Health system and hospital executives see the promise of automation and artificial intelligence to customize patient outreach and help address persistent engagement challenges.
AI-powered personalization technology can nudge patients to take action, which could help address long-standing challenges with medication adherence while also reducing no-show rates and closing care gaps.
According to a recent survey from Lirio and Sage Growth Partners, 60% of executives from U.S. health systems, independent hospitals and physician groups cited automating patient outreach to ease administrative burden as their top priority. Most executives (96%) said AI and automation can reduce administrative burden related to patient engagement. About half of executives said their organizations struggle to personalize engagement content, such as text messages or emails, at the patient level.
But this operational priority doesn't currently match up with AI investment strategies.
Among the surveyed executives, 35% said their organizations have yet to make investments in AI tools for patient outreach. At the same time, most health systems are channeling AI investment into operational efficiency. The majority of executives (83%) said their organization has invested in AI-based solutions for automated documentation and other tasks to improve clinician workflows.
"The disconnect between AI priorities and investments often comes down to the speed at which different technologies demonstrate value," Amy Bucher, Ph.D., chief behavioral scientist at Lirio, told Fierce Healthcare.
"Tools that automate operational tasks, like note-taking or billing, deliver immediate, visible benefits. Staff gain time back, workflows improve, and the ROI is clear. In contrast, patient engagement technologies require more time to show their full impact. Meaningful outcomes such as improved population health or reduced avoidable costs depend on patients changing behaviors—scheduling appointments, adhering to medications, or making lifestyle adjustments," she said.
For example, lowering HbA1c in patients with diabetes may take weeks or months after engagement begins.
"Under financial pressure, health systems understandably gravitate toward quick wins, especially given the cost and complexity of implementing new technology," Bucher said.
Lirio combines artificial intelligence and behavioral science to deliver adaptive, personalized outreach that helps patients overcome barriers to action, according to Lirio executives.
"When someone isn’t taking a recommended health step, there’s always a reason, whether it’s lack of awareness, personal beliefs, past experiences, or access challenges. Simply telling people what to do isn’t enough. Our approach uses behavioral science to address these barriers in ways that educate and empower, equipping patients to act now and in the future," Bucher noted.
The recent survey shows hospitals are struggling to use AI for patient engagement. Only 5% of respondents to Lirio's survey said they are very satisfied with their technology capabilities to support medication adherence and only 10% are satisfied with their tech tools to engage patients about lifestyle modifications.
Executives identified several inefficiencies with patient outreach that place burdens on care teams, including following up with patients who miss or cancel appointments, coordinating care across multiple providers or service lines, managing communication across multiple channels, tracking or closing care gaps and making sure patients understand instructions and take appropriate steps.
More than half of respondents also said that, in addition to cost, the need for highly defined ROI is the top factor
limiting their ability to adopt solutions such as those that support automated outreach for patient lifestyle
modifications or preventative health campaigns. Executives indicate that advanced patient engagement platforms would have the highest value in areas that are directly linked with high reimbursement potential. Those include chronic care management and gap-in-care notifications.
The survey results also signal that health systems need to take a fresh look at AI investment strategies and include patient outreach as a top priority, Lirio executives said.
"To shift investment strategies, we need to rethink how success is measured. Those of us working with agentic patient engagement technologies can help by highlighting leading indicators that predict long-term value. Engagement doesn’t have to be slow. There are opportunities for early wins that align with health system priorities," Bucher said.
"For instance, conversational AI can quickly improve data quality by capturing information about care patients receive elsewhere. This not only enhances outreach strategies and reduces costs but also creates a better patient experience. By combining short-term gains with a clear path to long-term outcomes, health systems can confidently invest in technologies that truly transform care," she said.
Covenant Health, a Tennessee-based health system that includes 10 hospitals along with outpatient clinics and specialized behavioral, oncology and rehabilitation facilities, has been working with Lirio since 2023 to use its AI-based solutions for patient outreach about preventative health screenings.
"To date, we have seen thousands of patients who may have otherwise missed their screenings," Mandy Grubb Halford, M.D., senior vice president, chief medical officer and chief medical informatics officer at Covenant Health, told Fierce Healthcare.
"Covenant Health aggressively seeks technologies that improve patient experience, patient engagement and operational efficiencies. Creating easy patient engagement is one of Covenant Health’s pillars in technology adoption," Halford noted.
She added, "The healthcare technology landscape is rife with AI tools that make many promises. As an organization, Covenant Health is dedicated to improving patient outcomes, and we have prioritized patient engagement tools that deliver results in our patients’ lives."
AI can make personalized patient outreach scalable, Lirio executives said.
"Reinforcement learning selects the right behavioral strategies from our content libraries based on each patient’s profile and continuously refines choices based on behavioral responses. Our multi-agent system ensures engagement remains dynamic across different behaviors, organizations and time, avoiding static segmentation. These interactions feed our Large Behavior Model (LBM), which learns from real-world responses to become increasingly precise and effective. The result: engagement that is not only personalized but predictive, driving better health outcomes at scale," Bucher said.
Editor's Note: This story has been edited to accurately identify Lirio executive Amy Bucher's Ph.D. academic degree.