In this episode of the Informonster Podcast, Charlie Harp sits down with Patina Zarcone, Director, and Millie Malai, Technical Project Manager of Datapult, an APHL company. They discuss how Datapult and Clinical Architecture came together to create a system that would get lab data to public health faster, especially during the pandemic. They discuss the impact of this system on the timeliness of data for emerging infectious diseases, and how converting data from CSV to HL7 v2.5.1 was a game changer to ensure that public health received the highest quality data.
About Patina Zarcone
Patina (Zarcone) Gagne serves as the Association of Public Health Laboratories’ (APHL) Director of Datapult, a new APHL company, and has been with APHL approaching 21 years. When joining APHL in 2003, Patina was the only informatics staff member focused primarily on helping governmental public health laboratories acquire laboratory information management systems. Today, she is running Datapult, a company that made a major impact on the pandemic by reporting COVID laboratory results electronically to public health. Prior to her tenure at APHL, Patina worked first in biotechnology as a bench scientist in gene therapy for Biogen Inc., in Cambridge MA then went on to work for the Massachusetts Department of Public Health, State Laboratory Institute helping run operations for the mosquito borne disease surveillance program.
Patina has an undergraduate degree (BA) in Biology from Boston University and a Master’s Degree in Public Health (MPH), specializing in Health Service Administration also from Boston University.
Patina lives in Newbury, MA right next to the Atlantic ocean with her husband, Joel Gagne Esq. and 4 children.
About Millie Malai
Millie is the Technical Project Manager for Datapult and oversees technical development and implementation across Datapult’s service, including Expanded ELR. She has been in the field of public health informatics for over a decade, with experience in syndromic surveillance systems, laboratory information systems, immunization registries, electronic laboratory reporting, and health data interoperability. She holds a Master of Public Health with a concentration in applied public health informatics from Emory University, Rollins School of Public Health.
Contact Clinical Architecture
• Tweet us at @ClinicalArch
• Follow us on LinkedIn and Facebook
• Email us at informonster@clinicalarchitecture.com
Thanks for listening!
In this episode of the Informonster Podcast, Charlie Harp sits down with Patina Zarcone, Director, and Millie Malai, Technical Project Manager of Datapult, an APHL company. They discuss how Datapult and Clinical Architecture came together to create a system that would get lab data to public health faster, especially during the pandemic. They discuss the impact of this system on the timeliness of data for emerging infectious diseases, and how converting data from CSV to HL7 v2.5.1 was a game changer to ensure that public health received the highest quality data.
About Patina Zarcone
Patina (Zarcone) Gagne serves as the Association of Public Health Laboratories’ (APHL) Director of Datapult, a new APHL company, and has been with APHL approaching 21 years. When joining APHL in 2003, Patina was the only informatics staff member focused primarily on helping governmental public health laboratories acquire laboratory information management systems. Today, she is running Datapult, a company that made a major impact on the pandemic by reporting COVID laboratory results electronically to public health. Prior to her tenure at APHL, Patina worked first in biotechnology as a bench scientist in gene therapy for Biogen Inc., in Cambridge MA then went on to work for the Massachusetts Department of Public Health, State Laboratory Institute helping run operations for the mosquito borne disease surveillance program.
Patina has an undergraduate degree (BA) in Biology from Boston University and a Master’s Degree in Public Health (MPH), specializing in Health Service Administration also from Boston University.
Patina lives in Newbury, MA right next to the Atlantic ocean with her husband, Joel Gagne Esq. and 4 children.
About Millie Malai
Millie is the Technical Project Manager for Datapult and oversees technical development and implementation across Datapult’s service, including Expanded ELR. She has been in the field of public health informatics for over a decade, with experience in syndromic surveillance systems, laboratory information systems, immunization registries, electronic laboratory reporting, and health data interoperability. She holds a Master of Public Health with a concentration in applied public health informatics from Emory University, Rollins School of Public Health.
Contact Clinical Architecture
• Tweet us at @ClinicalArch
• Follow us on LinkedIn and Facebook
• Email us at informonster@clinicalarchitecture.com
Thanks for listening!
Charlie Harp:
Hi, I am Charlie Harp, and this is the Informonster Podcast. Today on the Informonster Podcast, I've got Patina Zarcone and Millie Malai from APHL Datapult. And we're going to talk a little bit about Datapult and some of the work that Clinical Architecture and APHL Datapult have done together. But I'd like to start out by introducing first Patina. Patina, tell us a little bit about yourself.
Patina Zarcone:
Thank you, Charlie. And thank you for inviting us today. My name is Patina Zarcone. I am the director of Datapult, which is a new APHL company. I have been with APHL for almost 21 years. Prior to my work now with Datapult, I was the director of APHL's informatics program, building that up over the 17 years or so that I was in that position and now with Datapult and helping to move the company forward. I'd like to turn it over to Millie.
Millie Malai:
Hey there. I am Millie Malai. I'm the technical project manager for APHL Datapult. I've been with APHL for just a couple of years. But before that, I was working in consulting, doing a lot of public health informatics projects probably for the last decade. So this is my passion. I love public health informatics, electronic laboratory reporting, immunization registry, all of that good stuff, and I'm so glad to be here.
Charlie Harp:
Thank you guys very much. So why don't we start out for people that may not be familiar with APHL and Datapult, if you guys could kind of give us a history of the organization and what you guys do, that'd be fantastic.
Patina Zarcone:
Sure. I can start and then I'll ask Millie to add in any blanks for me. So Datapult is a new company, as I had mentioned earlier, we are a wholly owned subsidiary of APHL. The biggest difference between us and the rest of APHL is we are fee-for-service. So we are funded by the fees that we bring in from our various services. Back in the beginning of the pandemic in June of 2020, there was laboratory reporting guidance that came out from HHS and it went into detail as far as the data elements that were required for reporting Covid laboratory results to the public health agencies across the country. And as part of that guidance, the AIMS platform, which is an acronym for APHL Informatics Messaging Services, this is the cloud platform that APHL and Datapult utilize for our projects, was named in that laboratory reporting guidance as one of the three ways that private laboratories stepping up to test for Covid could report those laboratory results to the states.
So after we quickly began to realize that we had to set up ourselves for being able to help private laboratories across the country report this critical data to public health. We officially started our business with centralized electronic laboratory reporting for Covid only at the time, and we have since then moved on, which is, I think, what we're going to talk about today with our laboratory reporting services to expand out from Covid only to all of the nationally notifiable diseases. And we do this based on state reporting guidelines. So looking forward to getting into that a little bit more, but wanted to give you a brief history of Datapult and who we are. And I'd like to ask my colleague, Millie, if she wanted to add anything to that.
Millie Malai:
I think what I would add is prior to the pandemic, right? APHL was already thinking about, "How do we provide more support to public health when there's actually not a lot of funding for these extra projects that we knew were necessary," right? The way that the public health field sort of works is there's a government entity that says, "These are the priorities that are important to us," or maybe it's a state entity that says, "These are our priorities," and then funding is available for that, but then it doesn't cover everything.
And so I know that before the pandemic, APHL was already thinking about developing a new company or some sort of entity that would fill some of those gaps or bridge some of those gaps. And it just so happened that there was a lot of thinking that went into what would that business model look like. And then the pandemic hit, and it just naturally became obvious that the thinking that was done for this new entity was the perfect entity to help with this lab reporting that was now required. But there was actually no funding to stand up the lab reporting. And I was thinking... What's the word? serendipitous that a lot of the thinking and the planning had already started when then an emerging need came up, and so we kind of married the two, right? Would you say that's kind of accurate, Patina?
Patina Zarcone:
Yes, it is.
Charlie Harp:
I think it's interesting that Covid, the pandemic really I guess shone a light on how underfunded public health was. And it did it for a couple of things. It shined a light on the fact that we weren't investing in public health to the level that we should, and from my perspective, it also did the same thing for data quality that we weren't collecting data and handling data in a way that allowed us to really leverage it in meaningful ways when it comes to things like surveillance and things like that. So it's one of those things where something good comes out of something bad. And ideally, we will learn something from this and be better about how we do things. And I think what you guys are doing at Datapult is one of those good things that came out of something terrible that will allow us to be better prepared in the future.
Millie Malai:
I definitely agree with you. There are a lot of opinion articles that came out after the pandemic, like you said, shined that light on the current state of the infrastructure across what could be done. And like you said, I think public health practitioners and epidemiologists had been saying this stuff for years, but when it's not an emergency, sometimes nothing happens.
Charlie Harp:
I mean, it's not like there weren't 12 movies made on the subject.
Millie Malai:
The movies aren't based on reality, right? Covid, like you said, something good coming out of this tragedy. One of the good parts is there's now evidence that the public health community can come up with a uniform set of requirements, right? We had to have that during Covid. That is actually what makes a centralized reporting hub like our service Expanded ELR work. We also have evidence now that every public health jurisdiction is capable of receiving an electronic laboratory report, whereas before that, it wasn't always the case. So I don't want to say benefits, but there are enhancements or improvements that we had to quickly help the public health community deploy that is now the foundation for what we can do now. And I don't really see the public health community going backwards to paper or trying to manage a CSB file, a clunky file, actually can just move forward. And that's what we're trying to do with the Expanded ELR service, which is take the point that we got to with Covid and then advance it forward.
Charlie Harp:
Absolutely. So we've got this new plumbing that we built because we needed it for Covid, but it just so happens that this same plumbing can be used for anything that we really want to monitor and surveil at scale when it comes to lab data. Yes?
Patina Zarcone:
Yes. I would say that's definitely the case. I mean, there's only, I think, one big thing that could pull us all backward, and I think you mentioned this earlier, Charlie, but it's the funding. Many of our public health laboratories and agencies, the public health system utilizes information systems of various sorts to help them do their work. A lot of these systems have more recently been helping them improve data quality to both your points earlier as well as just allows them to help, I guess, augment, if you will, their workforce. If funding were to be pulled or not available to continue to support those systems, then we would be in a difficult place.
Many of our public health system partners that are doing the testing such as the laboratories or the epidemiology such as the agencies, really, they don't have access to people, the skill sets that are required to do this work, especially if you don't have systems to help enable it. And so we're just really hoping in kind of the aftermath of the Covid epidemic that this will continue to be known, to be heard, and these systems will continue to be supported into the future.
Charlie Harp:
Let's hope so. So around the time kind of in the early days of Covid, when this all started and this kind of when you guys and Clinical Architecture, we kind of came together. What was it like and what was the process that you guys experienced? Because obviously being mandated or being set up as one of the three people that people could use to report this information, obviously you guys probably were in a bit of a scramble, would be my guess when that happened. Is that not the case?
Patina Zarcone:
Indeed.
Millie Malai:
We were just surprised to be named.
Patina Zarcone:
We were very surprised. And yes, to your point, we were definitely scrambling. I mean, APHL and APHL Datapult, we are deeply steeped in public health. And being given this position was a great honor, but it was also definitely a last minute, of course, everything is in a pandemic, everybody's scrambling to do the best they can. And so yes, we had to deploy very quickly, and we had to really sit down and say, "How are we going to do this the best way for public health?" And that is when we had the pleasure of meeting you, Charlie, and your team at Clinical Architecture. And we were really, I think both companies, able to come together and do something really good.
And, of course, that started out with the work that we did together around transforming CSV into the format that public health would accept, which was HL7 v2.5.1. So that was actually really a game changer for a lot of private laboratories that still weren't able to create HL7 out of their systems. And this just CSV simply put was not a format that our agencies would accept. So that was a lot of good work, Charlie, in the beginning that we did together and we continue to do that work today with this good work with this Expanded ELR project.
Charlie Harp:
No, I appreciate it. We love working with you guys. We work a lot with IDNs, and we're working with folks like the CDC, and we just started working with you guys. And when Covid hit, a lot of our IDN clients, they were still leveraging the stuff that we provide, but a lot of the projects we were working on just stopped because they were overwhelmed with everything they had to deal with with patients and organizing their facilities. And what we tried to do is we looked for opportunities where we could help. So we did some stuff with the Covid vaccine, lot numbers, getting involved with you guys. It wasn't just a good project for us to work on and work with a great partner like APHL Datapult, but it's also one of the things I love about healthcare is that you're doing something that you feel is important.
And so we've been very grateful to have the opportunity to work with you guys because we feel like what you do is very important, and we like being part of important things, I guess. So when it comes to the Expanded ELR, for the people that may not be familiar with it, can you guys talk a little bit about what it is and what the purpose of the Expanded ELR is?
Millie Malai:
Sure. Definitely. So like we mentioned before, Expanded ELR takes our lab reporting system that we developed for Covid and enhances it and expands it. So every laboratory that does testing predominantly for infectious disease tests, it's likely that it needs to be reported. So in the United States, CDC maintains a list of nationally notifiable diseases, NNDs, and there's approximately 90 plus about 100 then. And to diagnose and to confirm a case of flu, let's just say, you might have to conduct a confirmatory test. Well, because of that, a lot of public health agencies and public health departments, they will require you to report test results for certain conditions that you might be trying to diagnose. So shorthand, we call that NND ELR, so for electronic laboratory reporting for those tests.
And then every jurisdiction has their own flavor of what is reportable. So while CDC might maintain, "Hey, these are all the nationally notifiable diseases," the reporting that each state or each public health jurisdiction might do to CDC is anonymized, maybe sometimes aggregate of "These were all the positive cases that we had for syphilis," or, "These are all the positive cases we had for dengue," or whatever the reportable condition is. But each state needs that patient level, all of the data for that test. But they all want something a different way. It's not just if you have a positive test results, send it. For something like HIV, they want all the results, any of the results. And also if you do a screening, they want the results for that, which is not necessarily a diagnostic test. Hepatitis C, which is a comorbidity of HIV, has similar, we want all the results, or some jurisdictions say, "No, we just want the positives. If it's a panel, we want all the tests, if at least one test on the panel is positive," right?
So there are all these intricate nuances to how each public health jurisdiction would like the test result reported by a lab. And you can imagine that if you are a regional or a national lab, which a lot of our customers for our Centralized ELR are, it gets very burdensome to manage, "Which state wants which type of test and also which type of result?" And we knew that there was this huge opportunity to take that foundation that we built for this centralized lab reporting, add the logic to it, and make it a lot easier for the laboratory to report without having to spend all this time and resources to figure out what they needed to do.
So Expanded ELR in a nutshell contains all of that logic and the routing rules. And with one connection, a laboratory can achieve everything that they would have to pay other staff to do to get it to the right jurisdiction. That's the nutshell of it. It's a lot more complicated, and Clinical Architecture was just the natural partner for this because of your inferencing suite, I guess you could say. I think it's called the Advanced Clinical Awareness Suite. So it just made sense that you all were already in the terminology space and you had inferencing, and it was a great fit for what we wanted to achieve for public health with our service.
Patina Zarcone:
Charlie, one of the things that we joke about sometimes when we're all scrambling around and overwhelmed is electronic laboratory reporting in and of itself, I guess I'll say this a little bit in jest, seems easy. But just hearing what Millie said, and knowing the process that we all had to go through to get where we are today, we can see it is not easy. I think we probably would've been doing this a long time ago if it was easy. So really being able to harness the skills and the expertise of your team along with ours, we really have done something here that although the idea in and of itself maybe a theoretical level has been around for decades, in practice this has not been done. So this is a major contribution to public health, and we were really happy to work on it with you guys.
Charlie Harp:
Well, we were too. I've been developing systems in healthcare for a long time, and one of the things that's challenging is innovation, and going after something new kind of dragon sling is a scary business because you have to find people that are willing to kind of put their passion and their ideas and go after something that's different from the status quo. And one of the great things about the partnership between Clinical Architecture and Datapult is you guys were one of our relatively early adopters on Pivot. And a lot of this stuff the Clinical Architecture does is about removing repetitive work or burdensome work that doesn't need to be done over and over and over again by human beings and letting software do the work or doing it centrally. So Symedical was all about taking things like data quality and mapping, and take it off of the backs of clinicians and spreadsheets, and let the software do the lion share of the lift.
Pivot was all about us building these transformations from formats in a central place and providing it to all of our clients instead of everybody hiring developers to write interfaces for the same variance of formats. And inferencing is really about creating logic that can be leveraged in a reusable, encapsulated way instead of having everybody go off and do this. And I think Millie articulated it really well. The nice thing about what we are doing together is that instead of everybody dealing with how to format things, and everybody dealing with how to write their own logic to handle these NNDs, what we've done working with you guys and really what you've done working with us, because I put most of the work on you guys because you have to figure out the best way to approach all this stuff, is create that kind of, let's call it the lab reporting easy button. So here's the beauty of it. It wasn't easy for us to do what we did, but because of what we did together, it could be pretty easy for everybody else.
Patina Zarcone:
Exactly.
Millie Malai:
Yep. That's exactly right.
Charlie Harp:
And I get that all the time. Even when it comes to terminology, you run into people like, "Terminology is easy." I'm like, "Well, you could fool me. Because we spend a lot of time and energy dealing with terminology. It doesn't feel easy to me." But I think that open heart surgery looks easy, not really. So I also really appreciate what we've done, and I'm very excited to see where it goes because once again, it's one of those things where, because you guys have articulated, "Here's how we need to do these things," and you guys have an amazing reputation in your space, we're very grateful also to have you guys work with us, make our products better, help us look at ways we can scale things and do things that are a little bit different. And so I think it's been a great partnership and we're very excited about it. So what's next for you guys? So we've done what we've done, but for Datapult, what does the future look like? What do you guys want to be able to accomplish in the next couple of years?
Patina Zarcone:
I'll start, and Millie can add. There's a lot of, to Millie's point earlier about the way kind of the grant funding works, and there are very specific projects, which are all amazing projects, and absolutely need to get done. We are here for the other pieces, and there's a lot of need in public health, especially around information technology and informatics and data standards. I think the three of us, we sit here, we understand what all of these words mean, we understand the process, and a lot of people in public health don't because it is complex, it's complicated. And so moving forward, there are a lot of great opportunities to continue to be supportive of public health, especially in those domain areas.
One of the exciting projects that we have going on now is centered around laboratories, both public health laboratories and other laboratories around next generation sequencing. And we have a suite of services that we call InSight. And InSight is geared toward helping laboratories manage pipelines for next generation sequencing, whole genome sequencing. And so we are working with many laboratories across the country who are evaluating this. A big part of the CDC's data modernization initiative is building these systems obviously for preparedness, but also this cutting edge science, especially around sequence work in the laboratories. And so this is much like data standards. It's a very complicated area in which public health, I'll speak particularly to the laboratories have to work in and data modernization initiative is there to support them in bringing on systems to help them with this work.
And so we are right now in the process of rolling out that suite of services and we're really excited. I think we're really hopeful that the work that we're doing in that area will be helpful. These systems within the next generation sequencing space take specific skill sets to be able to use. And a lot of our public health laboratories don't have access to bioinformaticians. So being able to have, and I'm doing air quotes here, easy system to help them with this work is just critical because most of our states don't have access to bioinformaticians. So that's one of the spaces we're in right now, and looking to move that forward with our laboratory community. Millie, did you want to-
Millie Malai:
Yeah. Well, we're not done with Expanded ELR, and it may come as a surprise. But laboratory reporting is something that I'm really excited by and I'm excited about it. And so yes, what's coming up for the future of Datapult? I think we're going to learn more about how we can complete this ELR service that we started, and we're going to continue to change it to then be this complete package for public health. That's kind of our vision with Expanded ELR. We're not going to one and done it like, "All of the NND rules are in there. Good, we're done." What we've already seen is, "Hey, what do we do when there's no standard code for a test? Well, let's figure that out." We have all the original experts in terminology, right? With Carol McCumber on your side and Ricky Merrick on our side to come up with, "How do we want to deal with scenarios where there's not already a cut and dry specification?" So there's already that.
We also have jurisdictions that have rules that aren't NND. That's a special testing that they do that they want the results for. Well, let's incorporate that. There's probably something else. A different standard is going to be published. So a different version of HL7 or even fire might be something that the jurisdictions can finally ingest into their systems. So there's always going to be something to do with Expanded ELR, and I think that's going to be one of the priority developments or releases that will have to make our service a service that covers whatever's needed for ELR, I could say that.
And I also wanted to just mention that I think Charlie, or was it Patina? You mentioned the systems or the goals for DMI, data modernization initiative, are to be responsive during the next emergency. We've seen when there's an emerging epidemic or a emerging infectious disease, how what we've already set up can pivot very quickly. So when mpox became an epidemic and public health really needed to get all the test results for anybody that was testing for mpox or... What is it called? non- variola Orthopox, right? They needed a clear view. Well, the laboratories that were sending Covid that could also do mpox testing, they just added it to what we already had set up.
And so with Expanded ELR, it'll be the same. If an emerging infectious disease comes up, we need to quickly get those results. The laboratories, if they're already connected to us, could just start sending it. And really the goal for DMI is to get the highest quality of data quickly to public health so that they can start doing their investigations and come into their insights a lot more quickly. So we just really feel like the more that we could strengthen Expanded ELR to different use cases, the connections are already there and it's pretty, I don't want to say instantaneous, but it would nearly be a week, not months to get that data flowing to public health.
Charlie Harp:
Well, because I remember when that happened and the time it took us to get things prepped and to get the inferences, if I remember correctly, it doesn't take long for us to do that. So we can be very responsive.
Millie Malai:
Right.
Charlie Harp:
So not long after Covid, I think it was in 2020, I spoke at a virtual HITEC meeting. And one of the things that I brought up, and it was kind of drowned out because everybody was focused on telehealth at the time, but it was this idea that one of the things we were seeing across all of our clients was that there was nobody that was providing guidance on the semantics of Covid testing. So Victor found 72 ways that people would say positive for Covid. And there were 15 different ways that people were describing the test for Covid. And what I was kind of saying is it'd be nice if there was an organization that was responsible for saying, "Hey, we know there's not a standard LOINC code, we know there's not a standard code, but this is what you should call it because when we go to map things or go to align things, it's a lot easier if we're taking the same approach and we're using the same semantics even if we don't have a common LOINC code or something prepped by the standards groups already."
I think for you guys are in a perfect position to do that. And what I would love to do is let's hope it never happens, but if something like that happens and you guys say, "Well, for public health, this is what we're going to call it." And Clinical Architecture could reach out to all of our non-public health clients and say like an alert, "This is what you should call it. When you set up the test in your local test master dictionary, call it this because it'll be easier to recognize and maybe we could orchestrate something like that."
Millie Malai:
Right. And that's exactly... Well, I wouldn't say exactly, but that is how it happened when something new came up. The tiny tweak that I would offer is we do the thinking and we say, "This is what you should call it." But every jurisdiction has to agree to that. Nine times out of 10 they do. But then our job, right? Because we are seeing this on a day-to-day basis, is to gain that consensus. So we offer the recommendation based on our expertise and get the consensus from the public health community. But that's how we've been operating with all the different tweaks during Covid like with school reporting and then over the counter reporting, that's how we did it. And I think that's a great way to move forward as well with our organizations being those experts that offer those recommendations to public health.
Charlie Harp:
Absolutely. So one of the things I like to do, and if you guys don't mind, I would like to ask each one of you, kind of what got you into health informatics? Do you remember the moment that you kind of stepped into this world? I mean, I remember it very clearly for myself, but what was the moment and what was it that got you into health informatics? When you were five years old, you said, "Someday I want to grow up and do electronic lab reporting"? What happened?
Patina Zarcone:
Not quite. So before my time at APHL, I was actually working at the Massachusetts Department of Public Health within their state public health laboratory. During my tenure, I should say, the events of 9/11 occurred. And I am sure as many people do, I remember exactly where I was, I remember seeing the TV and everything going on, and then just rushing to the laboratory. And it was then that the laboratory started to receive thousands of various household goods for testing for anthrax, some things that were legitimate, a lot of things that perhaps weren't, but we were still mandated to test everything at the time. And as you can imagine, the data coming from just the sheer amount of testing that was happening, it was profound. It was a lot of data. And back in 2001, certainly to my knowledge in the public health space, it was probably happening, but I wasn't quite there yet, but data standards were perhaps just becoming important or beginning to become important. This was in 2001.
So I took on with a couple of other folks the responsibility of getting the data from the anthrax testing to in a format that could go up to the governor at the time. And just remember the days and the nights of Excel spreadsheets, and typing in, making sure various lab results got transcribed correctly, and having to do different reports to report on the status of each of those specimen that came in. And it was sheer madness and overwhelming. And so I had, at that point, talked to the lab director and we all kind of set as a group, "We need to have a better way to do this. Access databases and Excel spreadsheets are great, and this is what we have right now, but there's got to be a better way." And so a group of us got into looking at various systems for the laboratory that could help in the event that ever happened again. And if it could help us at the present time, that was great too. But we all know how long it takes to implement systems.
So that was really my starting point, Charlie, was at the public health laboratory being in the trenches and being someone who had to deal with that data. And prior to that, I was dealing with arbovirus data getting over to the CDC. So had a very clear picture of how difficult all of that was. And so I wanted to make it better, I wanted to go into a career that really focused on those things. It was overwhelming, but it was exciting and it was fulfilling at the time. So that was my start.
Charlie Harp:
Great.
Millie Malai:
Thank you for this question because I love talking about things that I love. I can't remember exactly when, but through my consulting career, I was doing an assessment project of a public health registry, and we were trying to find out what the non-technical barriers were to the data quality. But I think during that time, so maybe it was like 2014 or 2015, data quality just made so much sense to me. And in my mind I thought, "I get this. I get why we need data quality." And my personality is I'm a problem solver. So I'm the type of person, if you tell me your problem, I'll try to fix it for you. So that's something that I'm sensitive about. But the problem statement is, "How do we get the best data quality to public health?" Well, that just really energizes me. And I think that's during that project I thought, "I want to keep doing this type of work."
And back then, we didn't really say the word informatics. I think just in the last five or six, seven years or so, informatics has been more of a household term. People still get it confused. But when I realized that there was an actual public health master I could get in informatics, I was very excited. So that question of data quality, I feel like it's totally achievable if you get the right people on board or the right organizations on board, and we're heading in that direction. And so that's what got me into a career in informatics for sure.
Charlie Harp:
I think it's interesting that when you think about data and the work that we do, it's almost like the data can reveal mysteries that you can't as a human being without the data and without a certain level of quality in the data. There are things that you can't see. It's like one of those magic eye pictures that if you can get the data right, then you can see things and do things that you may not be able to do otherwise. And I think I was in ONC Data Quality Symposium yesterday, and it was interesting because it was all about data quality, and they had a data quality expert from ISO 8000 on, and they were kind of saying that the whole point of this is about trust, and it's true. Data quality is all about trust. But I think that it's interesting how far we've come since 2001, Patina. I think that we kind of beat ourselves up in healthcare because the quality of our data today is not where it needs to be, especially when you consider what we're trying to do with it.
And I have concerns about, for example, deploying artificial intelligence and large language models on the data that we have today, because I worry that the data's not good enough for AI to be able to be trusted to do something significant with it. I think that humans still have the ability to say, "That doesn't seem right, even though the data says that," or, "I don't think this is true." And I'm rambling a little bit, but at the same time, I think that if you look back at 2001 or even 2014, and you talk about where we were and where we are today, I think that we've got a lot more data sharing than we had than I think we might've expected. I think we've come a long way. I still think we have ways to go. I'm excited to see where it goes, especially now that people realize that the quality of the data or they're starting to realize the quality of the data is important.
I just think if we can nail the quality of the data and we get really good at sharing that data, especially with all the stuff we're seeing in genomics and what kind of impact that can have if we really build the capabilities to harness that data, I'm very excited about where we're going and kind of the evolution. I wish it was faster, but I still think it's pretty exciting.
The reason why I asked this question about what got you into this health informatics or healthcare technology is one of the themes I find when I talk to people, partners, other people I encounter in healthcare. For a lot of us, it's not just a job. There's kind of a passion, there's a commitment, there's an investment. And it's funny because most people have the story where they realize that this is where they're supposed to be. And so I really appreciate you guys sharing those stories with me today. So this is kind of a weird question, but who is the customer of Datapult? Who should be picking up their phone and saying, "Patina, Millie, I need that ELR thing yesterday"? Who are the folks that are the ideal candidates for leveraging what you guys have built?
Patina Zarcone:
Well, I would say there are a few, certainly the public health system, our entire purpose at Datapult and our mission is to serve public health. And so we have solutions for our public health laboratories, we have solutions for our public health agencies and epidemiologists, but we also, of course, through the pandemic, worked with many private clinical laboratories. Actually can't even really say clinical, it was just a myriad of different laboratories. Some of them had never done testing for infectious diseases before, and they just wanted to do the right thing, and step up, and help augment testing across the nation. So certainly private clinical laboratories, hospital laboratories. We also do a lot of consulting in the public health space. So we work with any from the state down to the local level. So I would say primarily that's it. Millie, am I missing anybody?
Millie Malai:
I would take that one level to the next, and I think when Charlie was talking about data quality is about trust and sharing, right? I feel like our customers, really, anyone who needs to send data, share data, but needs to do it in a way that's trustworthy and where they can have confidence that it's not going to lose its integrity. Anyone that needs to do that, whether they're just sending it between them and one other organization, or they need to deliver it to public health like we have set up today, those are really the customers of Datapult. So do you need to move your data and do you need to do it with 100% trust? Well, then we could help them. We have a service or we can develop a service that meets their needs.
Charlie Harp:
All right. So you heard it universe. If you need to move your data, if you want to learn more about doing the right thing in public health, or you want to work with smart, passionate people, Datapult is there. How would they find you on the internet? What's the easiest way to find you guys?
Millie Malai:
We have a website.
Charlie Harp:
What's the URL? Don't make them guess.
Millie Malai:
It's datapultaphl.org. So you spell Datapult like you would catapult, but with a D for data. So datapultaphl.org.
Charlie Harp:
Excellent. All right. Well, thank you so much for taking the time to talk to me today. I really enjoyed it. And once again, very grateful to be able to work with you guys as partners. I enjoy it a lot. Anything else you want to add before we conclude?
Patina Zarcone:
No, just likewise, Charlie. Thank you for having us today. This was a lot of fun, and we appreciate the partnership and we will continue to do our best for the public health system, and excited to see what the future brings. So thank you.
Millie Malai:
Thanks, Charlie. This was really great. Appreciate you.
Charlie Harp:
Likewise. All right. Well, I'm Charlie Harp, and this has been the Informonster Podcast with Patina Zarcone and Millie Malai representing Datapult. Thank you very much, and we'll talk to you next time.