As a high-touch, highly human care delivery type, hospice care may seem in a sense removed from the fast-paced, high-tech world of artificial intelligence. Yet in many ways AI is giving rise to exactly what hospice professionals set out to do in the first place: provide patient care.

It also supports hospice professionals and patients in several other important ways, from reducing hospitalizations to providing scheduling efficiencies for staff and providing significant time savings. It can even be a key to providing the right care at the right time, and improved accuracy of patient data.

“It’s not reasonable to think that a care provider who is trying to work with a multitude of patients always has the time to read [EHR] documents as just as comprehensively as they might like to,” says Nick Knowlton, VP of Strategic Initiatives for ResMed, the parent company of EHR platform MatrixCare. “Leveraging the power of modern technology, be it AI or just simple machine learning to scan through those documents and pull-out bits of information that would be important for that patient’s care progression to make sure that providers don’t have to do duplicate data entry, there’s no resulting errors from that.”

Hospice care providers are already leveraging AI in numerous ways, through platforms that perform specific functions, such as administrative tasks or digesting massive amounts of patient data that would otherwise be cumbersome to review.

These specific applications are serving the industry versus an overarching approach, says Jessica Rockne, Head of Product, Home & Hospice, MatrixCare.

“I think that purpose-built solutions for specific topics will get us there faster,” she says. “It might be transforming patients from home health to hospice where the patient is eligible, or it could be that they are in the last seven days of life and [the technology] is predicting that end of life.”

Monitoring risk

With hospice care patients often presenting multiple comorbidities, one way in which AI tools can benefit care delivery is in its ability to monitor and mitigate risk.

“That could be a risk of wounds [for example],” Rockne says. “Let’s say a patient is bed-bound. It can assess specifically if there is any risk for pressure ulcers or things like that.”

Fall risk is another area where this type of machine learning can come into play, Rockne says.

“Even with patients at home, a fall can take them to the hospital, which is now where they want to be,” she says. “That’s a perfect use case for AI machine learning.”

The use of specific objective information is a differentiator in this type of approach to risk management and predictive analytics, versus a subjective approach that sometimes can be the effect of a variety of caregivers with a variety of different habits and ways of doing things.

The Back Office

The most important impact to the back office when it comes to AI is in scheduling efficiencies, Rockne notes.

“The No. 1 place it can do that is around scheduling, and making sure the clinician isn’t driving 20 miles between visits,” she says. “It also keeps the clinician in a relatively small area where if something were to happen, they’re able to easily get back to the patients, and could also do more visits if the scheduling piece has been done efficiently.”

This time savings can be significant, regardless of industry. According to a 2024 Thompson Reuters survey of 2,200 professionals globally across industries including legal, tax, and risk and compliance fields, those professionals predict AI will free up 12 hours per week within the next five years. Within the next one year, that number is expected to be four hours per week, potentially leading to staff savings.

Overcoming AI challenges

When it comes to implementing the latest AI technology, experts agree the healthcare industry at large stands to gain both in terms of staff management and patient care. It also stands to face more risk as generative AI evolves and teams begin to use the platforms to their advantage.

“No. 1: providers need to start slow,” Rockne says. “They need to get their staff similar with simple AI tools that are out there already; ChatGPT or Microsoft Copilot [for example]. Once they’re familiar with these tools, then it’s time to start introducing more things.”

Second, Rockne says, providers need to recognize that new technology tools are not a silver bullet to healthcare operations.

“It’s not magic,” she says. “It’s never going to replace the clinician. It can be used as a tool to help guide decisions or make the clinician aware of certain conditions, but at the end of the day it’s really their clinical judgment that’s going to drive that care.”

Above all, providers exploring new technologies can take a phased approach and can focus on specific objectives as a starting point.

This may mean tracking outcomes based on a new tool or implementation and training teams accordingly.

“Measure what you’re looking to improve on,” Rockne says. “Master that one item. Don’t try to hit everything. Stick with one master and get a feel for how accurate it is and how it impacts your work and improve that then move to the next. It’s going to take time to feel comfortable with that technology.”

This article is sponsored by MatrixCare. MatrixCare’s EHR is built to improve clinical and operational efficiency, so clinicians can focus on deeper connections with patients and families. To learn more about the impact they are making in the hospice space, visit https://www.matrixcare.com/hospice-software/.