we're able to
• 9:40
  - consistently pick up the phones, schedule patients, and
• 9:43
  - have the coverage correctly checked.
• 9:45
  - And so we increase the revenues of the
• 9:47
  - practice
• 9:48
  - up to 11%
• 9:50
  - just by being consistent on that workflow and
• 9:53
  - removing
• 9:53
  - part of the preventable denials that happen in
• 9:57
  - that pre visit process.
• 9:59
  - Thank you. And and talk about...
• 10:01
  - What is
• 10:03
  - top of mind right this moment?
• 10:06
  - How much focus on sort of
• 10:09
  - building out the models further, building out the
• 10:11
  - technology further
• 10:12
  - versus adding more clients or more areas?
• 10:15
  - What's the biggest focus over the next six
• 10:18
  - months or so?
• 10:19
  - Yeah. So I would say mainly two things.
• 10:22
  - One is,
• 10:23
  - you know, as I speak with,
• 10:26
  - practice leaders on stomology,
• 10:28
  - cardiology, dermatology,
• 10:30
  - what I'm seeing is that they're all feeling
• 10:32
  - the squeeze coming up to 2027.
• 10:35
  - There's, like, CMS is, removing the 2.5%,
• 10:40
  - rates boost.
• 10:41
  - And, in some specialties, there's cuts
• 10:44
  - on the actual reimbursements, right, up to 9%,
• 10:47
  - for example, in dermatology. So,
• 10:49
  - we're seeing that, like, practices are getting the
• 10:52
  - squeeze in terms of, like, their revenue, and
• 10:55
  - also
• 10:55
  - they are, seeing rising costs on running their
• 10:58
  - practice, where it's,
• 11:00
  - employees,
• 11:01
  - inflation, and, like, increasing cost of, labor as
• 11:04
  - well as
• 11:06
  - just general cost of running their practice. And
• 11:08
  - so what we've been focused on is on
• 11:11
  - telling them and, like, letting them know that
• 11:14
  - now they can regain control
• 11:16
  - on their practice and bring
• 11:18
  - AI to help boost
• 11:20
  - revenue and expand margin.
• 11:22
  - In terms of the,
• 11:25
  - impact that we're seeing, we've just published a
• 11:27
  - report,
• 11:28
  - today that talks about the $108,000,000,000
• 11:32
  - revenue opportunity
• 11:33
  - that these practices have,
• 11:36
  - in order to,
• 11:38
  - expand
• 11:39
  - their
• 11:40
  - revenue through AI.
• 11:42
  - And so when I think about the next
• 11:45
  - six months is about bringing further customers into
• 11:48
  - having that impact, further, practices being able
• 11:52
  - to deploy these AI agents and, you know,
• 11:54
  - avoid the squeeze that they are seeing today.
• 11:58
  - Thank you very, very much. And and when
• 11:59
  - you think about
• 12:01
  - your journey so far in starting the company
• 12:03
  - to where you're at today,
• 12:05
  - First, give me a shout out to your
• 12:07
  - cofounders. Tell me about your cofounders a little
• 12:09
  - bit.
• 12:10
  - Yeah. So I've got one other cofounder.
• 12:13
  - His name is Joseph. He, went to MIT.
• 12:17
  - He's on, like, more technical and engineering side
• 12:19
  - of things. Started his own machine learning company,
• 12:22
  - after school for three years. Worked at
• 12:25
  - a tech company in New York five years
• 12:27
  - after that,
• 12:29
  - leaving,
• 12:30
  - their product teams. And then until we, you
• 12:32
  - know, we met in New York through common
• 12:34
  - friends and decided to start together,
• 12:37
  - the company three years ago.
• 12:39
  - Phenomenal. And and
• 12:41
  - how
• 12:41
  - is the company going so far? Do you
• 12:44
  - ever wake up and and
• 12:46
  - be amazed at the progress that you've made
• 12:48
  - so far? Do you ever wake up and
• 12:50
  - and not not pinch yourself, but be like,
• 12:53
  - how do we get from here to there?
• 12:54
  - Or or how do you think about that?
• 12:56
  - I mean, obviously, you're a very accomplished person
• 12:58
  - to start with. Two physicians as parents. Were
• 13:01
  - they upset with you when you didn't become
• 13:02
  - a physician?
• 13:04
  - And then then how do you view the
• 13:06
  - progress so far? Are you ever blown away
• 13:08
  - by how far you've come so far, or
• 13:10
  - you view it as no, there's so much
• 13:11
  - decline?
• 13:13
  - Yeah. No. That's a that's a great question.
• 13:15
  - Well, the... My parents were happy that I
• 13:17
  - actually was not a physician. I think, like,
• 13:19
  - all my family is, in the health care
• 13:21
  - space. So I think they've been
• 13:23
  - excited for me to, use the,
• 13:27
  - skills that I developed
• 13:29
  - more on the business side of things and,
• 13:31
  - bring it to health care and help health
• 13:32
  - care, leaders like themselves,
• 13:35
  - and their their practices.
• 13:36
  - And then in terms of, like, your question
• 13:38
  - around,
• 13:39
  - the company, yes, we've, raised over $35,000,000
• 13:43
  - from Andreessen Horowitz, which is the largest venture
• 13:45
  - fund in the world.
• 13:47
  - We've got over a 100 customers, over a
• 13:49
  - 100 employees today and, like, growing fast. And,
• 13:52
  - we're seeing impact in our customers.
• 13:55
  - And we've got... I think, like, the most
• 13:57
  - rewarding part,
• 13:59
  - for me is seeing how COOs and CEOs
• 14:02
  - of practices,
• 14:04
  - thank us for the impact that we're having
• 14:06
  - with them. And I think that, you know,
• 14:08
  - as an operational leader myself in the past,
• 14:11
  - COOs of, medical practices are really firefighting all
• 14:15
  - day.
• 14:16
  - You've got,
• 14:17
  - always challenges. There's, like, turnover of staff. There's
• 14:20
  - insurance companies changing their policies consistently.
• 14:23
  - There's,
• 14:24
  - physicians asking also different things, consistently.
• 14:28
  - There's weather events that change suddenly, like operations
• 14:31
  - across locations,
• 14:33
  - when there's,
• 14:34
  - snowstorm in the Northeast.
• 14:36
  - And so being able to
• 14:38
  - bring AI technology that helps them streamline some
• 14:41
  - of those processes on the previous process
• 14:43
  - and there's a, benefits process,
• 14:46
  - and seeing the impact that we can drive
• 14:48
  - on
• 14:49
  - practices
• 14:50
  - in terms of increasing the revenue,
• 14:52
  - has been something rewarding because I think it
• 14:54
  - does help to,
• 14:57
  - help...
• 14:57
  - Or it does help, independent practice especially
• 15:01
  - to continue to be afloat
• 15:03
  - and thrive in, in the current environment.
• 15:07
  - Amazing. And anything that has surprised you as
• 15:09
  - you've transferred your energies into the health care
• 15:11
  - business, I know you have a health care
• 15:13
  - backroom... Background.
• 15:15
  - At at Bain, though, you weren't particularly doing
• 15:17
  - health care as a vertical. Were you at
• 15:19
  - Bain or no?
• 15:20
  - Yeah. So I was at Bain. I was,
• 15:23
  - a journalist. I really do, like, a couple
• 15:25
  - of projects in health care, but I was
• 15:27
  - generalist working with private equity mostly
• 15:30
  - and financial services.
• 15:32
  - But, yeah, it was, across the board.
• 15:35
  - In terms of, like, your question, like, what's
• 15:36
  - been surprising?
• 15:38
  - To me, it's been the rate of adoption
• 15:40
  - of health care,
• 15:42
  - practices with AI. I think, historically,
• 15:45
  - health care has always been seen as a
• 15:47
  - laggard on adoption of technology.
• 15:49
  - We... In two thousand tens, we needed, like,
• 15:51
  - a law to,
• 15:53
  - to make, EHRs compulsory
• 15:56
  - because EHRs were not being adopted by the...
• 15:58
  - By, ambulatory
• 16:00
  - practices.
• 16:01
  - And it was always blamed on the industry.
• 16:04
  - And I think that,
• 16:05
  - well, the point that people missed is that
• 16:08
  - it was never about the industry.
• 16:10
  - It was that at the time, the EHRs
• 16:12
  - were, like, implementing an EHR was actually more
• 16:15
  - inefficient or, like, was creating,
• 16:17
  - more trouble for physicians and for admin... For
• 16:20
  - the administrative staff to run the practice. And
• 16:22
  - so they were reluctant in adopting,
• 16:25
  - software,
• 16:27
  - in their practice because it was not helpful.
• 16:29
  - But what we're seeing now with AI is
• 16:32
  - that actually
• 16:33
  - AI is driving impact that is helpful for
• 16:36
  - practices. Like, the fact that, like, we're able
• 16:38
  - to complete 60% of the calls end to
• 16:40
  - end. You know? When someone picks up,
• 16:42
  - we can, like, keep 60% done by the
• 16:46
  - AI and 40% is passed by hue... To
• 16:48
  - humans.
• 16:49
  - That means that, like, we're really solving a
• 16:50
  - pain point for the COOs, and that's why
• 16:53
  - we're seeing AI technology
• 16:55
  - be adopted so fast in health care. Not
• 16:58
  - only our technology itself, but like AI scribes
• 17:01
  - or even
• 17:02
  - the AI doctor, that you've probably, seen. And
• 17:06
  - it's because when you help,
• 17:09
  - people get the job done,
• 17:11
  - they will adopt things fast.
• 17:13
  - And in health care, AI today is getting
• 17:16
  - the job done. And that means that,
• 17:19
  - the adoption is accelerating
• 17:21
  - much faster than when the EHRs were being
• 17:24
  - implemented fifteen years ago.
• 17:26
  - And let me ask you a question about
• 17:27
  - that because that's, you know, not that long
• 17:30
  - ago,
• 17:31
  - you would call into a call center or
• 17:33
  - you would call into, like, anything,
• 17:35
  - and you get the computer on the phone.
• 17:39
  - And you're saying right now the number is
• 17:41
  - about 60 to 70%.
• 17:43
  - We went through that computer on the phone.
• 17:45
  - Today's AI
• 17:47
  - can get you your answer. Could get a
• 17:49
  - completion and closure of wherever that task was.
• 17:52
  - Talk for a second about...
• 17:54
  - Because I... You know, it used to be
• 17:56
  - that you would dial into these things whether
• 17:57
  - in Expedia, your bank, wherever it was.
• 18:00
  - They give you an electronic voice,
• 18:03
  - and you were lucky if you got through,
• 18:05
  - you know, dialing the airlines.
• 18:07
  - 20% of calls completed that way without having
• 18:10
  - to go to a person.
• 18:12
  - Mhmm. And and and now you're up to
• 18:14
  - 60 to 70%.
• 18:16
  - Is there a place where that number gets
• 18:19
  - to
• 18:20
  - 80 to 90%?
• 18:21
  - And how far away are we from that?
• 18:23
  - Because, you know, again, you know, the one
• 18:25
  - thing... One of the things about the beauty
• 18:26
  - of the world is
• 18:28
  - we are all consumers. So all of us
• 18:30
  - have these shared experiences
• 18:31
  - just like the ones you're trying to solve.
• 18:34
  - Mhmm. Mhmm. And and at what point when
• 18:36
  - you call the doctor's office to get something
• 18:37
  - set up,
• 18:39
  - does it move from being,
• 18:40
  - you know...
• 18:42
  - Like like, one of my experiences last week
• 18:43
  - setting up a a procedure,
• 18:45
  - every single thing was done through a hema
• 18:47
  - on the other side,
• 18:49
  - well handled, but, of course, several calls back
• 18:51
  - and forth. And this was not a Prosper
• 18:53
  - AI client, of course. Let me make sure
• 18:55
  - people understand.
• 18:56
  - But at what point
• 18:58
  - do we get
• 19:00
  - to... From 60 to 70 to 80 to
• 19:02
  - 90 Yeah. Where there's not a human in
• 19:05
  - that conversation loop, and we could solve without
• 19:08
  - several calls back before they had last week.
• 19:11
  - Yep. Yeah. Let me first address that that
• 19:13
  - point on, like, what we've been used to
• 19:15
  - up to, like, when AI, really has happened,
• 19:17
  - which is the last two years. Right? I
• 19:19
  - think we've been educated
• 19:20
  - that when you call, somewhere and you hear
• 19:23
  - press 1 for,
• 19:25
  - appointments, press 2 for benefits, press 3 for
• 19:27
  - billing, it's a bad experience. Right? Like, you
• 19:29
  - don't want to be talking to a machine
• 19:31
  - that's talking to you that way. And so
• 19:34
  - you're like operator operator operator.
• 19:37
  - What has happened or what has changed in
• 19:38
  - the last two years is that we can
• 19:40
  - now bring the experience of speaking to ChargeGPT
• 19:44
  - into, like, the front desk office. Right? They're
• 19:46
  - like on the
• 19:48
  - phone with the same intelligence as, like, ChargePT
• 19:50
  - or Claude
• 19:52
  - and the even better understanding
• 19:54
  - of the SOPs of the physicians and even,
• 19:56
  - like, from the staff. Right? And so what
• 19:59
  - that means is actually that it's a very
• 20:02
  - human natural experience today, and that's why we
• 20:04
  - get to a 70%.
• 20:06
  - If not, people would be saying operator, but
• 20:08
  - they don't want to. Like, we're actually
• 20:10
  - to several things to, like, show you how
• 20:13
  - smart the AI is, which is when you,
• 20:15
  - Scott, call and you've already been a patient,
• 20:18
  - we say, good morning, Scott. How can I
• 20:20
  - assist you today?
• 20:21
  - And what we are doing there is we're
• 20:23
  - saying your name, and we're giving you an
• 20:26
  - open ended question, which up until now, never
• 20:28
  - you could speak with an AI or, like,
• 20:30
  - with a with a machine on the other
• 20:32
  - side and
• 20:33
  - ask whatever you wanted. You always had to
• 20:35
  - respond a decision tree. Now we leave it
• 20:38
  - open ended. And so whenever you answer that
• 20:40
  - question and you say, hi. I, you know,
• 20:43
  - I fell.
• 20:44
  - I... I've got a bruise on my,
• 20:47
  - knee, and I need to see an orthopedic,
• 20:50
  - doctor.
• 20:51
  - If we respond to that reaction that you
• 20:53
  - were saying and say, I'm so sorry you
• 20:55
  - fell.
• 20:56
  - Let me make sure that, like, we find
• 20:57
  - someone fast for you. In which location would
• 21:00
  - you want to get visited? If we're able
• 21:02
  - to react like a human would, then is
• 21:04
  - that when patients
• 21:06
  - are excited to talk because you're they're... You're
• 21:08
  - getting the job done for them.
• 21:10
  - In the moment when they realize that, like,
• 21:12
  - the machine cannot think the way that, like,
• 21:14
  - they need you to react, then they want
• 21:16
  - to speak with an operator. But we only
• 21:17
  - have 5% of patients ask for an operator,
• 21:20
  - and it's because we get the job done.
• 21:22
  - And so the reason why we cannot get
• 21:24
  - it, like, further than than the 17, no,
• 21:26
  - I'm like, that we transfer to to patients,
• 21:29
  - to
• 21:30
  - to live reps is because some of the
• 21:32
  - SOPs or some of the policies are too
• 21:34
  - complex.
• 21:35
  - So for a certain visit, for example, you
• 21:38
  - may need to schedule a lab visit and,
• 21:41
  - an MRI and a normal visit with a
• 21:44
  - physician.
• 21:45
  - And for now, we've said, hey. That's better
• 21:48
  - than, like, a, a live rep handles that.
• 21:50
  - But for all the others, we can do
• 21:52
  - it with the AI. And patients, surprisingly, and
• 21:55
  - even, like, the older population actually is even
• 21:58
  - more excited, even though that's counterintuitive.
• 22:01
  - They speak with the AI because it gets
• 22:03
  - the job done.
• 22:04
  - But but your point is so well taken.
• 22:06
  - I mean, when you get to the spot
• 22:08
  - where the person really wants to finish the
• 22:10
  - transaction with the AI
• 22:12
  - versus going to a person, you've really started
• 22:14
  - to win.
• 22:16
  - Mhmm. Because it's at that point that that...
• 22:18
  - Like, there's times, obviously, over the years with
• 22:20
  - Expedia and some of the other things,
• 22:23
  - Xfinity,
• 22:25
  - so many companies, Comcast, you went zero so
• 22:27
  - quickly because you can't wait to get to
• 22:29
  - the person
• 22:30
  - versus being in this endless loop of what
• 22:32
  - felt like just, like,
• 22:33
  - almost useless and couldn't take you to the
• 22:35
  - next step. And so at that point, it
• 22:37
  - must have been ten percent of calls got
• 22:39
  - settled. The only ones that got settled from
• 22:41
  - what I remember
• 22:42
  - were, do you wanna pay your bill right
• 22:44
  - now? If so, hit 2. You could pay
• 22:45
  - your bill right now. Everything else
• 22:48
  - required talking to a person almost always, and
• 22:51
  - maybe that's getting better at some of those
• 22:52
  - companies. I'm sure it is. But the fact
• 22:54
  - that you're up to 60, 70% is fantastic.
• 22:58
  - And are you seeing this... You know, where
• 23:00
  - are you seeing this go to? I guess,
• 23:01
  - you sort of answered that a little bit
• 23:03
  - already, but at what point does that get
• 23:05
  - to... Are you... Gets to 5%? That's the
• 23:07
  - goal?
• 23:08
  - Yeah. Yeah. So
• 23:10
  - we're seeing, like, here the key piece in,
• 23:13
  - you know, front end revenue cycle or, like,
• 23:16
  - the front end pre visit process is not
• 23:18
  - only about the phone call itself. It's about
• 23:20
  - the workflow that needs to happen after. Because
• 23:22
  - every patient that you schedule, you need to
• 23:24
  - make sure that, like, the benefits are covered,
• 23:27
  - that you know if prior auth is needed,
• 23:29
  - and that you can calculate the cost estimate
• 23:31
  - of that patient. And in order to do
• 23:33
  - that,
• 23:34
  - and, like, provide additional context to the patient,
• 23:36
  - you need to be connected to all the
• 23:38
  - systems and do the calculations real time. Right?
• 23:41
  - And so where these... Like, how we get
• 23:43
  - into, like, you know,
• 23:45
  - patients only wanting to speak with AI and
• 23:47
  - being able to do the full transactions for
• 23:49
  - any kind of visit or, like, reason why
• 23:51
  - they call is by connecting all those steps
• 23:54
  - and doing them end to end. And so,
• 23:57
  - what we're very focused on right now is
• 24:00
  - being able to not only speak with our
• 24:02
  - patients on the phone, but when we speak
• 24:05
  - that we're able to check eligibility real time
• 24:07
  - through clearinghouses,
• 24:09
  - that then we can go to portals
• 24:11
  - when needed, that we can even call the
• 24:13
  - insurance companies when needed. And then, like, all
• 24:15
  - that information then can come back to their
• 24:18
  - practice, calculate the cost, and provide that information
• 24:21
  - to the patient. So that, like, in the
• 24:23
  - same
• 24:24
  - conversation and transaction,
• 24:26
  - that happens end to end.
• 24:28
  - And what we've seen up until now when
• 24:30
  - there's even further opportunity
• 24:31
  - is that we've seen an increase of up
• 24:34
  - to 11%
• 24:36
  - of revenue
• 24:37
  - of practices
• 24:38
  - by implementing Prosper AI. And the reason is
• 24:41
  - because, like, we're consistent in this process that
• 24:43
  - I was telling you about. Right? We're consistent
• 24:46
  - in picking up the phone, in checking the
• 24:47
  - right benefits,
• 24:49
  - and so we're
• 24:50
  - able to make sure that, like, the practice
• 24:51
  - doesn't lose demand, not that it's capturing the
• 24:53
  - demand that comes in, especially on Mondays. That's
• 24:55
  - super busy. And then then there's no errors
• 24:57
  - on the benefits and prior auth checks, which
• 25:00
  - tend to be a revenue leakage reason. Now
• 25:02
  - you get denials because the operations team didn't
• 25:05
  - do the right process, didn't know how to
• 25:07
  - check,
• 25:08
  - the benefits correctly.
• 25:10
  - And so by bringing AI to do that,
• 25:12
  - we're seeing the impact, and I think, you
• 25:13
  - know, there's more that we can do on
• 25:15
  - those, two fronts.
• 25:17
  - Thank you so much. Like, simply, truly remarkable.
• 25:21
  - Javier, what a pleasure to visit with you.
• 25:23
  - A total pleasure,
• 25:25
  - brilliant to what you're doing, and I understand
• 25:27
  - it. Like, I understand the practical implications of
• 25:30
  - it. And I always believe
• 25:33
  - when companies can't
• 25:34
  - really translate or founders can't really translate that
• 25:37
  - practical, here's exactly what we're doing. Here's a
• 25:40
  - 60, 70 set milestone that we're at now,
• 25:42
  - and we wanna get to 80, 95, a
• 25:44
  - 100.
• 25:45
  - That to me is fantastic because it means
• 25:47
  - somebody knows what they're doing, what they're trying
• 25:49
  - to pitch, what they're trying to make work,
• 25:51
  - and and I just love that. So congratulations
• 25:54
  - on the amazing success that you've had and
• 25:56
  - and your team is having,
• 25:58
  - and the investments from these great in... Investors.
• 26:00
  - And most importantly,
• 26:02
  - the success with the 150
• 26:04
  - practices that are working with you so far,
• 26:06
  - because that's really, at the end of the
• 26:08
  - day, everything. If they're happy with you and
• 26:10
  - that's going great, a lot of other things
• 26:12
  - will take care of itself. Get all the
• 26:13
  - financing in the world, everything else in the
• 26:16
  - world, but if you can't take care of
• 26:17
  - customers well, and it sounds like you're doing
• 26:19
  - that really well,
• 26:21
  - that's what counts. And it sounds like you're
• 26:23
  - building a great, great business.
• 26:25
  - Javier, thank you so much for taking the
• 26:26
  - time with us today on the Becker Healthcare
• 26:28
  - podcast. What a pleasure to visit with you.
• 26:30
  - Thank you so much, Scott,
• 26:32
  - for for your words. And, yes, as as
• 26:34
  - you said, for me, it's personal because for
• 26:36
  - me, every customer is like my parents. Right?
• 26:38
  - I like my parents' practice. And so I'm
• 26:41
  - extremely excited to be able to have this
• 26:43
  - impact and, you know,
• 26:45
  - go much further than even the 150 that
• 26:47
  - we already have and, help the whole country,
• 26:50
  - with this. So thank you for having me
• 26:51
  - here and help me, share our vision.
• 26:55
  - Literally amazing. Well, you're a better son than
• 26:57
  - me because my parents said I could be
• 26:58
  - a doctor,
• 26:59
  - lawyer, or an accountant, and I ended up
• 27:01
  - being a lawyer or an accountant, but then
• 27:03
  - ended up in media.
• 27:04
  - And my parents who were, you know, literally
• 27:06
  - came here when they were five years old
• 27:08
  - were like, what are you doing in media?
• 27:09
  - Why aren't you a doctor, accountant, or a
• 27:11
  - lawyer? So you are a... You've done a
• 27:14
  - better job of pleasing the parents than I
• 27:15
  - have, but congratulations.
• 27:17
  - It's it's literally fantastic what you're doing.
• 27:19
  - Awesome. Thank you very much.