Healthcare software and long-term care providers are on the same page in using technology to shape care goals, according to some leading executives in both fields.
As senior living and care providers and associations have focused on the concept of value-based care, which ties finances and insurance coverage to residents’ quality of life, tech developers have moved in tandem to meet the twin goals of reducing costs and improving services.
Although many new innovations have helped achieve this aim, healthcare leaders have expressed the greatest enthusiasm for the future of artificial intelligence and predictive analytics.
“Early is critical with seniors,” said Allison Rainey, head of nursing and clinical informatics for MatrixCare, during a recent discussion for the McKnight’s Long-Term Care News Market Leaders podcast. “We can really impact outcomes if we identify changes early. Proactive tools are critical. But too, because time is so important, we can identify the plan of care that might be appropriate.”
The panel included Rainey; Mark Parkinson, president and CEO of the American Health Care Association/National Center for Assisted Living, and Bharat Monteiro, MatrixCare’s general manager for senior living. The experts discussed the need to coordinate on new software and tech tools so that they are both easier to use for providers, and tailored to their needs.
MatrixCare has been one of the leading companies developing interoperable tools for long-term care operators, so that resident and patient information can be shared along different points of care, such as hospitals and nursing homes.
That can be a moving target, as the benefit of AI’s analytical and predictive capabilities also generates a mountain of data that must be contextualized, Rainey noted.
“Health journeys are very complicated,” she said. “What is meaningful and what is not? More interoperability means more patient data coming in. How do we include the social determinants of health in that? Now we have great AI tools to serve only [relevant] changes.”
Monteiro offered the example of increasingly robust falls prediction tools, for how tech is advancing to both improve resident and patient health and reduce costs. Falls can cost communities and facilities an average of $380,000 a year, and it is anticipated that residents will fall at least once during their stay in a residential care setting, Monteiro noted.
“AI can look at falls and data points and stratify patients into the highest risk [level] for falling. That’s the opening step,” Monteiro said. “If I can create smart care plans, then I’m able to provide proactive care for residents. If you reduce falls, you’re automatically keeping the patient in that area of resident longer, and the provider has better financial benefits because they’re not dealing with as many fall repercussions.”
Parkinson agreed and noted that, from a policy perspective, the key will be for providers to control hospitalization and re-hospitalization rates.
“If you can keep your hospitalization rates low, you will do really well in value based purchasing,” he said. “And I don’t think it would be possible to slaughter your re-hospitalization rate if you weren’t using EMRs [electronic medical records] or tools that EMRs provide.”
The panelists also noted that, for the time being, clinicians’ anxieties about new technology replacing them are unfounded and that care providers are stressing that human healthcare workers still must make the final decisions and analyze information from new software and predictive AI.
In fact, a recurring theme in high-level analyses of AI over the past year is that new tools can take over the more mundane, frustrating tasks that clinicians and caregivers have to do and can free up more time for them to work directly with residents and patients.
