No Empty Promises: How Home-Based Care Providers Actually Plan To Use AI

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Artificial intelligence is likely to be a society- and business-altering technological development.

But, just like the advent of the internet before it, AI’s emergence will undoubtedly lead to as many empty promises from business leaders as it does actual use cases.

That’ll particularly be the case in the early innings of AI, which I believe we are currently in.


At any health care conference over the last five years or so, AI chatter was as ubiquitous as COVID-19 chatter was during the early days of the pandemic. But I’d often come away from that chatter with no more information on how providers planned to put AI to use than I had before.

This year is likely to be one of the first where a good chunk of providers are actually putting AI to use, however. That’s why, in Home Health Care News’ trends for 2024, we included the prediction that “providers will find ways to more seamlessly and strategically integrate AI.”

Over the last few months, I have tried to cut through the empty promises and ask providers directly: How are you currently using AI, or how do you foresee your organization using it in the near-term future?


More direct questioning, unsurprisingly, led to more direct answers.

In this week’s exclusive, members-only HHCN+ Update, I hope to take you behind the curtain on providers’ AI strategies across home-based care.

An AI prologue

First things first, every provider I talk to about AI generally starts off with a similar opening statement on AI, which is that they do not believe AI or other technology will be able to replace hands-on, human care.

Particularly in the early innings, that seems like the right mindset.

“We hear so much about tech in the home,” Visiting Nurse Health System CEO Dorothy Davis told me. “Not that I don’t think that’s an important piece, but I think the revolution is going to be on the consumer side and on the caregiver side. Tech is an enablement. If the user and the person impacted doesn’t understand it, the tech means nothing.”

That’s a key caveat. If AI cannot be implemented in a way that can be understood by a select few people – or in some cases a large group of people – then it is useless.

It’s also generally useless, particularly for generative AI, if there is not good data to feed into it.

Providers can’t go from an archaic operation with no data tracking capabilities to a future-facing, AI-embedded operation in one jump.

“I often describe data as the clay,” Guillaume Vergnolle, a senior data scientist at AlayaCare, told me on stage at Aging Media Network’s Continuum conference. “It’s your best material to come up with an [AI] solution. You need the right kind to come up with the solutions. So, when it comes to the retention problem, make sure that you’re actually collecting the right data to mirror what you’re trying to solve.”

Guillaume Vergnolle, senior data scientist at AlayaCare, at Aging Media Network's Continuum conference.
Guillaume Vergnolle, senior data scientist at AlayaCare, at Aging Media Network’s Continuum conference.

AlayaCare is one of the home-based care vendors aiming to help providers out with AI. Its commitment to AI solutions – along with WellSky’s, for instance – is a heavy indication that providers will soon be further along with practical implementation.

Where AI will be useful

Compassus COO Laura Templeton told me that she sees AI becoming useful in two areas in the near-term future: documentation and scheduling.

“We currently have a couple of work streams right now — one being for clinicians — around how AI can make their job and role easier or better,” Templeton said. “We’ve been looking closely at how to consolidate and optimize processes by utilizing AI tools.”

Compassus COO Laura Templeton at Aging Media Network's Continuum conference.
Compassus COO Laura Templeton at Aging Media Network’s Continuum conference.

Compassus leaders were the first to divulge AI use cases to me in December at the Continuum conference.

“We’ve piloted several scheduling programs where we’re using our clinicians at the top of their license, and where we are sending the right clinician, at the right time, to the right place,” Templeton continued. “Scheduling is one area that comes to mind where I’m excited to see what AI can do.”

Indeed, scheduling is one area where providers could use advanced help.

After all, staffing is a top concern for nearly all home-based care providers. Within that, most leaders will say the key issue they’re trying to solve is retention. Within that, scheduling is the No. 1 reason that home health workers turnover.

“We’re having humans doing things that humans don’t have to do, scheduling being one of those,” VitalCaring President Luke James told me. “Medical records. Systems work. Where can we apply some generative AI and some kind of workflow technology that can take most of it out of the hands of humans? Reacting to the exceptions only, for instance.”

Axle Health, a home health scheduling platform, announced a $4.2 million funding round Thursday.

James also mentioned documentation, which Jordan Holland – the VP of value-based contracting at Compassus – also dove into in December.

“Clinical documentation has always been a big one — which has a lot of different layered potential use cases,” he said at Continuum. “There’s the idea of talk-to-text, but then there’s also talk-to-text to other discrete fields. Talk-to-text is great, but is that actually going to help you facilitate filling out an OASIS form? There’s an added layer to that because that talk-to-text then gets submitted to another party.”

James added that VitalCaring “has to get more efficient in the back office with rates continuing to fall.”

That is the core driver of a lot of home health providers’ AI strategies: finding ways to become more efficient to avoid fallout from any rate cuts from the Centers for Medicare & Medicaid Services (CMS).

Alivia Care CEO Susan Ponder-Stansel is taking the same approach, but through a different lens.

A provider that has gone deep into value-based care over the last few years, Alivia Care wants to find ways to up reimbursement through better outcomes.

“We want to really be able to stratify risk and create a patient profile,” Ponder-Stansel told me. “There are certain algorithms that you can develop to say, ‘Okay, when these particular things happen, you need an extra visit, you probably need to do a med rec.’ Because all those things downstream help prevent that rehospitalization, help prevent that adverse outcome. So that’s what we’re looking at.”

Elara Caring CEO Scott Powers, meanwhile, told me that stripping caregivers and home health aides of non-value work is the “No. 1 use case” that he sees coming to fruition.

The home care side

Personal home care providers are generally approaching AI a bit differently, which is interesting to note.

For instance, Home Helpers sees it helping most on the marketing side, particularly for franchises.

“We use AI in our franchise-development process around identifying potential new franchisees, and doing some specific psychographic targeting,” Home Helpers President and CEO Emma Dickison said during a HHCN webinar last year. “Internally, for the team, where we see the biggest lift with AI … is in the marketing department. But there are just so many applications.”

Similarly, BrightStar Care isn’t writing big AI checks yet, but is, for now, using AI-enabled chats on its website to help out with back-office functions and to get feedback from clients.

But there also are rate concerns for these home care providers, some of which are similar to home health providers’ concerns.

For those that are diving further into Medicare Advantage (MA), for instance, there’s a need for more efficient processes to make MA beneficiaries worthwhile clients from a business sense.

“I think you just have to prioritize where you can make the biggest difference on the margins,” Kristen Duell, the EVP of experience and innovation at FirstLight Home Care, told me. “We need to create automation in certain areas, leveraging technology and leveraging machine learning so that we can reduce overhead costs – and sometimes field costs – so that we can take on those health plan [clients]. We need it to make economic sense.”

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