Boom Or Bubble: Home-Based Care Providers Balance Innovation, Risk In AI Adoption

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As the AI industry has boomed, so too have concerns of an “AI bubble” – in which investment rushes into companies wielding the technology without significant staying power.

Concerns about such a crash are not unfounded, given the dot-com bubble’s collapse in the early 2000s. Rapid interest in internet startups led to the Nasdaq composite stock market index rising by 600%, before falling 78% from its peak and killing any gains from the bubble.

A recent wave of news relating to AI in home-based care led me to ponder if the bubble will pop after providers and investors make major bets regarding the technology.

Providers and home-based care technology companies have continually expanded their AI horizons, but several recent HHCN stories suggest this trend is speeding up. I believe that continued investment is not only helpful for an industry beset by challenges on multiple fronts, it is critical.

However, it’s key for providers to be savvy about when they fully jump into the AI opportunity. Sensible caution may lead to a wait-and-see moment where providers are all waiting for their peers to take the boldest of strides. And there could be a mini-bubble that bursts within the home-based care space, as some AI-focused startups succeed and others falter.

In this week’s exclusive, members-only HHCN+ Update, I explain why the recent onslaught of home-based care AI news matters and offer analysis and key takeaways, including:

– Why home-based care should not be too wary about an AI bubble

– Why providers should be concerned about being first in line for cutting-edge technology

– But why it’s critical for providers not to tread water amid the AI revolution

Inside the AI surge for home-based care

While AI has been a buzzword in home-based care and health care in general for a few years, home-based care organizations have stepped up AI investments and put notable new tools into action in recent months.

One I’ve written about before is Bayada Home Health Care’s predictive analysis technology, which the company officially launched a month ago. The AI-enhanced program tracks over 40 health indicators and offers AI-driven insights to detect changes and intervene before clients’ risk levels escalate. 

Around the same time, Team Select Home Care debuted its own predictive analytics platform, which was a multi-million-dollar, years-long project that CEO and President Fred Johnson aims to use as a catalyst for more value-based care contracts. 

Home-based care-focused AI companies have also attracted significant investor interest. AI-powered home health coding and quality review platform Olli Health raised $10 million in November. In October, AI-powered home care delivery platform Zingage raised $12.5 million and Arya Health, an AI company with a platform designed to support home health and post-acute care providers in automating scheduling, compliance and other administrative functions, raised $18.2 million.

But providers and tech companies aren’t the only ones pursuing AI for the home-based care space.

The U.S. The Department of Health and Human Services (HHS) announced a $2 million initiative aimed specifically to relieve some of the burdens of caregiving for both family and professional caregivers. 

During an HHS virtual event announcing the Caregiver AI Challenge, Jason Resendez, president and CEO of the National Alliance for Caregiving (NAC), highlighted the U.S. health care system’s over-reliance on institutional care.

“Transforming this imbalance requires more than incremental policy adjustments,” Resendez continued. “It demands a fundamental reorientation of how we think about care itself, rather than continuing to prop up an institutional first system we must build from the ground up, what families need and what the evidence shows us works best.”

The answer to this fundamental imbalance, it can be assumed, is AI technology that supports family and professional caregivers to help patients stay in their homes. The recent flush of AI initiatives demonstrates home-based care’s own race to AI – the technology has become accessible and reliable enough to warrant focused investments of time and finances.

But this influx of investments can’t be considered as tenuous as the broader AI bubble. A bubble, fundamentally, means that the asset’s prices have risen higher than their fundamental value. In home-based care, AI is far from overvalued. That’s because the home-based care industry has nowhere left to turn to besides AI. With widespread staffing shortages, wage pressures, the rise of Medicare Advantage, and the need for greater efficiency, AI is critical to the industry’s success. I don’t believe there is any need to fear a home-based care AI bubble.

Still, the AI space in home-based care is becoming increasingly crowded. I anticipate this will only continue in 2026. Inevitably, not every startup seeking to serve at-home care providers will go on to be a world-beater, and this assumption is fueling some warranted caution among companies that are trying to time their AI investments. On the one hand, executives realize that they can’t fall too far behind as competitors – and the world at large – leverage AI in increasingly sophisticated ways. On the other hand, providers fear partnering up with the wrong AI companies, and even some early adopters are trying to ride the very fine line between bleeding edge and leading edge.

When providers should throw their hat in the ring

All this is to say that providers shouldn’t run toward AI technology recklessly. And one key consideration that I see providers making relates to the purposes of the AI technology that they are willing to invest in versus those that they are much more cautious about.

Although I had little exposure to AI until ChatGPT became available to the public, AI isn’t new. A computer and cognitive scientist coined the term in 1956 at a Dartmouth workshop (and the theories related to it go back much further). Of course, it takes time for technology to become accessible, affordable, and, crucially, reliably accurate.

While AI “delusions” are still a key concern – try asking ChatGPT to conjure a seahorse emoji or tell you how many r’s are in the word strawberry if you haven’t already – the tech has become far more reliable. But it isn’t bulletproof. Technology like ambient listening is not yet completely reliable, and home settings can complicate things even further. In the home, clinicians and frontline workers are moving around with a patient, maybe needing to place their phone in a pocket or other location that makes crystal-clear audio difficult to obtain.

In general, being an early adopter in a rapidly changing industry like home-based care can be a great idea. But I fear that for many of the more cutting-edge uses of AI, there are more reasons to delay some uses of the technology.

Back-office use cases, from my conversations with providers and tech companies, seem pretty solid. So too do AI tools for hiring. But some of the front-line use cases, like ambient listening, seem less robust at the current point. In my recent HHCN+ TALKS episode with Aveanna Healthcare Holdings (NASDAQ: AVAH) CEO Jeff Shaner, he mentioned that he doesn’t want to be the first to adopt cutting-edge, front-line AI technology tools. Still, he said he doesn’t want Aveanna to be last.

My take is that providers likely should aim to be in the middle of the pack, waiting for some of the newer tech to be more reliable to avoid burdening home-based care workers with unwieldy tools and potentially breaking their trust in AI (not to mention wasting an investment).

This leaves an important question: If providers want to be second, who will be first? Maybe Bayada and Team Select, with their predictive analytics tools, are examples of providers who like to be first in line. But these are house-developed tools that have undergone a lengthy testing process. For providers looking to embrace vendors’ expanding AI offerings, we may be in for a pause in AI investment, only for a flood of interest to come once the first brave providers break the ice – and once providers have a firm sense of the stability of the technology partners in the AI space.