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How AI is Transforming LTC Pharmacy | FrameworkFocus™ · Pharmacy Podcast Network

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Speaker 1: You are listening to the global network of podcasters dedicated
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Speaker 1: to the pharmacy profession. Welcome to the Pharmacy Podcast Network.
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Speaker 2: You are listening to Framework Focus, the podcast that explores trends, innovations,
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Speaker 2: and insights in the long term care pharmacy industry. Join
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Speaker 2: us as we connect the entire LTC ecosystem.
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Speaker 3: Hey, welcome back to Framework Focus, the Softwriters podcast.
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Speaker 4: It's great to have you.
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Speaker 3: Back listeners out there tackling sometimes struggling. Hopefully not hopefully
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Speaker 3: you're preparing for twenty twenty six and but boy it
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Speaker 3: to run and operate and operate a pharmacy, a long
1:02
Speaker 3: term care pharmacy today, there's a lot of things. There's
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Speaker 3: a lot of obstacles, a lot of things to get through.
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Speaker 3: It's the planning. It's putting things almost like a blueprint,
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Speaker 3: making sure that you're writing those goals and the challenges
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Speaker 3: down reach out to your technology providers. That's why Framework
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Speaker 3: Focus is such an important podcast on the Pharmacy Podcast Network.
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Speaker 3: We can rely on the team at Softwriters to bring
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Speaker 3: us information around the technology space and automation and tie
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Speaker 3: ins and today is a topic that we've been talking
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Speaker 3: about a lot on the Pharmacy podcast network and today
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Speaker 3: it's how AI is transforming long term cure pharmacy. We
1:46
Speaker 3: have to have people on the team and in our
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Speaker 3: partners directories to get insights from because it's not us,
1:56
Speaker 3: it's I mean, it's certainly not me. I look for
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Speaker 3: people that are the best to the best. I want
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Speaker 3: to introduce our guests today. I want to start off
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Speaker 3: with Rick Pollock. If you could just give us some
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Speaker 3: background on your soft Rick and share with the framework
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Speaker 3: focused listeners who you are, what you do in long
2:13
Speaker 3: term care pharmacy.
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Speaker 4: Yeah, yeah, absolutely.
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Speaker 5: You know, we were talking just a little bit ago,
2:18
Speaker 5: and I give you a little bit of my background.
2:20
Speaker 5: I started as a software engineer for years and kind
2:23
Speaker 5: of transitioned into project and product management a while back.
2:27
Speaker 5: I've been in healthcare for you know, fifteen years plus,
2:31
Speaker 5: and I've been at Softwriters for about a year. When
2:34
Speaker 5: I was brought into Softwriters, I was working as their
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Speaker 5: primary release manager, so making sure that the stuff that
2:39
Speaker 5: we work on from a software standpoint, deliverable standpoint goes
2:43
Speaker 5: out smoothly on time, gets validated correctly, so on and
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Speaker 5: so forth. And you know, I'm sure Kaywa allude to it.
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Speaker 4: As well.
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Speaker 5: There's been a hard shift recently to kind of really
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Speaker 5: incorporate AI into our product suite, and they brought together
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Speaker 5: a small group that we call the Alpha Team, and
3:02
Speaker 5: they invited myself and Kyle and some other folks to
3:05
Speaker 5: be a part of that. So right now I am
3:07
Speaker 5: part of that team. I'm still doing some release management work,
3:09
Speaker 5: but also working exclusively within the realm of how do
3:13
Speaker 5: we bring AI forefront, how do we get it into
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Speaker 5: our products, and how does it make sense to our stakeholders,
3:19
Speaker 5: to our customers and so forth. So that's what I've
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Speaker 5: been doing now for the last i'd say in full months.
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Speaker 4: That's that is so cool.
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Speaker 3: Kyle Nilsen, you're with the Alpha Labs team at Starck
3:33
Speaker 3: Writers as well. Before we get Kyle to do is introduction,
3:37
Speaker 3: they got to make you, guys some kind of patch
3:40
Speaker 3: to put on your jack because you guys are like
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Speaker 3: a special forces team.
3:44
Speaker 4: Now, Oh, trust me.
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Speaker 5: I used AI to create one day one and it
3:49
Speaker 5: looks like the what is it Star Trek Enterprise kind
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Speaker 5: of logo?
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Speaker 3: Your way ahead of mey, Kyle, jare a little bit
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Speaker 3: of your background as well with our framework folks us listen.
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Speaker 6: Yes, I've been with Softwriters just about three years now,
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Speaker 6: started on the pharmacy support team, so I've spent a
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Speaker 6: lot of time with our customers talking about what they
4:10
Speaker 6: want to need, what they like and they don't like.
4:12
Speaker 6: So I've got a pretty good grasp on all that.
4:15
Speaker 6: And in the last year I've transitioned on the product
4:17
Speaker 6: management team, spent a lot of time helping get framework Insight,
4:21
Speaker 6: our new BI solution out the door. And in the
4:25
Speaker 6: last few months I've been working on the d Alpha
4:27
Speaker 6: Labs team, and I've been able to bring my background
4:30
Speaker 6: in kind of restaurant and retail operations into that BI.
4:35
Speaker 6: I'm kind of realm I'm just for free analytics, and
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Speaker 6: that's transition into the AI world because the whole point
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Speaker 6: of this is to make things more efficient for the
4:44
Speaker 6: pharmacies to drive that bottom line, and I think my
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Speaker 6: background kind of falls into that that team pretty easily.
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Speaker 4: All right, but let me wesh.
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Speaker 3: I guess we'll shift gears real quick back to Rick
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Speaker 3: just to kick us off.
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Speaker 4: And that is so we're talking about AI.
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Speaker 3: We've heard about it in pre authorization, prior authorizations. We've
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Speaker 3: heard about it in data reporting obviously, in financials I
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Speaker 3: think it's under it's easier to understand. But what's the
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Speaker 3: What about the biggest opportunities that you see for artificial
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Speaker 3: intelligence solving specific issues in the long term care pharmacy sector.
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Speaker 5: Yeah, I'll break it down like this because I like
5:26
Speaker 5: to speak in plain language too.
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Speaker 4: I don't want to get overly technical.
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Speaker 5: But it's all about areas that we can find efficiencies
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Speaker 5: right where we take it away. AI is really good
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Speaker 5: at doing things that humans are not good at, repetitive tasks,
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Speaker 5: deciphering things, going into large data sets and finding patterns
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Speaker 5: and themes very very quickly. So how can we what
5:50
Speaker 5: are some of the things in our day to day
5:51
Speaker 5: operations that we see our pharmacists, our technicians doing that
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Speaker 5: we can say, oh, this can be expedited by using
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Speaker 5: an LLLM, by integrating AI, or by introducing some sort
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Speaker 5: of logic. Those are the biggest, and there's a plethora
6:04
Speaker 5: of them, so I couldn't pinpoint one or the other.
6:06
Speaker 5: But there are a ton of opportunities where hey, we're
6:09
Speaker 5: making sense of messy forms, right, we have multi page
6:13
Speaker 5: packets that we need to marry up data on. How
6:15
Speaker 5: can AI kind of go through and look at that
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Speaker 5: and observe that and give us a readout so that
6:20
Speaker 5: we're not having to trudge through that spotting patterns like
6:25
Speaker 5: we talked about a second ago, risks, the being boring,
6:28
Speaker 5: repetitive clicking tasks that we do for new RX prescriptions.
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Speaker 4: All those things.
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Speaker 5: Are things AI or and or the logic that kind
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Speaker 5: of ours AI is really really good at, takes it
6:40
Speaker 5: off of our pharmacists plate that we work with, kind
6:44
Speaker 5: of puts it into the hands of the computer.
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Speaker 6: Yeah, like just add on to that. At the end
6:48
Speaker 6: of the day, our pharmacies are asking for it. You know,
6:50
Speaker 6: we didn't just put a team together and say how
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Speaker 6: can we get AI into pharmacy. It's it's been a
6:56
Speaker 6: conversation for years. Really, as soon as AI became prevalent,
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Speaker 6: our pharmacies have been asking our executives and our product team, Hey,
7:04
Speaker 6: how can we increase automation, how can we brand the software?
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Speaker 6: What can we do? And that's what we're doing here.
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Speaker 6: It really eighty percent of their time has spent on
7:15
Speaker 6: twenty percent of their work and in a landscape where
7:18
Speaker 6: margins are shrinking, whether it's manufacturing issues, whether it's the
7:23
Speaker 6: maximum fair price topic with medicare margins are getting smaller
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Speaker 6: and they have to manage this as closely as they can.
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Speaker 6: So if they can make that eighty percent of their
7:34
Speaker 6: time be forty percent, that's a huge win from them.
7:36
Speaker 3: Kyle, what about like that day to day work and
7:41
Speaker 3: how it may improve the actual operations or workflow.
7:45
Speaker 4: Give us some examples of that day to day impact.
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Speaker 6: Yeah, so a lot of pharmacies don't have a huge staff.
7:55
Speaker 6: Some of our pharmacies are large summer smaller. Some of
7:58
Speaker 6: them rely on entry texts all this order entry. Some
8:01
Speaker 6: have special teams just for new facility or new customer onboarding.
8:05
Speaker 6: Some pharmacies have the pharmacists entering orders. So when they
8:09
Speaker 6: spend that much time doing manual tasks, that's high cost labor.
8:14
Speaker 6: They could be shifted towards more quality work, not just
8:17
Speaker 6: hand king orders, but actually making proper recommendations, consulting, and
8:23
Speaker 6: spending more time on reviewing complex orders and patients instead
8:28
Speaker 6: of just hand typing in the same one hundred orders
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Speaker 6: over and over again.
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Speaker 3: Any concerns Rick when you're thinking about AI, I mean,
8:40
Speaker 3: I know my concerns as a published a publisher, authenticity,
8:44
Speaker 3: making sure that when I use AI more for spell.
8:48
Speaker 4: Checking than anything.
8:49
Speaker 3: To give you guys some insight to how simplicity I
8:53
Speaker 3: use AI. But what about the fact and fiction of
8:59
Speaker 3: it all? Rick, what concerns do you hear from pharmacists
9:02
Speaker 3: A boudy eye?
9:04
Speaker 5: The two big ones that I've heard, and these things
9:06
Speaker 5: actually come from myself too in certain ways.
9:08
Speaker 4: But are hey, is this going to replace jobs?
9:13
Speaker 5: Right? Is this just going to be a wholesale We're
9:15
Speaker 5: going to implement AI, and so we don't need the
9:17
Speaker 5: human aspect in certain areas, not every area, but in
9:20
Speaker 5: certain areas like that's a big one. We can dive
9:22
Speaker 5: into that a bit, and then the other one and
9:24
Speaker 5: this goes down a little bit more of an intricate trail.
9:26
Speaker 4: But is how do we secure this? Right?
9:29
Speaker 5: There's PHI, there's hipoc compliance, there's different things that we
9:31
Speaker 5: need to be concerned with. So how do we keep
9:33
Speaker 5: that secure and tight? And what are we doing to
9:36
Speaker 5: make sure that it's clinical grade? The stuff that we're
9:39
Speaker 5: implementing 're opposing to implement. So those are the two
9:43
Speaker 5: big areas we.
9:44
Speaker 4: Can dive in. Or Kyle, if you want to add
9:45
Speaker 4: on to that, I'd be happy to.
9:48
Speaker 6: Yeah, the two words you said there that stand out
9:51
Speaker 6: to me or clinical grade. Right. So if I'm working
9:54
Speaker 6: on a side side project, I'm trying to analyze a document,
9:58
Speaker 6: summarize it. If I get kind of a fuzzy match somewhere,
10:01
Speaker 6: that's usually okay. If I'm looking at patients and drugs
10:07
Speaker 6: and allergies, I can't have a fuzzy match, right. I
10:11
Speaker 6: can't have AI guessing on a drug man or an
10:13
Speaker 6: allergy if it doesn't know exactly what it is. So
10:16
Speaker 6: we have to be able to cut through that and
10:19
Speaker 6: make sure we're speeding things up. But we can be
10:22
Speaker 6: one hundred percent accurate in what we do.
10:25
Speaker 3: I think of databases when you're talking, when you're saying this, Kyle,
10:30
Speaker 3: I'm thinking of medi spin or Walter's Clue or something.
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Speaker 3: It has massive amounts of data accessibility, but it's very
10:38
Speaker 3: tedious to have to dial into maybe a specific titration
10:43
Speaker 3: or a specific instance of medication issue or whatever it is.
10:48
Speaker 3: And I think, boy, if we could take some kind
10:51
Speaker 3: of API, some kind of connection, tied it into these
10:54
Speaker 3: databases that are there's so much information in it, and
10:59
Speaker 3: then ask us civic question or ask a specific issue
11:02
Speaker 3: that a patient is experiencing. Now we can resource a
11:07
Speaker 3: trusted database that does have the evidence based information in
11:13
Speaker 3: it and then marry that with some of our processes.
11:16
Speaker 3: Is that to star trek Key right now or is
11:20
Speaker 3: that kind of like where we're going with all this.
11:24
Speaker 6: No, that's exactly where we're going, honestly. So one of
11:27
Speaker 6: our hurdles that we're getting over and we're working through
11:31
Speaker 6: is drug matching, right, So we use NDCs and gpis
11:36
Speaker 6: and a doctor might be sending in our ex norms.
11:39
Speaker 6: So we're pulling in different types of data from Walter's
11:42
Speaker 6: clewer just so we can kind of dotorize and cross
11:45
Speaker 6: our teas and make sure that, like you said, we
11:47
Speaker 6: are being accurate, thorough and using all the resources at
11:51
Speaker 6: our disposal.
11:52
Speaker 5: What's cool about that, what Kyle just said, what you
11:54
Speaker 5: were getting at too, is that because of that, going
11:58
Speaker 5: back to the question you asked, were omitting jobs, we're
12:01
Speaker 5: validating data right, and the AI has proposed in many ways,
12:07
Speaker 5: is becoming more of a helper. It's a supplemental thing, right,
12:10
Speaker 5: and it's going to make pharmacies, pharmacists and everybody across
12:13
Speaker 5: the board faster and cleaner and easier to understand things
12:17
Speaker 5: and decipher things. And make the right decisions and as
12:20
Speaker 5: we go forward, it'll become better and better and better.
12:23
Speaker 4: I just wanted to add that in that's excellent, excellent.
12:26
Speaker 3: Then I want to go back to what Kyle was saying,
12:28
Speaker 3: because you mentioned there's a balance between innovation, safety, accuracy,
12:34
Speaker 3: and regulatory requirements.
12:36
Speaker 4: That's a lot to juggle.
12:37
Speaker 3: I know that if I'm a long term care pharmacy operator,
12:40
Speaker 3: I don't really have to want to have to think
12:42
Speaker 3: about having to double check my pharmacy management software systems.
12:46
Speaker 3: I'd like that to just kind of be baked into
12:50
Speaker 3: whatever whatever it is. How to expand upon what you
12:53
Speaker 3: were saying when it comes to safety and accuracy and
12:57
Speaker 3: the meat, how it meets the road of.
13:02
Speaker 6: Yes, the approach we're taking is we kind of get
13:05
Speaker 6: that temperature right, so we when all of our orders
13:08
Speaker 6: come in, whether it goes new batch or whether it
13:09
Speaker 6: gets put back into the queue to be hand entered,
13:12
Speaker 6: we're able to distinguish through our pipeline if it was
13:18
Speaker 6: match on automation with no AI, did AI have to
13:21
Speaker 6: touch something? Okay, If it did, how do we flag
13:24
Speaker 6: that for the end user to see so they know
13:26
Speaker 6: they have to double check it or if something I'm
13:28
Speaker 6: rejected for a specific reason, they're going to know that
13:30
Speaker 6: as well. So that way, we're taking this in kind
13:33
Speaker 6: of bite sized chunks. We're not just saying, Okay, let's
13:36
Speaker 6: flip a switch and turn AI on and now you
13:38
Speaker 6: have a thousand orders you have to go hope they're accurate.
13:41
Speaker 6: You know, we're doing this in small chunks just to
13:44
Speaker 6: make sure that we not only can be as efficient
13:48
Speaker 6: as we can, but we'll see it in small sample
13:50
Speaker 6: sizes first and make sure we know kind of where
13:53
Speaker 6: we stand. Then we can throttle and kind of change
13:57
Speaker 6: that speed up a little bit, up a little more,
13:59
Speaker 6: figure out years, we're seeing what the reasons are, manage those,
14:04
Speaker 6: and just kind of up to that percentage gradually. So
14:05
Speaker 6: we're not we didn't make a goal to have one
14:07
Speaker 6: hundred percent automation on day one, you know in our
14:10
Speaker 6: first test form is because that's just not realistic or safe.
14:13
Speaker 6: It's hey, let's let's start with five percent. Okay, we
14:15
Speaker 6: got that, let's go to fifteen percent.
14:17
Speaker 4: Okay, we got that.
14:19
Speaker 6: The drug matching is the next big thing we want
14:21
Speaker 6: to tackle. Okay, let's let's change how we look at
14:24
Speaker 6: WAL There's CHLORI data and now we can get that
14:26
Speaker 6: to thirty percent. And that's that's how approaching this and
14:29
Speaker 6: the safest way we can.
14:34
Speaker 3: All Right, I want to I guess I should have
14:37
Speaker 3: asked this in the beginning of the interview, but I
14:39
Speaker 3: like throwing. I guess throwing things in different directions. It
14:42
Speaker 3: keeps everybody on your toes. But creation of Alpha Labs this,
14:46
Speaker 3: like I said, it sounds like a force is a
14:49
Speaker 3: division of soft writers. Talk to us about that they
14:52
Speaker 3: recently launched Alpha Labs and what what sparked that whole
14:56
Speaker 3: initiative and why'd you guys, why'd you guys do what
15:00
Speaker 3: you're what you're deciding to do.
15:02
Speaker 5: Yeah, I'll jump in and I'm sure Kyle will have
15:05
Speaker 5: more to add. But you know, when I was approached
15:09
Speaker 5: with the topic of Alpha Labs and joining, it's essentially, hey,
15:13
Speaker 5: there's a lot of stuff that we can do, but
15:15
Speaker 5: we want to kind of give the time to do
15:18
Speaker 5: it right. We want to kind of carve out a
15:21
Speaker 5: place where we can experiment and try and learn and
15:24
Speaker 5: change things and come up with proof of concepts, you know,
15:29
Speaker 5: And that is exactly what Alpha Labs is. It's it's
15:32
Speaker 5: a group of folks that are explicitly focused on what
15:35
Speaker 5: are we being asked for, what do people need? What
15:37
Speaker 5: can we try? How can we attempt this? And we
15:40
Speaker 5: have the space to do that. That's what Alpha Labs is.
15:43
Speaker 5: That's exactly I think the thought process that kind of
15:45
Speaker 5: came up with generating Alpha Labs.
15:51
Speaker 6: Yeah, and we brought in a well rounded team, yeah,
15:55
Speaker 6: everywhere from the executive level done to myself who came
15:59
Speaker 6: from and on the product and bi side rix, our
16:03
Speaker 6: resident AI Whiz, We've got an architect on the team.
16:07
Speaker 6: So we're looking at it from all different angles. Right,
16:10
Speaker 6: So we between all of us, we've kind of touched
16:13
Speaker 6: almost every part of our company in some way, shape
16:15
Speaker 6: or form, so we're able to bounce ideas off each
16:19
Speaker 6: other and kind of answer questions that we didn't know
16:22
Speaker 6: were questions. Right, So I think this idea is great,
16:25
Speaker 6: and then our architect MND say well that's that's a
16:27
Speaker 6: little more tricky than the seams, or he might have
16:30
Speaker 6: an idea and we could say, well, not sure that's what.
16:33
Speaker 4: The customers want.
16:34
Speaker 6: So we have a really well randed team that can
16:37
Speaker 6: look at things from all these different angles that we mean.
16:39
Speaker 5: To What's cool too, is that just like Kyle said,
16:43
Speaker 5: is because we have that while we're on a team,
16:45
Speaker 5: and because we're able to have that time and space
16:48
Speaker 5: to try things, often find that, you know, we're sitting
16:52
Speaker 5: in a room, we're talking about the architecture or the
16:53
Speaker 5: design of something, and there's always.
16:55
Speaker 4: Somebody said, well what about this, or maybe.
16:57
Speaker 5: You didn't consider this right, where my expertise does not
17:01
Speaker 5: lie necessarily with you know, medical terminology, right, so I
17:06
Speaker 5: don't necessarily think through every facet of something that somebody
17:11
Speaker 5: like Kyle or some of the other members like Lisa
17:13
Speaker 5: who's on our team might come up with and suggest, hey,
17:16
Speaker 5: you should consider that she should try it this way.
17:19
Speaker 4: So very very true what Kyle said. Totally agrees.
17:23
Speaker 3: Yeah, and there's different challenges that people in that technology
17:29
Speaker 3: space have.
17:32
Speaker 4: I like what Kyle agree with you.
17:34
Speaker 3: You have to take what the client is sharing with
17:38
Speaker 3: you and sometimes deviate or sometimes pivot what you're designing
17:42
Speaker 3: and what you're building because of the end user using
17:47
Speaker 3: it might not make sense where from a programmer's perspective,
17:51
Speaker 3: you see it one way. If you have that kind
17:53
Speaker 3: of diversity on the team, so many of you and
17:58
Speaker 3: Lisa's the OG, she's been you know around forever, you
18:01
Speaker 3: know programming, the first SEQL database driven pharmacy management system
18:07
Speaker 3: and long term care was was Framework LTC. What a
18:11
Speaker 3: It was a mind blower back in the day of
18:14
Speaker 3: how it was used, you know, how it was extracted.
18:16
Speaker 3: So that diversity continues and you know, I want to
18:21
Speaker 3: hear from your team how you think that that empowers
18:25
Speaker 3: off of Labs to do more for a long term
18:29
Speaker 3: care pharmacy operator.
18:31
Speaker 4: Yeah.
18:31
Speaker 6: So the whole concept of the team kind of like
18:34
Speaker 6: we said, is to have a space to innovate, right.
18:37
Speaker 6: So it when we're trying to keep up with kind
18:41
Speaker 6: of like you mentioned a few minutes ago, all the regulations,
18:43
Speaker 6: all the all the bugs, all the deadlines that we
18:46
Speaker 6: have to hit for compliance reasons or you know, just
18:49
Speaker 6: to keep moving things where they need to where they
18:53
Speaker 6: need to drive, we can actually take the time to
18:55
Speaker 6: think up new ideas. Right. So our Framework LTY product
19:00
Speaker 6: owner is just trying to keep up with the day
19:02
Speaker 6: to day grind of keeping this massive software SUITEP moving.
19:06
Speaker 6: And you know how much time does she have on
19:07
Speaker 6: the data actually dedicate to thinking up and testing and
19:12
Speaker 6: making pocs for new ideas. It just the time just
19:14
Speaker 6: isn't there, right, it's it. It's multiple full time jobs
19:18
Speaker 6: just to keep one product moving. So we're able to
19:22
Speaker 6: actually take a step back and not focus on what
19:27
Speaker 6: we need to do the next three months, you know,
19:29
Speaker 6: to be compliant with controlled substance exporting and all that
19:33
Speaker 6: kind of stuff. We can actually think what can we
19:36
Speaker 6: do to make this more efficient? How does AI come
19:38
Speaker 6: into play? What's automation versus AI? How do we how
19:42
Speaker 6: do we decipher that and actually think outside the box.
19:45
Speaker 6: And that's that's our core responsibility.
19:51
Speaker 4: Rick.
19:52
Speaker 3: You mentioned about the team giving you that ability to
19:56
Speaker 3: dedicate more time to innovation because they're able to pull
20:00
Speaker 3: out the project and see it through and work on
20:03
Speaker 3: it together.
20:04
Speaker 4: What does that mean to you, know, giving giving you.
20:07
Speaker 3: The opportunity and an ability to accomplish innovation in general?
20:14
Speaker 3: What was it like before you had a team that
20:16
Speaker 3: was so diverse.
20:18
Speaker 5: Yeah, I mean a lot of this stuff and even
20:20
Speaker 5: speaking for me personally, was done on the side.
20:23
Speaker 4: When you had time.
20:24
Speaker 5: Right, So you had an idea and it might have
20:26
Speaker 5: been a very valid, good idea, something that you heard
20:29
Speaker 5: interfacing with somebody who uses our product, Right, But you're
20:33
Speaker 5: just trying to carve out the time, like Kyle said,
20:37
Speaker 5: in the midst of keeping.
20:38
Speaker 4: The lights on and moving the needle.
20:40
Speaker 5: And keeping our products up to date and up to snuff,
20:43
Speaker 5: you're also trying to say, well, how could I incorporate
20:46
Speaker 5: this or you know, and then you're also doing it
20:48
Speaker 5: with limited knowledge, like in your own ability. Right Again,
20:52
Speaker 5: I'm a software engineer at heart.
20:55
Speaker 4: And I can. I could spin you up an LLM,
20:58
Speaker 4: train it and code out the wazoo.
20:59
Speaker 5: But at some point it's like, this isn't exactly our
21:03
Speaker 5: need right because we don't have the expertise to kind
21:06
Speaker 5: of direct our focus. So bringing them back into the
21:10
Speaker 5: offha lab is we have dedicated time to say, what
21:13
Speaker 5: are the ideas that are out there?
21:15
Speaker 4: What are we being asked for?
21:16
Speaker 5: Okay, cool, Now, let's go as a collective and jive
21:21
Speaker 5: on that. For lack of a better term, let's go
21:23
Speaker 5: and dive in on one or two high value things
21:25
Speaker 5: that we think might turn into something. And what's very
21:28
Speaker 5: neat is we might get down a certain path and
21:31
Speaker 5: say there is no value here, or we need to
21:34
Speaker 5: rethink this so that it is valuable, and we could
21:36
Speaker 5: get into the little details of what those things are.
21:39
Speaker 5: But what's cool is again, I'm not doing it on
21:41
Speaker 5: the side, Kyle's not doing it on the side, Lisa's
21:43
Speaker 5: not doing it on the side. The other folks in
21:44
Speaker 5: the team, we're not doing on the side. We're working
21:46
Speaker 5: as a group to come up with and to kind
21:49
Speaker 5: of suss out a bunch of these ideas and these
21:51
Speaker 5: asks that we have, figure out how do we make
21:54
Speaker 5: them appropriately.
21:55
Speaker 4: To be high value items, and then how do we
21:57
Speaker 4: get them to our core product.
21:59
Speaker 5: Before it was happening on the side, I guess the
22:01
Speaker 5: best way to put it or as time permitted.
22:04
Speaker 3: So the big the first one out the gate and
22:09
Speaker 3: shared with me before we started recording it was order
22:13
Speaker 3: entry and automation around order entry. Let's let's jump into that,
22:16
Speaker 3: because that's rubber hits the road. Let's talk to us
22:19
Speaker 3: about that first big implementation of AI into order entry
22:25
Speaker 3: automation and just in case someone's unfamiliar with that problem, it.
22:29
Speaker 4: Actually solves Sure.
22:32
Speaker 5: Yeah, I mean this goes back to what I was saying,
22:35
Speaker 5: and I'll allow Kyle please to elaborate on this. But
22:40
Speaker 5: when we talked a moment ago about how AI can
22:44
Speaker 5: be the assistant, not the replacement for the human kind
22:46
Speaker 5: of aspect of things.
22:49
Speaker 4: We looked and we saw.
22:50
Speaker 5: That you know, right now, as new new our ex
22:52
Speaker 5: scripts are coming in right from our em rs. We're
22:55
Speaker 5: having our texts, the people that use their software are
22:58
Speaker 5: re keying almost one hundred percent of these prescriptions right
23:01
Speaker 5: directly in the LTC. So it's taking a significant amount
23:05
Speaker 5: of time. And we said, well, if we can match
23:07
Speaker 5: these things right, and if we can eliminate some of
23:10
Speaker 5: the ri keying and the clicking and then going through
23:12
Speaker 5: the screens by auto populating some of this stuff and
23:16
Speaker 5: then having it go to them for validation, it would
23:19
Speaker 5: trim off a significant amount of time, right. And this
23:22
Speaker 5: is again, like I said, this is the part where
23:24
Speaker 5: it's like, Okay, we're not replacing people, but we're allowing
23:27
Speaker 5: them to do other stuff while the AI piece is
23:30
Speaker 5: taking care of some of the more mundane things where
23:33
Speaker 5: it's just I've got to go look in two three
23:35
Speaker 5: different areas to populate this.
23:37
Speaker 4: Why do you have to do that.
23:38
Speaker 5: Let's have the computer do that, Kyle, Do you have
23:42
Speaker 5: anything else you want to add on that?
23:44
Speaker 4: Yeah?
23:44
Speaker 6: So, for for a lot of years and now we've
23:46
Speaker 6: been able to take refills and reorders from facilities and
23:51
Speaker 6: EMRs and put them straight into batches with minimal human intervention. Right,
23:56
Speaker 6: that's how can we increase automation and reduce the clicks.
24:00
Speaker 6: That's the whole point of this. This is the first
24:03
Speaker 6: leap we've taken into doing that with new orders. So
24:06
Speaker 6: we spend a lot of time a collecting data from
24:10
Speaker 6: a few test pharmacies. What are the orders coming in,
24:13
Speaker 6: what are the data that we are getting, and what
24:15
Speaker 6: do we have to work with First, then we spend
24:18
Speaker 6: a lot of time just watching users enter orders, click
24:21
Speaker 6: for a click, counting the clicks, seeing how many minutes
24:27
Speaker 6: they spend per order, everything from choosing the drug into
24:33
Speaker 6: retyping the directions that come in on the message. But
24:35
Speaker 6: they have to hand type anyway, or they come in
24:38
Speaker 6: and doctor's language and they have to take that language
24:41
Speaker 6: and re key it with our own shorthand sig. So
24:46
Speaker 6: then we're answering the question how can we take that
24:48
Speaker 6: work and automate it? And that's what this project is
24:52
Speaker 6: is taking those those drugs, those directions and trying to
24:56
Speaker 6: get it straight into a batch, so it skips that
24:59
Speaker 6: time seeming order and train goes straight to a pharmacist
25:02
Speaker 6: for review.
25:05
Speaker 3: What's the customers saying, what are your framework LTC users?
25:09
Speaker 4: What are they saying about? This sounds great.
25:12
Speaker 6: They want Yeah, this is what they've been asking for it, right,
25:16
Speaker 6: So between eRx messages coming in and new new facility
25:23
Speaker 6: onboarding where they have to hand key one hundred patients
25:26
Speaker 6: and you know, ten or fifteen orders per patient, this
25:29
Speaker 6: is what Rick was talking about earlier when it was
25:32
Speaker 6: those literally hundreds of mundane steps that you're just doing
25:37
Speaker 6: as fast as you can. That's when you have human error.
25:39
Speaker 6: That's when you have burnout. And this is what we're
25:42
Speaker 6: trying to solve. And it's not just really trying to
25:45
Speaker 6: cut their labor right and make their jobs faster. It's
25:48
Speaker 6: also trying to take the cap off of their growth.
25:51
Speaker 4: Right.
25:52
Speaker 6: Some of our pharmacies are trying to onboard five, ten,
25:55
Speaker 6: fifteen new new nursing homes in a month, and they
25:59
Speaker 6: don't have the staff to do it. So they have
26:02
Speaker 6: to throttle their growth because they can't staff the team
26:05
Speaker 6: to hand enter the stuff. So not only are we
26:08
Speaker 6: trying to make this more efficient for the staff they
26:10
Speaker 6: do have, we're also trying to help them build their
26:13
Speaker 6: business faster.
26:14
Speaker 5: Yeah, and one other thing I'll mentioned too, because it
26:17
Speaker 5: goes back to one of the I'm not sure who
26:19
Speaker 5: said it at the beginning of the call, but you
26:22
Speaker 5: know it's reducing the potential for error too.
26:26
Speaker 4: Write.
26:26
Speaker 5: This is an area where AI is a lot more
26:29
Speaker 5: efficient parsing through data, like Kyle alluded to, than just
26:34
Speaker 5: a human. Right when you're whizzing through things, you might
26:36
Speaker 5: have a cadence down or a practice down that you're
26:38
Speaker 5: used to, but you know you're not actually maybe looking
26:41
Speaker 5: at everything verbatim. A I can do that, A I
26:44
Speaker 5: can parse it. It's trained. Its clinical great, like we said,
26:47
Speaker 5: So it helps a lot too.
26:52
Speaker 3: All right, so we're excited to hear about the order
26:56
Speaker 3: entry or all over that seem to have it nailed
26:58
Speaker 3: down clients.
27:00
Speaker 4: And make it customers like it.
27:01
Speaker 3: Let's talk about what's next and where it's on the
27:04
Speaker 3: eve of twenty twenty six, so it's always fun to talk.
27:07
Speaker 4: About what's coming up in the new years.
27:10
Speaker 3: So what other areas of framework l TC in this
27:13
Speaker 3: workflow do you see up next for AI?
27:18
Speaker 4: Yeah, I'll kick it off and then I'll letter it again.
27:21
Speaker 5: But you know, I think the biggest thing is not
27:24
Speaker 5: how do we automate one or two specific tasks or
27:27
Speaker 5: set a task, but how do we start to kind
27:30
Speaker 5: of get things in an operational flow from end to end, right,
27:34
Speaker 5: And so you have to take that one at a time,
27:37
Speaker 5: But we want to start putting those puzzle pieces together
27:40
Speaker 5: to say, hey, initially, what are we doing up front?
27:43
Speaker 5: What are the first couple of things that we have
27:45
Speaker 5: to do? And you know, one of the things that
27:47
Speaker 5: we're looking about is onboarding pharmacies. How do we go
27:50
Speaker 5: about that process?
27:51
Speaker 4: Right?
27:51
Speaker 5: That's one of the initial things in a workflow that
27:54
Speaker 5: we would go through from our software standpoint, So we
27:56
Speaker 5: want to start to build that out holistically going into
27:59
Speaker 5: twenty six and beyond. Where are the areas that are
28:03
Speaker 5: our customers and the people to use our software as
28:05
Speaker 5: well as ourselves? Say, these are pain points and we
28:08
Speaker 5: can start to automate this flow on every little step.
28:10
Speaker 5: But we can start to automate the flow and make
28:12
Speaker 5: it a lot easier more efficient. Ktty, why don't you
28:15
Speaker 5: want to jump in a little bit what we've been
28:16
Speaker 5: working on with the.
28:19
Speaker 4: Onboard Yeah, So we broke.
28:22
Speaker 6: This down into two chunks really, So the first part
28:26
Speaker 6: is where does the data come from?
28:28
Speaker 4: And how do we handle it?
28:29
Speaker 5: Right?
28:29
Speaker 6: So and that depends on the pharmacy, and that depends
28:32
Speaker 6: on the facility and what EMAAR vendor they use. You know,
28:36
Speaker 6: PCC for example, makes it easy. They blast everything through
28:39
Speaker 6: HL seven. It's accurate. You can basically flip a switch
28:42
Speaker 6: and within a day you're live. Right, There's some groundwork
28:45
Speaker 6: that has to be put into place, but it's it's
28:47
Speaker 6: pretty quick and painless. But the rest of the vendors
28:50
Speaker 6: out there aren't that aren't that efficient or that simple.
28:53
Speaker 6: Either they have a mechanism to impoort data, but there's
28:57
Speaker 6: gaps and they have to go back and fix it anyway,
29:00
Speaker 6: or there is no mechanism and they just have to
29:01
Speaker 6: print PDF forms for you know, like I said, one
29:05
Speaker 6: hundred patients or more. I've heard as high as eight
29:08
Speaker 6: hundred in some areas, and they have to hand key
29:13
Speaker 6: all that stuff. So how do we take in that data?
29:16
Speaker 6: Sometimes it's structured, sometimes as not. So we're building out
29:19
Speaker 6: systems to take everything from DSV files with just kind
29:23
Speaker 6: of thousands of rows of orders down to just scanning
29:27
Speaker 6: in PDFs that were printed and handed to the pharmacy
29:30
Speaker 6: because that's how they get the data. Sometimes, So there's
29:34
Speaker 6: varying degrees of AI that's needed in that process. We're
29:37
Speaker 6: trying to kind of stomach that. You know, what does
29:40
Speaker 6: it cost us, how accurate is it, how quick is it?
29:44
Speaker 6: All those things that that's what we're here to evaluate
29:48
Speaker 6: and then make recommendation to the core teams on what's
29:51
Speaker 6: implement And then the other side is the more I want.
29:55
Speaker 6: It's easy, but we already have most of the endpoints
29:59
Speaker 6: for this kind of work, so we know the format
30:01
Speaker 6: that we need to take a big JSON dump and
30:04
Speaker 6: get it into framework LTYC. What we're doing is how
30:07
Speaker 6: do we get it from end end right? How do
30:09
Speaker 6: we take a CSV file? How do we take a
30:11
Speaker 6: PDF and just zap straight into into LTC for a
30:16
Speaker 6: new facility onboarding And that's what we're working on now
30:19
Speaker 6: and then in the future we're still mapping that out,
30:23
Speaker 6: but there's a lot of ideas from a lot of
30:25
Speaker 6: pharmacies out there. Inventory management could be a topic that
30:28
Speaker 6: comes up for us in the near future new pharmacy onboarding,
30:33
Speaker 6: whether it's converting data from other pharmacy systems or just
30:39
Speaker 6: the whole onboarding process of setting up our pretty complex
30:42
Speaker 6: web of facility and system options and how do we
30:46
Speaker 6: make that process faster, so we're looking at a bunch
30:48
Speaker 6: of different things.
30:52
Speaker 3: Earlier, you commented Kyle on keeping the client in check
31:00
Speaker 3: and keeping them a part of the roadmap of what
31:04
Speaker 3: you're doing with Framework LTC. And you also were the
31:08
Speaker 3: very first organization to put together a bunch of long
31:12
Speaker 3: term care pharmacy operators in a think tank. I think
31:15
Speaker 3: this might have been eighteen months maybe two years ago
31:19
Speaker 3: where you can't got everybody together in Pittsburgh and it
31:22
Speaker 3: was an AI forum per se. It was awesome, so
31:26
Speaker 3: I actually we were there.
31:28
Speaker 4: I loved that. It was fun. So talk to us
31:30
Speaker 4: about what's the how.
31:32
Speaker 3: Does framework users how do they help to shape the
31:36
Speaker 3: AI roadmap of what you guys are planning to do?
31:40
Speaker 6: Well, I mean, they're the essential driving force for what
31:45
Speaker 6: we're choosing to undertake here, so we could kind of
31:48
Speaker 6: to Rick's point, not all of us have the background
31:52
Speaker 6: having worked in pharmacies for our adult lives, right like
31:55
Speaker 6: I have. I've been here for a couple of years,
31:57
Speaker 6: Rick's been here for a year, Lisa's been around for
32:01
Speaker 6: decades quite literally, so like we have varying degrees of experience.
32:05
Speaker 6: But even that being said, none of us actually work
32:09
Speaker 6: in pharmacies. Right, we work for softwriters. We're working on
32:11
Speaker 6: the software. We're not in the pharmacy doing the day
32:15
Speaker 6: to day grinds. So those are the people that need
32:17
Speaker 6: to tell us what the pain points are, what they
32:20
Speaker 6: want to automate, and in what they don't. Otherwise we
32:24
Speaker 6: just kind of be guessing, you know best, in our experience,
32:26
Speaker 6: and frankly, we need them to tell us what to do,
32:30
Speaker 6: and it's it's our jobs to take that feedback and bring.
32:33
Speaker 4: It to life. And what's cool about that is now
32:36
Speaker 4: with Alpha Labs, right, we have a place that that
32:39
Speaker 4: stuff goes to directly. Right, It's not.
32:43
Speaker 5: Going into a larger hoppa where it's a core product
32:45
Speaker 5: improvement that hey this needs fixed, right where we need
32:47
Speaker 5: to address the speed at which this operates. This this
32:50
Speaker 5: new creative stuff that folks are asking for that Kyle mentioned,
32:54
Speaker 5: and they say, hey, I want to automate this, I
32:55
Speaker 5: want to fix this field, I want to I want
32:58
Speaker 5: to go in and see if this can be expedited
33:01
Speaker 5: in some certain way. Right, all that stuff goes into
33:04
Speaker 5: the hopper for the Alpha team. This is all stuff
33:06
Speaker 5: we're prioritizing and looking through and communicating back with people saying,
33:10
Speaker 5: you know, we're looking at this, we're working on this,
33:11
Speaker 5: and it's you know, we're going to figure out the
33:13
Speaker 5: best way to approach this and then have people engage
33:16
Speaker 5: with us as we as we produce it, put it
33:18
Speaker 5: out onto pocs, have it tested, and eventually incorporate.
33:22
Speaker 4: Into the core product.
33:26
Speaker 3: All right, what about kind of like from your perspective,
33:31
Speaker 3: and it's the margence of what's possible to what you
33:37
Speaker 3: see the client, the framework, user, their input and all
33:41
Speaker 3: of a sudden you release it. That must be really
33:44
Speaker 3: be exciting to you because you see something actually making
33:46
Speaker 3: an impact. It's impacting people's lives, it's impacting your operations.
33:50
Speaker 3: Most importantly, it's impacting the patients they are being served
33:54
Speaker 3: at these facilities. So talk to us that about personally
33:58
Speaker 3: in the future at soft Writers and AI.
34:01
Speaker 4: What what what excites you?
34:02
Speaker 3: What's coming up that you can even sprinkle in some
34:06
Speaker 3: secrets as long as Scott Baty doesn't get mad at
34:09
Speaker 3: us all for the secrets.
34:12
Speaker 4: Tell you any secrets. But I.
34:15
Speaker 5: H know, I get excited about a lot of different things.
34:18
Speaker 5: Kyle will attests to that, and sometimes I go off
34:20
Speaker 5: onto all kinds of weird tangents.
34:22
Speaker 4: You know, AI excites me just in general.
34:24
Speaker 5: I think what's cool is what's cool, I'll put it
34:27
Speaker 5: this way, is that everybody has the ability to start
34:30
Speaker 5: to design things, even kind of like indie developers, maybe
34:34
Speaker 5: not even affiliate I'm talking more generally, not just as
34:36
Speaker 5: soft writers, but everybody has the ability now to do
34:39
Speaker 5: things and create things a lot quicker, leveraging AI for
34:43
Speaker 5: design tools and coding and so on and so forth.
34:47
Speaker 4: And that's really great.
34:49
Speaker 5: And there will be a lot of cool innovations that
34:51
Speaker 5: come out of it in the coming years as things mature.
34:54
Speaker 5: But what's cool about softwriters, and what I think excites
34:57
Speaker 5: me the most is that that term we keep you
35:00
Speaker 5: using is its clinical grade. And you know, some of
35:03
Speaker 5: the auto population of fields, the reduction of clicks, the
35:07
Speaker 5: way that we automate processes, the ocr that we're using
35:10
Speaker 5: for some of the onboarding stuff that we talked about,
35:13
Speaker 5: that's all been hardened and validated and scoped, and we
35:18
Speaker 5: are going through and making sure that there's actual value
35:21
Speaker 5: being provided to our customers and also ultimately the patients.
35:24
Speaker 5: Right Like you mentioned, that's what excites me the most
35:27
Speaker 5: is that there's going to be a wave of development,
35:29
Speaker 5: new things coming along. But I would also encourage people
35:32
Speaker 5: to understand it. Look at softwriters, look to us for
35:37
Speaker 5: very very quality, hardened, clinical grade stuff, and you're going
35:41
Speaker 5: to see a lot of innovation coming out. I guarantee
35:43
Speaker 5: you with the twenty twenty six twenty twenty seven time frame,
35:46
Speaker 5: it's going to be great.
35:51
Speaker 3: All right, Kyle, We're going to be wrapping up. So
35:53
Speaker 3: you get the last kind of question here, and that is,
35:57
Speaker 3: you know, we think of our long term care pharmacy
36:00
Speaker 3: leaders out there, the ones that are like.
36:03
Speaker 4: The super users.
36:04
Speaker 3: You think of the clients that are really making impacts
36:07
Speaker 3: in their community because of the services that they're assuring
36:11
Speaker 3: that public health is taken seriously.
36:14
Speaker 4: It's their life, it's what they deliver.
36:16
Speaker 3: But there's lots of people that are like, I'm not
36:19
Speaker 3: exactly sure where to begin with them. You'll hear tons
36:22
Speaker 3: of your users probably say things like I'm old school,
36:25
Speaker 3: like I'm going to do it this way, I'm going
36:26
Speaker 3: to do it this way, But in their minds, they
36:28
Speaker 3: are thinking the same way. AI is preparing them to
36:32
Speaker 3: be able to leap frog, or instead of doing one thing,
36:35
Speaker 3: they're going to be able to do four things. But
36:37
Speaker 3: it's hard to get your mind around that sometimes. So
36:40
Speaker 3: what's one practical tip you can give long term care
36:43
Speaker 3: pharmacy operators who want to adopt AI but they're just
36:46
Speaker 3: not sure exactly where to begin.
36:49
Speaker 6: I think a lot of people when they think of
36:52
Speaker 6: what is AI, they think Chad GBT. Right, So they're saying, well,
36:56
Speaker 6: what's this going to do for me?
36:57
Speaker 5: Right?
36:58
Speaker 6: I still got to put in these orders? Can just
37:00
Speaker 6: tell Jock GBT to discharge patients for me? Right? So, Like,
37:04
Speaker 6: I don't think they understand at a broad level what
37:09
Speaker 6: we can do behind the scenes and under the hood,
37:12
Speaker 6: and that's where we come to play it to do
37:14
Speaker 6: that for them, right. So one of the things that's
37:17
Speaker 6: most exciting to me about this is with my operational background, right,
37:22
Speaker 6: my brain automatically goes to what tangible difference does this make?
37:26
Speaker 4: Like why are we doing this? Right?
37:27
Speaker 6: So if we can tell a pharmacy hey, we're gonna
37:31
Speaker 6: create a tool that's going to turn eighty hours of
37:33
Speaker 6: work into twenty five, it's tangible, it's black and white,
37:38
Speaker 6: it's it's something we're doing for them, right. And it's
37:41
Speaker 6: the part that it excites me the most too, is
37:43
Speaker 6: that both soft writers and our pharmacies, and nobody's asking
37:49
Speaker 6: for this is a way to cut jobs or shrink
37:52
Speaker 6: their labor force. Right. That's what most people are probably
37:55
Speaker 6: the most worried about, right because they'll look, Meta, just
37:58
Speaker 6: cut ten thousand jobs, right, ask for all of us.
38:01
Speaker 6: That's what people think. But that's not the case on
38:05
Speaker 6: our side, or at least any of the customers we've
38:07
Speaker 6: talked to. Right, we're trying to unlock the door for
38:10
Speaker 6: pharmacies and take in more business. We're not trying to
38:13
Speaker 6: shrink their staff. We're trying to increase their output. And
38:17
Speaker 6: then same thing with software is right, I mean, I
38:19
Speaker 6: don't want to give you any secrets, like you said,
38:21
Speaker 6: makes got be mad, But we have a big enough
38:24
Speaker 6: backlog of things we need to do with our software
38:28
Speaker 6: right in framework LGC and across our whole suite where
38:32
Speaker 6: it's going to take us a long time to catch up, right,
38:34
Speaker 6: So we're not going to replace any jobs anytime soon.
38:37
Speaker 6: And I learned that pretty quickly. But if this can,
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Speaker 6: if this can come into play and help us create
38:43
Speaker 6: new solutions that have a tangible impact and then unlock
38:48
Speaker 6: the you know, the capability for us to put out
38:52
Speaker 6: five new features instead of three. That's the goal, right,
38:55
Speaker 6: It's just going to accelerate our products and separate us
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Speaker 6: from competitors. It's not replacing anybody, and that's that's been
39:03
Speaker 6: the most refreshing part for me through all this.
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Speaker 4: I love this.
39:09
Speaker 3: Thank you so much both of you for sharing today.
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Speaker 3: Rick Kyle, this has been a great episode of Framework Focus.
39:17
Speaker 3: We appreciate your insights and the impact of AI in
39:20
Speaker 3: long term care pharmacy.
39:21
Speaker 4: Thank you appreciate it.
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Speaker 6: Thanks Tom.
39:23
Speaker 3: Framework Focus is a podcast that explores trends, innovations, and
39:27
Speaker 3: insights into long term care pharmacy. It's Framework Focus, the
39:31
Speaker 3: podcast for everything long term care pharmacy.
39:33
Speaker 4: If you are not a Framework.
39:35
Speaker 3: User, take a look at the system just in comparison
39:39
Speaker 3: to where you're at. See if this can't help you
39:41
Speaker 3: leap frog and start to grow your business. Lean against
39:45
Speaker 3: the consultancy as part of the team. Reach out to
39:49
Speaker 3: the team at Framework HEALTHC. Go to frameworkaltc dot com.
39:53
Speaker 4: Ask questions.
39:54
Speaker 3: So just see if this system isn't what's best for
39:57
Speaker 3: you and your operations and moving forward more than the
40:00
Speaker 3: future of long term care pharmacy. Twenty twenty six, Here
40:04
Speaker 3: we come