Healthcare is moving from episodic to ‘life-based care’

By Dave Muoio

Improvements in data management will soon mean healthcare providers can engage patients outside of standard care more than ever before, Judy Murphy, chief nursing officer at IBM, said Monday at HIMSS 2018 in Las Vegas.
“We’re managing everyone. We’re not just worrying about the people that we see in front of us, and it’s a real important difference,” Murphy said at the Coordinated and Connected Care Symposium. “Because we can’t always treat everybody all of the time, [we need to] be able to predict those people that we … absolutely need to get at, and prioritize them to the top. That’s where this predictive layer comes in, where we’re actually using the data to look at predictive modeling and forecasting.”
Over the past few decades, healthcare IT has made serious strides in the ways it collects and presents patients’ data, Murphy said. But while EHRs and patient portals are making their mark internally, a number of trends transforming the industry — such as the growing emphasis on addressing behavioral health factors and providing value-driven care — are turning the focus back on tech-driven patient engagement.
“We’ll be talking a bit more about patient engagement because … when you’re moving from episodic care to life-based care, you are not going to be in a healthcare organization the majority of your life,” she said. “A lot of the follow through — whether it’s preventive things, whether it’s checkups or potential conditions, or whether it’s disease management — a lot of that is going to be managed and taken care of by the patients themselves, so there’s this big movement toward making sure that our patients are engaged.”
Fortunately, Murphy said, a substantial number of patients are “very willing” to use today’s patient portals and other resources to manage their day-to-day care. For the others, it’s up to healthcare to review the available data and engage at-risk individuals before they join the small portion of critical patients that are driving costs.
“Part of our efforts sometimes is in understanding how we can actually predict which people are going to be catastrophic, so that we make sure, even though we want to intervene with everybody, that we at least make sure that we intervene on the ones that are probably going to move into that very sick side,” Murphy said. “That is a hard thing to do, but that’s one of the things we’re talking about here.”
Alongside her case for continued development of predictive analytics, Murphy also highlighted the advantages that machine learning and cognitive analytics can bring to these patients. The technologies can sift through data to flag more at-risk patients, and from a research perspective draw new health insights that a human researcher may not have hypothesized.
“That’s where we’re becoming a learning healthcare organization,” she said. “That’s where we’re not just relying on our guidelines and what’s been published through knowledge-driven methods, we’re also able to start looking at using data-driven methods that are much more concurrent and much more real time. That’s the healthcare organization of the future.”
Reaching this point will require health IT to make more progress toward common and interoperable data fields, an issue that Murphy admitted continues to be the field’s greatest challenge. Still, she was hopeful that cognitive technologies, such as natural language processing, may help mitigate these issues with time.