Denise Triba - Using HR operational data and AI to improve service delivery

[00:00:04] Welcome to the HR Data Labs Podcast, now part of the WorkDefined Podcast Network. Join us as we explore the vital role of compensation, strategy, data, and people analytics in navigating today's complex business world. With the resources of WorkDefined, we're now bringing you deeper insights and actionable ideas from top experts. Now, here's your host, David Turetsky.

[00:00:28] Hello and welcome to the HR Data Labs Podcast. I am your host, David Turetsky. And like always, we try and find people inside and outside the world of human resources to give you the latest on what's happening. And today, we have one of the best, Denise Triba from Ingenovus Health, the CHRO. Denise, how are you? I'm wonderful. How are you, David? Good to be here. I'm great. I'm great. It's been a long time. Yeah. Yeah, absolutely. Why don't you tell people a little bit about you and about Ingenovus?

[00:00:57] Wonderful. Well, you know, I'm the Chief Human Resources Officer here for Ingenovus Health. Ingenovus Health is a workforce solution company. So we work across the nation with healthcare providers or life sciences companies to help them get the most optimal workforce mix, workforce solution they possibly can. What's really important about it is you place some of the most important people, people who try and keep people healthy.

[00:01:25] Yeah, at the bedside every day. Our mission is to make sure that care is delivered at the best bedside in the best possible way. Thank the Lord that you guys exist. Yeah, absolutely. Absolutely. You fill holes for some of the most complex jobs in the world. Yeah. They take care of people.

[00:01:44] Yeah, absolutely. We do anything from, you know, the most intricate cardiology teams to, you know, OR specialist, OB specialist, ER, you know, techs in the field to the just to the back office engines even. And it's really a full service that we work with healthcare providers so that they can do what they do.

[00:02:10] Thank goodness. As I said, I'm actually going to see my dermatologist this afternoon because I have an extremely bad case of poison ivy right now. And it's literally keeping me up at night. Oh my gosh. Not good. I had that a couple years ago. I don't envy you at all. No, no, it's one of the it's one of those really bad. I always get it. And when I get it, I get it really bad. It goes all throughout my body. And oh my gosh, it's so uncomfortable right now. I'm smiling. But inside, I'm in hell.

[00:02:39] But but before we get into our topic today, which is a really cool topic, Denise, what's one fun thing that no one knows about Denise Triba? You know, there's probably several. But, you know, years ago when I was back and just starting college, I really wanted to be a doctor. That was my mission. I thought, gosh, you know, I'm going to go through medical school and support people at the bedside.

[00:03:02] Flash forward, I do that, but in a very different way, you know, in working with healthcare providers to provide solutions where, again, we're placing physicians at the best at the bedside. And you didn't have to pay for all those student loans. I didn't. I have enough though, right? I did. Well, yeah, but but that five years, I have friends who'd gone through dental school and others. And yeah, it's wow, it's a burden. But thank God you do what you do.

[00:03:32] And we're really excited about it. And we're really excited to talk to you about how HR is going to use operational data and AI to improve service delivery. Yeah, absolutely. Absolutely.

[00:04:02] So, Denise, I understand your HR team is really focused on improving how they work to better support the business and clinicians. Can you explain to our listeners how and why it's so important to Ingenovus? Absolutely. You know, our employee and clinician experience is really just at the forefront of everything we do.

[00:04:24] And our teams, our HR teams in particular, work very hard because that clinician experience, the way they receive our services, is pivotal to whether they want to engage with us and how they engage with us. So our work is always focused around how do we get better and how do we better serve our clinicians.

[00:04:44] Do you think of yourselves as recruiting team or do you think yourselves as really an embedded, because you called yourself a workforce management and really a workforce. Solution. Solutions company. Yeah. Do you think of yourselves as really being more than just, and I'm not trying to minimize what you do as saying recruiters, but you really are trying to find and place clinicians and support teams into the right facilities, right?

[00:05:12] Yeah. And it's really about working with the client. You know, every healthcare facility has their own workforce, you know, issues or problems or opportunities that they're trying to really optimize. Our company comes in to really understand those from the client perspective. And then working with the clinician portfolio that we have, how do we help them optimize what they need to get done?

[00:05:37] So it is very much a consultative approach versus only recruiting. Recruiting is a big part of what we do, but it's so much more when you put it into play with the clients and the workers that we work with. How has this changed over time? Obviously, technology has made this different. But how has this changed over time in terms of in the old world where we used to hire people, everybody was a W-2.

[00:06:04] And then the new world where not only are they not even W-2s, they're not even 1099s. They're actually working on behalf of you in the clinics, in the areas. They're working for you. So it's really a very different business model, isn't it? It is. Yes, we do a mix.

[00:06:24] We have both W-2 employees that really are an extension of our workforce and they're placed in facilities that they become part of that organization's workforce. And we also have 1099 contractors as well. So it's a nice for us. But it's not even, I'm talking about from the facility side. Yeah. The facility, when they're making the decision to hire, they're going through their workforce planning. And they're saying, well, we need to hire a radiologist.

[00:06:54] They say, well, we could do it the old-fashioned way and post it and pray. Or we can go to Ingenovus Health and be able to hire someone through them. So even from their perspective, you know, is it the new world of gig working and the supply and demand? Or what forces or what pushes a facility to choose the hire through you instead of the going direct and W-2ing them?

[00:07:20] Yeah, it's optimizing their workforce, you know, budgets or how they use their workers. And in some cases, if they can't find a specialist, and we can, we can be that quick arm that can help them continue service. So, you know, in particular for some of the modalities that they have as operating needs, they may not be able to find a specialist if somebody steps out.

[00:07:46] Or if somebody needs to take a leave, we can come in and very quickly keep that engine going for them. And we understand their business. And so we may find things in working with that client that helps them get better at delivering care. So, you know, it's understanding the mix of resources, full-time, temporary, you know, the whole gamut of what we can offer. Has that changed significantly since the pandemic?

[00:08:11] And obviously the pandemic was a, put a bookmark in that, but has it changed dramatically since then? I would say dramatically. I think healthcare organizations have become much more attuned and becoming much more precise on what they need. And that obviously means that they need a good partner that can be right there with them. And that's what our organization is.

[00:08:37] So tell me how your team, especially your HR team, is adopting artificial intelligence to help that change and to help optimize what they do. Yeah, this has been a great journey. And I'm really proud of our HR team, in particular, our HR operations workers, because we have a small HR team. And they have really stepped back to say, how can we work more effectively and optimize our work processes?

[00:09:04] And so that journey actually started by the HR operations team doing workflow mapping of just what they do on a day-to-day basis. Why that's important? They really wanted to understand the opportunities, the gaps, things that maybe they could do better, areas that could flow smoother. So they started by doing process mapping. Then they went to a ticketing system.

[00:09:31] And that was a big step from a standpoint of validating the data that they instinctively knew. For example, you know, the HR operations teams know that, you know, 50% of our calls are going to be about benefits enrollment and how to select the right benefits programs. Another chunk of calls is going to be around accessing benefits cards when they're at the doctor. Sure, sure. Right?

[00:09:59] But what helped by using the ticketing system is it validated that. So it put a number behind what they intuitively knew. Then, once they kind of spent some time in a very short period of time, I think over the last three months of using the system, we've had like 3,000 calls that they've been able to track and learn from. A lot of volume.

[00:10:23] They've taken that data now and they've been able to focus with the partnership of our IT team in standing up our AI tools, which we call Freddy, our in-total tool. How did you come up with that? Nothing significant other than putting a human kind of touch to AI. Right. But Freddy now is now being focused on those key areas that we've learned about through data.

[00:10:50] So taking data, validating it and analyzing it now. And now the team is building out articles or questions that Freddy is internalizing and they're training Freddy so that Freddy can then take some of the high volume, more repetitive types of work off the desk. So let me play it back for you.

[00:11:14] So the first thing you did was you did a workflow analysis and then you built a ticketing system that went through each of those workflows and basically captured the data throughout those workflows of how do things actually work? And who was requesting things and how did it get disposed or dispositioned, I guess is the word. And that gave you the ability to say, here's what we spend our time on. Here's the friction in the system.

[00:11:40] And here's how Freddy can help alleviate some of that friction so that we can spend our time doing more value-added tasks. You got it. That's exactly right. Wow. And at the end of the day, the bottom line is we're helping get information and data to our clinicians faster in a more accurate way.

[00:12:01] And it takes the task off the HR coordinators so now they can focus on other things and we're expanding them into other areas in HR and growing their careers and skills. Of course. So what did you find that Freddy is doing the most of? Is it answering, like you said, is it answering questions about how do I use my healthcare card when I go to my clinicians? Is it how many vacation days do I have available?

[00:12:28] What were the questions that were most often asked that Freddy is taking care of now? Most often it was around how to enroll for benefits. Because we have a lot of people coming in very fluidly and they're always enrolling in benefits as they come into our organization, our travelers are. So a lot of times people, you know, they go through their onboarding and they wait a couple, two days. And now all of a sudden it's like, oh, I want to sign up. Right? How do I do it?

[00:12:57] So that was the number one area where we started to say, how do we teach Freddy to say, here's where you go to enroll. Here's how you enroll in benefits. Here's the types of benefits that are being offered. So that was number one. The other areas were leaves where we had people that maybe they got injured or something came up from a family and they need to take a few days of leave. How do they do that?

[00:13:23] So those are the more routine, but yet time consuming processes that we wanted to get at the front line sooner in a very thoughtful way so people didn't have to wait. And that they got the information they need to get connected to where they need to be. So on the back end though, Freddy's connected to your HRIT. Yep.

[00:13:44] Freddy's connected to the databases so it knows how many hours they have left and how many vacation days. So it's also got to have access to all of the knowledge about what is an FMLA leave and other things. So you have an LLM that's connected to a lot of different sources to be able to answer those questions effectively as well as correctly.

[00:14:09] Yeah. And right now what we're doing is kind of that initial phases is we're taking and we're what they're calling articles. We're writing articles, which is basically here's the core answers to the top questions that are coming in. And one question may have 10 articles responses that actually are teaching Freddy.

[00:14:34] And then the coordinators are coming back and validating, did Freddy internalize that right? And is the right answer coming back? So there's a lot of, I think to the HR coordinator's surprise, there's a lot of involvement in getting the AI tools to anticipate the questions and being able to respond in a variety of ways. So it takes a lot of interaction with our HR coordinators who are the subject matter experts.

[00:15:02] They've done the work to help Freddy get to a point where Freddy can answer and know where to go for these types of information. Are you worried at all about the hallucinations that sometimes AI comes up with? And what do you do to validate against that? We are very thoughtful. We don't want to put misinformation. And so there's a lot of time that's being spent validating the responses. That's part of the training of the AI tools. And we've started very basic.

[00:15:31] So again, instead of just opening everything up, it's let's take these few questions and really work with them to get those answers really down pat. And, you know, 50 to 60, maybe even 70% of the time, those questions, the responses to those are going to get the person where they need to be quicker. So they don't have to, you know, email or call.

[00:15:58] They can go get a quick answer and get to where they need to be. So maybe it sounds like a stupid question, but is this a chat bot? Is this a IVR? How do people actually interact with Freddy? Freddy is just, it's part of our, right now, it's part of our HR interaction before we actually release it. We're still in that beta. So Freddy right now is between our HR teams and our IT teams. Okay, okay.

[00:16:26] So we're not quite there yet to kind of turn Freddy out and unleash Freddy, but we're making great progress to get there quickly. But even so, even if Freddy is now an internal tool for HR to be able to get the right answer, and then the HR person can turn that answer over to the person, the clinician or whomever it is, to be able to tell them what's going on. It's a really huge time saver. It is. It is.

[00:16:52] And people are seeing that we're seeing, you know, not just time saver, but we're finding that through the ticketing system and the use of Freddy, we are, we're not missing calls, right? The capture rate is much greater. And the ability to respond the first time has improved significantly versus, like I mentioned, a repeat follow-up email or somebody coming back and saying, I left a voice message.

[00:17:22] Those things have really dropped. And, you know, again, the team is learning so much more about the detail of the work, right? Because you're actually teaching. I've always said, if you have to teach someone, you learn it. You internalize it better. And be it AI tools, but they're still teaching. So there's some great learning that happened along the way here. Oh, my gosh. Absolutely.

[00:17:48] And, you know, they may actually realize there are other things they might be able to ask or be able to help the AI with or help the clinicians with beyond that first question. Yeah. That enables the entire interaction to be more useful, more thorough. And then get higher clinician satisfaction so you can hire them again in the future when you have another placement. Yeah. Yeah. Yeah. Extend and get them to extend their services. Right. You know, and that's actually true.

[00:18:18] I think the big aha for the HR coordinators was really the amount of engagement that they've had to have in this whole process to really dive into the workflows, understand the works, and now being able to recreate that within the AI tool. So that is anticipating, not just responding. Right. Yeah. Yeah.

[00:18:42] Denise, I wonder, though, this is a really good case study for the anticipation of the 365-day questions that come up. Because your ticketing system and the people who are the humans in the loop now are going to hear certain things that they anticipated. They're going to hear certain things they didn't anticipate. They're going to be able to answer the questions much more quickly. Absolutely. There's going to be so much cool stuff that comes out of this.

[00:19:12] So I guess the question I wanted to ask you is, could we keep the human in the loop and make them better, faster, richer, stronger, improve Freddie, and then enable that clinician to keep that face of that person who they trust? Have Freddie become a more trusted source? Of course, it's still taking care of a lot of the hard work. And that person who's in the middle can still do a lot more without giving Freddie out to the clinician.

[00:19:41] Is that a possibility here? Yeah, it is. And I think, you know, the way we've thought about this is Freddie's kind of that front line. But there's going to be times where Freddie needs help, right? And so that's kind of like the tag pull in, right? And that's where your subject matter expert comes in and says, this is absolutely right. You've gotten the right quorum information. Let me help you get the rest of what you need.

[00:20:06] A good example of that might be when somebody calls in from employment verification. There are standard things that we can offer somebody calling in to get employment verification. But because of the nature of a traveler assignment, not all that data may be readily there because there's breaks in time.

[00:20:27] That's where you may need to tag in someone to say, hey, can you go and look and make sure that all these assignments link up to the full year, right? You might not be able to do that. So this is the combination. Because the data is not there. The data is not relevant in one place. It may be there, but not necessarily accessible. So this is where you do need a little bit more expertise of coming in and really be able to quickly.

[00:20:54] But for the 90% of here's where you need to call to get your employment verification or here's where you need to start the process, Freddie can answer that just at a quick, quick second. It's just the extra that they need to help with. So you're using Freddie to answer questions from the clinicians. What about from the facilities? Or are you thinking maybe there's a way for turning Freddie, I'm not saying over to,

[00:21:24] but maybe to your internal sales teams to be able to get more insight about maybe performance management or other things that enable you to make the sales side of this better? Yeah. Our production side are also using AI and how they're adopting it to that. Not only they're recruiting to find the right talent for clinicians, but also in how they're working directly with the clients. So yes, we are. It's not Freddie, but it's a different version of the AI tools. Frida. It might be Freddy.

[00:21:54] There you go. A different version. But yeah, for us, Freddie is focused more on the core of our shared services group, our corporate employees. And then again, how do we best serve our clinicians? Well, I mean, that's all fascinating. But where do we go from here? What learnings do you think you have from this that you'll be able to utilize in the future? What surprised you and what kind of you thought you were going to get, but you didn't?

[00:22:24] You know, I think the biggest surprise, and this is coming directly from our HR coordinators. You know, I talked with Julie Biggs the other day. And, you know, I think one of the things, as I mentioned, she said, I was surprised at how much involvement, human involvement it takes to really work with the AI tools to do it right. And that's because really of the thoughtful approach.

[00:22:46] So I think that was the initial and the learning curve, not just with the bringing up and standing up the AI, but the learning curve that the coordinators went through to really understand the work and the opportunities to get better. And that starts with just a deep dive on what you do every day.

[00:23:06] That's going to help us, though, not only with the AI, but internally we can now look at how do we better engage with our recruiters and other guests so we can help them get better information. It also has started some really good conversations around what else? You know, what other opportunities and workflows can we look at? And so that's extending in other areas of HR.

[00:23:31] So it's a very exciting project that has, you know, again, they've really owned, the team is really owned, and there's a lot of good learnings that will apply in other areas. Do you think that there was reticence because people thought maybe this is going to take my job away? I think with any change, there's reticence. But up front, we were very clear that this isn't about a workforce change internally. This is about how do we get better?

[00:24:00] How do we get better and take things that could be done in a different way off your plate so that you can grow and that you can take on new skidsets? We've always approached it from a career development standpoint. And there's nothing more important in career development right now than learning these new tools and how it will not just impact your world, but how you can take advantage of them. Absolutely. And it's really working for us.

[00:24:26] And, you know, people are excited about doing things differently than what they've done before. And what they thought, you know, a little hesitant. Now it's second nature and they're really taking and developing and coming back and saying, I could do something over here. So very impressed with our HR team and just how they've really owned this through the very beginning. Now I'm going to ask you something that you don't have to answer, but I'm going to ask it anyways. Do you think that you considered all the costs involved?

[00:24:56] Because it's expensive to leverage artificial intelligence. It's not cheap. And it's certainly not free. A lot of people think that leveraging AI is free. Free is never free, by the way, even if it is free. But did you expect it to cost as much as it did? And I don't even know how much it costs, but I bought some AI systems. I have a supercomputer sitting on my desk over there. It was expensive. Did you expect it to cost so much? Or are you surprised how much it didn't cost?

[00:25:24] Well, we've partnered very closely with our IT groups. So cost was always a factor when we go into these things to understand what we can do and what we may have limitations around. And even as we work with other shared services partner, we continue to have those conversations. Does it make sense to license a certain type of ticketing system in that? Or can we use what we have initially?

[00:25:53] So those are some very thoughtful conversations going on. And we've been able to kind of keep things very, very contained within our budgets at this point. But we're absolutely having those dialogues. So we didn't go into this with our eyes closed. We understood that there was an expense. And we have to think about that in terms of other groups want piggyback on what we're doing because we've kind of created the model. How does that look to?

[00:26:22] And are we thoughtfully considering that in part of their budgets as well? So always a conversation. Okay. Well, that always makes sense to have partners involved in it and making sure that you're giving them what you could possibly give them from the learnings that you've had from this really cool journey you've been on. Yeah. And our IT partners have been very good about saying, okay, time out. You know, this is what your options are here, right? And I really appreciate that because, you know, that's their expertise.

[00:26:51] So we'll make good partners for us. Yeah. I also believe that HR going into something like this and we're not the best with technology. We need good partners to be able to help us. I mean, you know, HR IT people are wonderful and good HR IT people are very hard to find. They exist and they're wonderful and you don't want to lose them.

[00:27:11] But when you have IT people who understand the HR problem and the HR problem set and they did that really cool experiment with the learning the processes as well as being able to implement the ticketing system, that was a brilliant idea. They seem like really phenomenal partners for you. They are. They have been. And they continue to be. As we're building up Freddie and expanding and they're really working with us to make sure that we're getting where we need to be. And we've been very grateful for that partnership.

[00:27:42] What would be your advice for HR teams that are considering taking on this challenge of moving forward with what artificial intelligence could bring to them? I would always start where the work is being done with the people that are doing the work and the process. You really need to understand the what before you start to apply the how.

[00:28:06] And if you take time up front in refining that process and really thinking through it, the technology will really enhance versus just coming in and applying the technology. Then you're spending time rethinking the process. So that would be, you know, the advice I would have is really spend time with the process and listening to the people that do the work. I think that's always a good thing to do, whether or not you're talking about process change or not.

[00:28:35] Listening to the people who do the work is where it's at. Absolutely. Absolutely. Denise, this has been amazing. Thank you so much. I've learned so much from listening to you. And I think you've given a lot of people a lot to think about with the way in which you've approached this. You've really taken a very good scientific approach to it. Yeah, we're very pleased with how the team's embraced it. And it's been a lot of learning. And, you know, you can't be afraid to try new things.

[00:29:04] And this group has just dived right in. So if you don't mind, I'm going to ask you back in a few months to see how that experiment has gone further. I would love it. I would love it. Hopefully there'll be another Freddy or something else, a Frida by then that we can share and talk more about. Right? Freddy and Frida might have kids by that point. We'll be talking about the Fridos team. I don't know. Right. Denise, thank you so much. You're awesome. I really appreciate your support and being on the HR Data Labs podcast.

[00:29:34] Absolutely. Great to be here, Dave. Thank you all for listening. Take care and stay safe. Thank you for listening to the HR Data Labs podcast. Don't forget to hit subscribe and share it with your network. You can also check out the recordings on Spotify or the HR channel now on Roku and Fire TV. Thank you. Take care and stay safe.