Can AI can uncover hidden talent buried in the resume pile?

Health care staffing has always been one of a practice's toughest challenges, but artificial intelligence has added a new wrinkle. It's now trivially easy for candidates to generate polished resumes and cover letters and fire off applications by the hundreds, which means hiring teams face far more volume without necessarily getting better quality. That leaves already-stretched staff trying to separate genuinely qualified applicants from the noise, while looking for ways to streamline their hiring process while application volume keeps climbing.

The stakes are especially high in health care, where unfilled shifts translate directly into gaps in patient care, and where matching for the right clinical or administrative fit is more nuanced than scanning a resume for keywords. Taylor argues AI should surface and summarize candidate data — skills, behaviors, cultural fit — so a human still makes the final call, an approach that could also help practices weighing whether now is the time to hire nonclinical staff to close coverage gaps.

Dina Taylor, chief evangelist at HireVue, says technology has changed the interview itself, and better-matched hires lead to stronger long-term retention.

Medical Economics spoke with Taylor about this issue to learn more.

(Editor's note: The following transcript has been edited for brevity and clarity.)

Medical Economics: If you ask anyone running a medical practice or a health care facility, they'll probably list staffing as one of their top challenges. Why is that such a problem right now?

Dina Taylor: I think in health care in particular, staffing is always a critical challenge, and these days even more so than usual. Simply because it's the staff that you have on board that has that direct impact to patient care, and that's really what everybody is focused on and passionate about day by day. So not having a team in place, not having those shifts filled, directly impacts people's lives and people's health, and that really is fundamental and paramount to what this team is doing and focusing on every single day. We are also living in a world where there is a rise of so many applications and so many interested people in so many open roles, and health care in particular is one of those spaces, as you well know, that requires some pretty specific and important skills to fill those open roles. So asking hiring teams, doctors, medical professionals to sift through all of that data and all of those resumes to find that great talent — when that volume continues to grow exponentially, that makes that job harder now more than it ever has been.

Medical Economics: I'm assuming AI has played a big role in this, whether it's creating resumes or cover letters for people. Talk to me a little bit about what kind of impact that's having.

Taylor: Completely. I mean, candidates now — and we hear this across all industries, and it's happening a lot in health care as well — AI has made it really easy to write a resume. It's made it really easy to write a cover letter. It's also made it really easy to apply for a lot of jobs in a short period of time. It used to be applying for a job took a minute — you had to sit down, complete an application, go through this process. AI has made that process a lot simpler for candidates, and that's contributed to this rise in so much volume and so much data for hiring teams.

Medical Economics: So what does that mean for the hiring team? Does that mean they're getting applicants who maybe aren't as serious about the position and are just throwing everything against the wall, rather than people who are genuinely interested?

Taylor: I think it's actually both. At the end of the day, what it means is they're getting a lot of people applying, and there are going to be some people who don't have the right skills for that job but are doing everything they can to find a job out there. So they're applying even though this might not be the right one for them, and that volume is keeping that super highly qualified, highly skilled talent buried in this application mess that has occurred, and it means the really strong talent is really hard to find.

Medical Economics: So how do you deal with this? How do you sift through everybody and find the person who actually has the skills you're looking for and is a good fit culturally at your organization?

Taylor: You know, it's funny because we were just talking about how the rise of AI has made it easier for candidates to apply. The rise of AI can actually solve this problem statement for hiring teams as well. We recommend, and we've seen over and over again, using that AI for good and having hiring teams leverage AI in order to uncover that strong talent that's buried in those applications.

Medical Economics: So are we getting to a point where we have AI applications being sorted by AI hiring teams? I feel like it's sort of like the battle of the robots to define the best person.

Taylor: And that's where I think it comes down to using that AI for good. We don't want the AI to ever take the place of the human and do the work for the human. We do want the AI to make the human's work easier and help the human do what they're super good at. From a practical, tactical perspective, we have the opportunity to leverage AI to uncover those skills that are so required and critical for that role, and do it in a way that's really fair and unbiased, and focused on the core critical requirements of the job. The reality is, if you post a job and get 1,000 people apply in the course of a week — which happens all the time — no human has the time or the space to read through all of those resumes and make informed decisions. It's literally impossible with everything else going on, but AI can help. AI can help do the sorting. AI can help uncover the information, and then that human in the loop validates what the AI is uncovering.

Medical Economics: What does this actually look like? Would the hiring manager get a pared-down list of, say, the AI's top 20 candidates, and then take it from there the same way they used to? Explain what the process looks like.

Taylor: Yeah, I think that's an important question too, because there are a lot of different rules and processes out there for what I mean by using AI for good. We never want the AI to be the decision-maker to say Todd is a better fit for this job than Dina. What we want the AI to do is evaluate all of the data and then present all of the information over to the hiring manager for their consideration. So instead of maybe a top 20, it could take all 1,000 people who applied, go through, create summaries of what everybody has provided, bubble up the relevant information for the hiring manager and the hiring team in order to make those informed decisions. Kind of have everybody listed in order of prioritization, but making sure everybody is still considered in a really fair way. So it's truly the thing that prioritizes and helps make the job easier.

Medical Economics: Is it difficult, though, if everyone's using AI to create resumes and cover letters? Is it harder to ferret out the good candidates from people who are using AI to pad the resume a little bit?

Taylor: Well, this is where skills validation becomes so important, right? Because the reality is, you and I can use AI to create the exact same resume and look like the exact same type of person and type of skill. We're talking a lot about leveraging AI to support the top of the funnel and the resume. But what we really believe in, and what we partner with our health care clients on as well, is to look past the resume. The reality is humans are far more than the bullet point and the word they put on paper. So you're right — leveraging AI to sift through resumes only gets you so far, but asking meaningful questions, digging into your skills, your behaviors, your personality traits, understanding whether or not you display the right amount of empathy to be a great health care provider, or the organizational skills necessary to work in a clinical environment — that's what matters more, and that's what the AI can evaluate. Not just what you write, but also how you respond and what you believe in and what you say.

Medical Economics: So the AI can help you find that cultural fit as well as just the skill set you're looking for?

Taylor: It can. We have a lot of health care customers who will say, we can train on these five skills over here, but what's really critical is the behaviors aligned to our culture and the core skills and certifications you are required to demonstrate in order to be an effective clinician. We'll focus on digging into those things and then let you train the other skills that are necessary.

Medical Economics: If a practice manager has never used AI before, a question might be: Where do I fit into this? Am I still interviewing? Am I still asking questions the same way? How does their process change in all of this?

Taylor: Totally — I would say yes, 100%, to the interview. But your interview really becomes a conversation to validate the information you have, so it's less meeting the candidate for the first time and more that you're walking into the conversation already knowing so much about the individual and their potential. You now have the opportunity to have a conversation with them, validate that information, and make sure this is the right thing for them and for you before you move toward an offer. But it becomes a really personalized, meaningful conversation instead of a more perfunctory interview that happens earlier in the process.

Medical Economics: If you get the right person by using the right process, do you find that retention is better in the long run?

Taylor: Absolutely. This process we're describing — we've been talking a lot about the AI managing the efficiency, which is completely true, right? Neither one of us is capable of interviewing 1,000 people in a short period of time, or reading all those resumes without going cross-eyed. But there's also an element of what happens on the outcome side. Once you hire great people, they stay longer. They have higher patient satisfaction scores. They are embedded into the culture and into the organization. They might be promoted more quickly or move into a different role. And we have the ability to track all of that data and prove out all of that goodness along the way.

Medical Economics: What's the biggest mistake you see health care hiring teams make, whether it's trying to move fast or in how they screen candidates?

Taylor: The biggest mistake that I've seen is being afraid to start, because we think someone's going to say no. It's like health care has always done it the way it's always done it — "we can't use AI, we can't use technology, this is just too new for us." I think that tone is starting to shift, simply because technology and AI are now becoming so ubiquitous that people are realizing they're left behind if they don't lean in. But I do think that legacy assumption — that this would be really hard to do in health care — is still living out there a little bit, and I think that's the biggest mistake that gets made.

Medical Economics: Looking out over the next five years or so, how do you see the hiring process changing?

Taylor: I think we're going to continue to think more about the skills and the behaviors, and less about the job itself. There are studies that show the greatest data scientists out there are actually philosophy majors. And there are so many individuals entering health care fields later in their career, after having done something else along the way, because they've learned they have the compassion and the empathy that might really make them a great fit for that role. I think that's going to continue. I think we're going to continue to see the rise of transferable skills, because AI is also changing the landscape of what jobs are so much. We've spent the last 18 months learning how to use AI and applying it to what we've been talking about. Now I think it's going to redefine the jobs in a really exciting way.

Medical Economics: Is there anything else you think physician practice owners need to know that we haven't talked about?

Taylor: I think we've talked about it, but I'm going to double down on it: lean into innovation, and don't be afraid to try. The hardest part is knowing where to start, but once you start, really good things happen.