Speaker 1: Bloomberg Audio Studios, Podcasts, radio news.
Speaker 2: You're listening to Bloomberg Business Week with Carol Messer and
Speaker 2: Tim Stenebek on Bloomberg Radio. Okay, get this, Carol. In
Speaker 2: the US, more than twenty two point six million people
Speaker 2: work in healthcare a lot. It's about fourteen percent of
Speaker 2: the US working population. This is according to the Bureau
Speaker 2: of Labor Statistics. Yeah, healthcare pretty huge. There's a ton
Speaker 2: of money there and there are a lot of inefficiencies.
Speaker 1: We hear about that.
Speaker 2: You ever tried to go get an MRI like sent
Speaker 2: from one healthcare provider to another.
Speaker 1: It's like impossible.
Speaker 2: You got a cd ROM seriously, like way they still
Speaker 2: make that. Don't even get me started on faxes because yeah,
Speaker 2: doctors are still doing that too.
Speaker 3: I know.
Speaker 2: Well. Anterior is trying to make the system more efficient.
Speaker 2: It's an AI company. It's focused on healthcare administration. It's
Speaker 2: backed by Anya, Sequoia Capital and more. We've got with
Speaker 2: us doctor abdel Mamod, CEO of Interior. He joins us
Speaker 2: from London, Doctor Mahmood, good to have you with us
Speaker 2: this after this evening. Thanks for staying up late. We're
Speaker 2: in sort of the chain of the healthcare ecosystem, do
Speaker 2: you fit.
Speaker 3: Yeah, well, lovely to meet you and thank you for
Speaker 3: having me on. So we tut of the main area
Speaker 3: is administration. So we were born to tackle the trillion
Speaker 3: dollars that I think the US spends on healthcare administration
Speaker 3: every single year. And administration is made up of many,
Speaker 3: many workflows, but at the core of it, and I
Speaker 3: think you mentioned this statistic, there's a lot of manual labor.
Speaker 3: There's a lot of very manual, tedious things involving factes,
Speaker 3: and yes that's true. Doctors still use factors today. So
Speaker 3: we've started mainly on the health insurance because we feel
Speaker 3: like a lot of that administrative workflows and burdens starts
Speaker 3: from that side, and we focus there, and maybe they're
Speaker 3: the easy way to say it is we're focused on
Speaker 3: wherever there's a fax machine that a kind of a
Speaker 3: highly trained nurse or adoptor needs to spend hours reading,
Speaker 3: that's the bit that we make faster.
Speaker 1: Okay, So I feel like folks have been trying to
Speaker 1: figure out how to modernize healthcare records and then also
Speaker 1: protect patient privacy. So tell us about how you guys
Speaker 1: are going to actually figure this one out. Why you
Speaker 1: versus everybody else who's been talking about it and trying it.
Speaker 1: Why you can do this.
Speaker 3: Yes, that's a great question, and I think you touched
Speaker 3: on something important there, which is patient privacy and interoperability, right.
Speaker 3: And the reason we have facts today is actually because
Speaker 3: it's one of the most secure forms of getting two
Speaker 3: systems that don't know each other kind of aren't connected
Speaker 3: electronically to be able to communicate in a very safe way.
Speaker 3: The result in issue of that is that when you
Speaker 3: send over a fax, the system on the other side
Speaker 3: receives the facts, but it can't ingest the data. Right.
Speaker 3: It's four hundred pages of medical information that only a
Speaker 3: human today can read, so you hire a doctor or
Speaker 3: nurse to just read it. And I think the bit
Speaker 3: that's made us stand out in the market and we
Speaker 3: have the leading product in the market that we be
Speaker 3: able to understand unstructured medical data and we're almost like
Speaker 3: recreate the structured version of that and do interesting things
Speaker 3: with that downstream, such as bio authorizations, payment integrity, and
Speaker 3: a few other certain workflows. And that's the the bit
Speaker 3: that's the most valuable here is you can maintain if
Speaker 3: you still use facts, and we should be getting rid
Speaker 3: of facts, but it remains a kind of a core
Speaker 3: part of of the US healthcare system. It allows you
Speaker 3: to kind of still have that digitized and still have
Speaker 3: that interoperable to that you know, of most valuable assets
Speaker 3: and resources nurses, doctors, pharmacists on not spending hours reviewing facts.
Speaker 2: Is this for the US healthcare system only?
Speaker 3: Yes? I think the US healthcare system is quite unique
Speaker 3: in the sense that it spends so much on these
Speaker 3: administrative workflows that are quite outdated in a sense.
Speaker 1: Yeah.
Speaker 2: Yeah, So where were you in your medical school journey
Speaker 2: or in your career where you discovered that the US
Speaker 2: has these inefficiencies. Take us to that moment.
Speaker 3: Yeah. So I trained in the UK, and somewhere along
Speaker 3: the journey I realized that, you know, the impact you
Speaker 3: can have in healthcare is probably less about how better
Speaker 3: I got as a doctor clinically, but more about the
Speaker 3: environment and ecosystem and the tools that we use as condisions.
Speaker 3: So I embarked on a master's degree in computer science
Speaker 3: and spend some time actually Facebook and then later at
Speaker 3: Google working on some healthcare applications, and that's where I
Speaker 3: got exposed as you know, those are American companies. I
Speaker 3: got exposed to the US healthcare system and I saw that, yes,
Speaker 3: there are issues of this in Europe, and there are
Speaker 3: issues in Canada and other places, but in the US
Speaker 3: it's really magnified and it's blown up in the last
Speaker 3: ten years or so. And that's what got me exposed.
Speaker 3: And that's when some of the large language model staff
Speaker 3: and GENERALLYVII that was coming out, felt like that was
Speaker 3: the most exciting thing you could apply large language models to,
Speaker 3: which is this huge problem of unstructured data. That guess what,
Speaker 3: large language models inherently have that capability that we never
Speaker 3: had before of structuring unstructured data.
Speaker 1: Go ahead, well, you know, it's interesting. I do think
Speaker 1: about AI and large language models and how they might
Speaker 1: be able to look at an X ray, you know,
Speaker 1: and compare it to all these others, you know, in
Speaker 1: a database and maybe figure out when there really is
Speaker 1: something wrong and do it rather quickly versus waiting for
Speaker 1: humans or maybe there's a doctor who's tired and it
Speaker 1: doesn't come out so well. Having said that, I'm reading
Speaker 1: from the Sequoia website says that what you guys are
Speaker 1: doing handles all the work that comes before the clinical
Speaker 1: decision making, where it might take bless you a nurse
Speaker 1: several hours to track down, organize, and summarize hundreds of
Speaker 1: pages of records they need, Interior can do it in seconds,
Speaker 1: allowing clinicians to increase their output tenfold and focus on
Speaker 1: what matters most, helping patients love that love that mission.
Speaker 1: How do we know that LLMS that AI is going
Speaker 1: to make the right clinical decisions?
Speaker 3: That's a great question, and that's because you never let
Speaker 3: the large language model make the clinical decision. That is
Speaker 3: the key bit here, which is everything up until the
Speaker 3: clinical decision is the bit that takes the most time
Speaker 3: and the most effort. So if you look at the
Speaker 3: nurses workflow right, they'll spend kind of of ninety five
Speaker 3: percent of the time logging into the twenty different systems
Speaker 3: right downloading the right PDF files, finding the clinical reference
Speaker 3: and documentation and guidelines, mapping it all out, finding needles
Speaker 3: in a haystack across hundreds of pages to then just
Speaker 3: have all the information gathered so that he or she
Speaker 3: can make that decision right. So what we tell our
Speaker 3: nurses that use the product and the reason they love
Speaker 3: it is like each one of you gets an intern
Speaker 3: that overnight really smart intern that overnight has prepped all
Speaker 3: the nurs right, has gone through everything, and then lays
Speaker 3: it up for you. So you make the call, right,
Speaker 3: and that what that is is you ensure that nurses
Speaker 3: and doctors go through administration much much faster. But they
Speaker 3: focus on the bit that they're great at the top
Speaker 3: of their license, the thing they went to medical school
Speaker 3: or nursing school for many years for.
Speaker 2: So I want to know about training these models because
Speaker 2: there's a big issue when it comes to what this
Speaker 2: stuff is trained on. How do you do this when
Speaker 2: patient data is confidential?
Speaker 3: Yeah, so flatter out. We don't absolutely don't train on
Speaker 3: any customer data, and I don't think companies would even
Speaker 3: allow you to, right, So that's all in the contract
Speaker 3: that we sign and so on. I think the world
Speaker 3: has moved on in a sense of the previous issue
Speaker 3: with AI was that you needed to train a lot
Speaker 3: of medical data to even have something viable. But I'm
Speaker 3: sure you've seen chat, gipt passes, the USMLI and all
Speaker 3: these things. A lot of these general models that we
Speaker 3: use off the shelf right, they're actually pretty good clinically.
Speaker 3: The trick is that last ten percent though, And the
Speaker 3: reason why chat gipt isn't doing any of this stuff
Speaker 3: today is because it maybe gets sixty percent of the
Speaker 3: way there. How you orchestrate them, How you get multiple
Speaker 3: large language models, including also built on top of the
Speaker 3: decades of machine learning progress that we've had, on top
Speaker 3: of databases and and other kind of computational techniques. How
Speaker 3: do you stitch that all together in end to end
Speaker 3: experience that feels like almost almost like a kind of
Speaker 3: completes the picture with a great user experience as well.
Speaker 3: That's the bit we focus on, and that's the bit
Speaker 3: that actually delivers them value.
Speaker 1: Well, let us know how things are going. Doctor Abdel Mahmoud,
Speaker 1: Chief Executive Officer of Interior, joining US