LeadingAge 2018: How AI Can Optimize the Resident Experience
When it comes to artificial intelligence, too many people have images of the Terminator, HAL 9000 or C-3PO stuck in their heads, says Joe Weber, senior vice president of research and product development and CTO for electronic health record developer MatrixCare.
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In reality, the technology is more like a spellchecker in that it’s not prescribing a particular path with 100 percent certainty, but rather offering a high probability that a selection is moving in the right direction.
“If I were to type in hallucination, a spellchecker can come back and give me a couple of suggestions of words that probably are similar,” said Weber, speaking Monday at the LeadingAge 2018 conference in Philadelphia. “We’re learning a lot as we start to use these systems.”
In healthcare, the technology continues to improve, he said, helping to guide care providers, as well as vendors like MatrixCare, in their solution designs.
“Deep data and machine learning is working to build on clinical practices,” Weber said. “We’re going to apply those across multiple time zones, multiple points of day, across skill domains. Our fourth generation is around the corner.”
Machine Learning Connects the Dots Between Patient Assessments
Presently, too many manual processes exist in healthcare, said Megan Lenthe, senior clinical product manager with MatrixCare. To that end, she said, machine learning will enable providers to more easily leverage the data obtained through those processes to take actionable steps for patient care.
“Advancements for EHRs have been remarkable, but in some ways, some EHRs aren’t any better than paper charts,” Lenthe said. “If we can’t take that data and harness that and do something with it, then we might as well be back to using paper charts.”
Machine learning in the company’s new systems will give providers the ability to connect the dots between assessments, helping them provide more preventive (rather than reactive) care. With chronic care, in particular, this would allow for better disease management.
“We can look at things like the patient’s activities of daily living to see if there is a decline or changes in those ADLs over time,” Lenthe said.
AI Paves the Way Toward Preventive, Connected and Continuous Care
Dr. Clifford Goldsmith, the national director of U.S. providers for Microsoft Health and Life Science, agreed that AI is paving a path toward a new model of more preventive, connected and continuous care that focuses on optimizing the patient experience. For example, he pointed out that the University of California, San Francisco and Johns Hopkins Medicine, through Project Emerge, have taken advantage of real-time information culled from AI to monitor actions in the intensive care unit, identify which ones specifically lead to patient harm and strive to consistently keep that harm at bay.
Goldsmith believes that efforts like this could lead more hospitals, and potentially long-term care facilities, to leverage information in real time to ensure that steps taken to keep patients and residents healthy and happy aren’t arbitrary, but rather data-driven and systematic.
“Imagine if these were not ICU-type events,” he said. “Imagine if these were the kinds of events or harm that you worry about in long-term care, like bed falls or dignity and respect, and that you were using these to monitor from your electronic health record to constantly give you feedback on how you were doing with patients.”
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