Artificial intelligence (AI) has the power to dramatically transform healthcare—and in many ways, it already has. The use of AI in patient engagement programs is just one example of how it can encourage healthy behaviors, adherence to treatment, and proactive self-management—all with the ultimate goal of improving patient outcomes.
How are AI-enabled strategies reshaping patient engagement? We’ll fill you in:
Evolving Views on Patient Engagement
It’s a common belief among healthcare stakeholders that in order to successfully promote engagement, they have to reach out to as many patients as possible—and get as much data from them as possible—in order to be effective.
What we’re learning more and more is that this approach is not only inefficient, but also ineffective. Every healthcare consumer is unique, and taking a cookie-cutter approach to patient engagement is no more than a shot in the dark.
Fortunately, advancements in AI technologies are slowly but surely changing how these stakeholders think about engaging with patients. Rather than inundating patients with a slew of phone calls that may or may not be engaging, pharmacists, payers, and manufacturers are now focused on personalizing their outreach to proactively target the patients that need it most.
Targeting the Right Patients at the Right Time
By leveraging large datasets from a number of different sources—medical claims data, consumer behavior data, social determinants of health, and historical program data—we can now anticipate which patients are at risk of non-adherence or gaps in care. On top of that, AI helps determine which of those high-risk patients are most likely to benefit from and be influenced by an intervention.
AI enables healthcare organizations to make smarter, more strategic decisions around patient engagement by targeting patients with the greatest likelihood of changing their behaviors. Using data-driven insights, healthcare organizations can prioritize patient outreach, fine-tune which individuals to engage with, and determine the frequency and timing of said communications.
Personalizing Interventions for Better Outcomes
From there, AI learns and accurately predicts which intervention is best to effectively influence a patient’s behavior. Although in-person and phone interventions were once the norm, now healthcare organizations are embracing omnichannel patient engagement strategies that include text messaging, email, web chat, direct messaging, video, telehealth visits, in-person visits, and phone calls.
Personalizing patient interventions with custom-tailored interactions allows organizations to determine who will benefit more from higher-touch intervention methods—such as in-person appointments—or more cost-effective channels, such as text messages or apps. However, personalized outreach not only requires using omnichannel technologies to engage patients, but also determining the right messaging strategy.
When you deliver personalized engagements to the right patients via the right channels at the right time, getting the messaging right is essential. AI can help with that too.
Pulling from the same varied datasets used to make predictions and optimize interventions over time, AI empowers healthcare organizations to figure out not only who to engage with, where to interact, and when to reach out, but also what to say to empower patients to take a more active role in their own care.
As a result, pharmacies, payers, and pharmaceutical companies can deliver the most effective engagement possible—and it’s all thanks to artificial intelligence.
Interested in learning how AllazoHealth’s AI technology can promote better patient engagement, improve the effectiveness of support programs, and boost quality outcomes for your organization? Request a live demo to see it in action.
About the Author
Dev joined AllazoHealth to help drive healthcare innovation. As VP, Business Development, Dev leads commercial and strategic activities to drive the company’s growth. Before joining AllazoHealth as employee #2, Dev held strategic roles at scaling digital health companies including CipherHealth & CareDox. These operating leadership roles included spearheading business development activities as well as financing initiatives such as managing a Series B round and acquiring a tech enabled services business. Earlier in his career, Dev spent several years at Booz & Company and Cognizant within their transaction services and healthcare practices.