4 Ways AI Can Help Build a Better Consumer Engagement Strategy
Many of us are aware that artificial intelligence is being used in factory robots, driverless cars, and robotic surgery. Few imagine that AI has any role to play in engaging healthcare consumers. Increasingly, it does.
Recently, Change Healthcare conducted a survey of payers, providers and consumers about the need for consumer engagement. The results showed a startling gap: Both payers and providers call consumer engagement a top priority and are investing up to a third of health care information technology dollars in it.
Yet almost three quarters of consumers surveyed said their experiences with providers and health plans haven’t improved—or have worsened—over the last two years.
This gap is far more than a marketer’s nightmare: It can mean the difference between lives lost and saved. We know the frustration of missed consumer engagement: We know many patients’ lives could transform if they would visit the doctor or change the behaviors causing and amplifying chronic conditions.
But though we know these patients are “out there,” we cannot easily identify them or, if we can, our emails, messages, and phone calls go unanswered. Thus, many consumers aren’t getting cancer screenings; eating more greens; getting treatment for addictions. People continue to suffer needlessly and healthcare costs continue to climb.
To put it bluntly, we are failing to engage our consumers.
The unexpected solution: Artificial intelligence
When most of us think of artificial intelligence (AI), we imagine factory robots, driverless cars, or robotic surgery. Few imagine that AI has any role to play in engaging consumers. Increasingly, it can. Consumer engagement is a promising frontier for AI in two ways:
First, it can help us find “missing” consumers.
Consider potential dual-eligible Medicare patients—those Medicare members who could also be enrolled in Medicaid, but aren’t. There are some 6.5 million of them - patients who may forego screenings or treatment because Medicare alone doesn’t cover everything.
Enrolling them in Medicaid would give them more access to health care (and increase Medicare plans’ capitation to cover their care). New healthcare AI platforms, fed “big data,” could help payers precisely identify these members and understand how to best achieve the desired consumer behavior.
Providers can also use AI to predict which patients will need services. Orthopedists, for example, could identify knee-surgery candidates—consumers who may not yet be feeling the agony of worn-out knees, but who will.
Moreover, using AI, we can segment patients by disease state, geography, gender, etc., setting the stage to provide personalized information.
But simply identifying these hidden patients won’t dissolve the consumer engagement gap. More challenging is understanding how to provide them the content they need, want, and will act upon. That’s where machine learning comes in.
Machine learning (ML) —a subset of AI whereby software becomes “smarter” as it ingests more data—can help us determine which channels, and which messages, to use to reach each consumer segment. If a campaign isn’t working, ML can quickly help us fine-tune it.
There are, of course, caveats. One is data security, which is obviously critical; the other is patient privacy. Not only must we anonymize data, but payers and providers ensure wary consumers that patient information is sacred.
We must not only keep it safe and private, but we must say so clearly.
In addition, we cannot parse data in discriminatory ways, and should adopt strict, public-facing policies that state so in no uncertain terms.
A 4-step strategy
To get started:
To use AI to predict consumer need, feed thousands of consumer-level attributes, such as social determinants of health, into AI applications
Cluster data into micro-segments: Medicare plans can cluster it by regions, chronic diseases, etc., to make it easier for communicators to reach the right people, through relevant channels, with personalized content
Use human-centered design-thinking principles, starting with empathy, to discover unmet needs.Combine with the information uncovered by AI to design consumer journeys to meet those needs
Finally, use machine learning to monitor consumer actions, boost successful campaigns (i.e. those spurring behavior change) and replace or improve upon those that are missing the mark.
There is no silver bullet for obliterating the vast consumer engagement gap. But applying AI to the problem—in essence, operating at the intersection of data science, behavioral science, and experience design—holds promise for drastically shrinking it. In the hands of sophisticated, caring marketers, it can help save lives.