The Growing Impact of AI on Providing Quality Care at Home

Artificial intelligence continues to permeate the news and reports show that many people are wary of the technology. Fears ranging from AI taking over our jobs to taking over our planet run rampant. Hey, we’ve all seen at least one Terminator movie, right?

The use of AI in healthcare has its own set of critics. Compassionate professionals may feel that healthcare should be driven by humanity, not technology. RNs, not robots.

Yet, AI and machine learning bring definite advantages to improving precision and quality of care. In our latest podcast, I sat down with Elliott Wood, president and CEO of Medalogix, to discuss the advances of predictive modeling and other benefits of AI.

Among other factors, Elliott explains that their predictive modeling with a software system called MUSE has achieved 90 percent accuracy – and growing – for predicting when a patient will die within seven days. Imagine the implications of knowing with certainty how to care for patients and their families during that last critical week of life.

To hear much more about AI and predictive modeling for healthcare at home, listen to the podcast with Elliott. I’m confident you will find the conversation as fascinating as I did.

Applying predictive modeling to enhance value-based care.

After learning about the capabilities of MUSE from Elliott, I wondered how providers were applying these AI-aided solutions to patient care in the real world.

So I also sat down with Charlotte Mather (RN, MBA, FACHE, RWJF-ENF), vice president of nursing for hospice at AccentCare. If you’re not familiar with AccentCare, the agency provides a robust continuum of home-based care across 31 states.

AccentCare has been utilizing MUSE for about two years. Charlotte confirmed Elliott’s claim that the system is at least 90 percent accurate on predicting the last seven days of life. I asked Charlotte to describe some of the major benefits of predictive modeling. Here’s what she said:

A key theme is providing the right care at the right time – knowing where a patient is in their end-of-life journey. Knowing the likelihood that they’re going to die in the next 7 to 10 to 12 days really helps us as clinicians have a great plan of care in place and adjust our interventions and support for the patient and their family.

Having a reliable system and analytics that are feeding us that information that maybe we aren’t picking up on as we’re doing our assessments – it’s that added layer to really drive the right care at the right time for our patients.

I also love having oversight for all our hospice patients across our organization. MUSE provides a beautiful digital dashboard that I can see which patients need us the most and make educated, informed decisions about how to get our staff to those patients.

As a manager, I’ve got air traffic control to see what’s going on with all of my patients. So I can’t imagine not having this view and getting quality outcomes.”

 

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The effects on smart management of staffing.

When I asked Charlotte for more details on how predictive modeling helps her allocate her staffing resources, she replied:

This knowledge helps me leverage the different team members we have. CMS has very specific metrics for hospice quality of care and a couple of key areas really focus on the RN and the MSW. Those two roles are among many on our hospice team. So it really helps to leverage the RN and MSW working at the top of their license and then using our LPNs, our LVNs, our chaplains and the rest of the care team with other patients that need them as well.

The way the dashboard is displayed is really visually easy to see. So I know exactly which patients are at the highest risk of dying within the next 7 to 12 days. I can see on the dashboard really quickly what each patient’s visit schedule looks like this week, so I can determine if I make an adjustment here or there, I can meet this patient’s … and this patient’s … and this patient’s needs.

It leverages us to work as a very dynamic team. With help from MUSE, certain H.I.S. scores have dramatically improved over the past couple of years.”

And what about those patients who aren’t so close to end of life? Charlotte explained the virtues of predictive modeling for those cases, too:

“MUSE knows the patient’s primary diagnosis and what some of the fluctuations and trends look like for that disease’s decline. There’s a flag that marks a sudden decline in condition. It can say, ‘Hey, the patient isn’t really near death yet, but once they start to decline, they probably will decline very quickly – so keep your eyes on them.’”

 

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Please note that this blog post isn’t intended to be an advertisement for MUSE. Transcend Strategy Group has no affiliation with Medalogix. Other software solutions are out there. Yet I appreciate the specificity of these real-world examples for explaining how AI-driven solutions are improving the ways providers can deliver quality care at home.

If you’re using AI or other solutions to enhance value-based care, you should be packaging and promoting that story to payers and referrers. Transcend can help. If you want to learn more, contact us anytime at hello@transcend-strategy.com.