As AI becomes more embedded in everyday life, patients increasingly expect the same level of digital convenience and personalization from their healthcare providers.
In a recent Becker’s Healthcare webinar, AI strategist and healthcare futurist Ron Galloway joined Cary Bran, vice president of product AI, insights and reporting at Reputation, to explore how health systems can harness AI not just to react to patient needs, but to anticipate and exceed them.
Here are four key takeaways from the discussion:
1. AI enables proactive engagement
Mr. Galloway emphasized that the most transformative power of AI in healthcare isn’t just automation, it’s anticipation. AI’s ability to analyze massive amounts of data and identify latent patterns makes it uniquely positioned to predict shifts in patient sentiment and behavior before they emerge.
“AI allows us to see more than we could see before,” Mr. Galloway said. “The feedback loop is quicker and It picks up patterns that humans wouldn’t normally think to look for.” He cited a study in The Lancet showing that a radiologist paired with an AI outperforms two radiologists reading scans, underscoring AI’s potential to spot subtle diagnostic signals.
This predictive capability can directly translate to the patient experience. For instance, social media posts, online reviews and wearable data can be synthesized to detect early signs of dissatisfaction or care gaps, giving providers an opportunity to intervene early.
2. Patient expectations are rising fast
AI is raising the bar for what patients expect from healthcare. From personalized Google search results to real-time updates on airline boarding, consumers are becoming accustomed to seamless, AI-driven interactions. Healthcare must evolve in parallel.
“What you have to do is consider the cost of ignoring all this data and ignoring patient expectations,” Mr. Galloway said. “You have to figure out how to use the tools at hand to make everything better for the patient because if you can decrease any friction, it’s a wonderful thing.”
Reputation’s platform aggregates millions of patient reviews to identify friction points across the care journey. Bran noted that communication quality, wait times and perceived loss of control consistently drive negative sentiment. “We can track that journey and break it down with precision,” Mr. Bran said.
3. Real-time feedback loops enhance operational agility
By combining AI with agent-based systems, health systems can move from static dashboards to dynamic workflows. These “agentic” models can analyze sentiment in real time, generate alerts and even recommend (or initiate) actions to resolve issues, dramatically shrinking the feedback-to-action cycle.
“AI is a time machine,” Mr. Galloway said. “It saves you hours a day. When you’re working and trying to improve the patient experience, it goes a lot faster than you were able to in the past because it scales so incredibly quickly and now you can automate it through agents.”
He added that advances in code generation allow nontechnical staff to build custom dashboards or reports without programming experience, empowering departments to track performance metrics that matter to them.
4. Context matters
AI’s power depends not just on data volume, but on understanding context. Galloway warned that sentiment analysis must account for semantics and regional dialects, for example interpreting “positive test” correctly in a cancer care setting.
Mr. Bran added that HIPAA compliance requires AI responses to be carefully designed. To him one of the key priorities in AI application is that models need to deeply understand context before taking action.
One case study involved refining AI models for a coastal community where residents speak Gullah, a unique dialect. It took 18 months to train the AI to interpret this language for use in healthcare sentiment analysis.
As AI becomes more embedded in patient engagement strategies, the margin for error and the opportunity for innovation grows. Both Mr. Bran and Mr. Galloway concluded that health systems must act quickly to adapt, not only to meet regulatory and operational demands but to align with a new generation of patients who expect more, faster.
“Use AI not just to react to patient feedback, but to anticipate and shape tomorrow’s expectations,” Mr. Galloway said. “We can use these tools to put it all together to empower staff and make the patients’ experience much more pleasant.”
