What does AI mean for care experiences—and for the providers who deliver them?
By Steve Jackson, President, NRC Health.
This article was published in the April issue of Becker’s Hospital Review and can also be viewed here on Becker’s website.
Artificial Intelligence (AI) already looms large in consumer services, financial technology, and manufacturing. It’s now making inroads into medicine, too.
AI, in fact, is already revolutionizing clinical work. Algorithms are proving to be about as accurate as radiologists in diagnosing lung cancer. Even more remarkable, researchers predict that by the year 2053, all surgical work could be conducted by machines.
At first glance, trends like this might stir up some anxiety for those of us in healthcare. This reflects broader fears about AI in the economy. Leaders and frontline staff alike may worry that as AI grows more capable, the importance of human labor will shrink.
That is decisively not the case. More than perhaps any other industry, healthcare hinges on a human touch. The unique demands of the work put a premium on intimacy, connection and compassion—traits that no machine could ever replace.
Quite the contrary, as AI begins to revolutionize care experiences, it will usher in a new era in patient service, one that will require more human input than ever before: the era of mass personalization.
Insights gleaned from two particular innovations—predictive analytics and personalized engagement—will empower organizations to deliver experiences custom-tailored to individual patient needs.
The power of predictive analytics
Predictive analytics is one of AI’s most promising developments. The term is short-hand for any process that uses historical data to make predictions about the future. Its most famous use-cases are in the finance industry: banks routinely use AI-enabled predictive analytics to assess a borrower’s credit-worthiness.
With a little imagination, it’s not hard to see why such tools would be useful in healthcare. Health systems can use predictive analytics to anticipate what their patients will need, instead of merely reacting as new health concerns arise.
Well-designed analytics engines will combine patient health information (EHR-derived data points like lab values, diagnoses and treatments administered) and patient behaviors (such as online engagement, appointment-setting, cancellations, satisfaction scores, compliance and follow-up contacts) to bring new clarity on what patients want from their providers.
Health systems will know, for example, not just when a new health concern is likely to arise, but also when and how the patient would prefer to make an appointment to address it, what kinds of services the patient will most likely desire, what kinds of interactions are likely to increase the patient’s compliance with follow-up instructions and more.
While today’s predictive-analytics products can’t yet offer that level of sophistication, the technology’s evolving fast—and there’s little doubt of its strategic value to health systems.
Personalized engagement: The “Netflix-ification” of care experiences
As complicated as predictive analytics are, personalized engagement engines demand a much higher order of complexity.
Think of Netflix’s recommendation system. The streaming service takes in what it knows about viewers, and then automatically offers suggestions for what they might like to see next.
On the surface, this might not seem so complicated. But what’s remarkable about processes like this is the technology that underlies them, collectively called cluster behavior prediction systems. These absorb trillions of raw data points from consumers and independently identify patterns that can be used to group consumers by their preferences. This is how Netflix’s algorithms work.
True, personalized media recommendations like these are complex. Personalized healthcare engagement, however, is on another plane entirely.
To work, personalized healthcare-engagement systems will require exponentially more data. Nor are these data points limited to clinical information, or to patient interactions with health systems. They also include socioeconomic status, demographic data, ZIP codes, fitness-center attendance rates, family status, biofeedback data from wearable tech like the FitBit and Apple Watch, grocery and restaurant food consumption, and more. With healthcare engagement, the array of conceivably useful data points is truly staggering.
But imagine the potential. With a refined personalized engagement engine, it’s possible that health systems will have a newfound ability to tailor their services individually for patients.
They won’t just know what patients will need—they’ll also know where and to whom to send it in order to maximize a given patient’s happiness with a given encounter. This will give health systems a way to construct a concierge care experience, customized for every patient who comes in the door. It would truly begin the era of mass personalization.
While such an idea sounds like science fiction now, researchers are already at work making such technologies a reality. “Cognitive aide” technologies, for instance, are already making an impact on clinical care. It’s only a matter of time before they augment service decisions as well.
Care will always be personal
But note carefully—that’s augment. Not replace.
AI technologies can offer direction, but it’s providers who will need to assess and execute on what AI-driven solutions uncover. Healthcare’s very human heroes needn’t worry about obsolescence. If anything, the era of mass personalization will demand more of us than ever.