Gregory Gallivan’s newly released book, “Healthcare: Compassion in Action,” has a lot to say about AI in health care and health outcomes. Gallivan perceives artificial intelligence as instrumental in profoundly changing health care through claims workflows and back-office automation. However, Gallivan feels AI’s biggest impact will be at the most human point in the system: the patient experience.
“AI can tailor a genuinely individualized care plan for each person,” observes Gallivan. “It can replace the generic protocols that too often define modern care. At a time when many patients feel like health care has become a carousel of rushed appointments and repeated questions, AI can help to restore the time and trust that has been squeezed out of the clinical encounter.”
How AI streamlines personalized healthcare in a world of digital health
When patients long for a healthcare system that delivers personalized care, they are looking for a patient-centered system that does not treat them as an average case defined by a checklist. Clinicians want to understand the patient’s full story and create a proactive treatment plan for the person in front of them. Yet in practice, patient history is scattered across portals and years of fragmented visits. That fragmentation is a major reason so many patient journeys still begin with triage and generic pathways. Clinicians must first spend time reconstructing the basics.
Gallivan argues that AI makes the full picture usable in real time. When AI systems are fed a patient’s metrics and medical history, complete with labs and medication lists, they accelerate a clinician’s ability to understand each individual.
Gallivan connects this capability to a bigger movement in healthcare. “The future of medicine is preventive care,” he predicts. “Your risk will be identified earlier because your monitoring will be virtually continuous. Your physicians will offer interventions to prevent deterioration instead of reacting to medical issues. The whole experience will become less about waiting for problems to worsen and more about receiving a plan that’s just for you.”
The practical result is care that is personalized to each patient’s behavioral realities and risk factors. It will even account for the barriers that prevent the patient from following their prescribed plan. In Gallivan’s opinion, personalization is what compassionate care looks like when it is engineered to work for real people.
Benefits of personalized patient care: How AI cuts the delays that come from manual coordination
Many patients describe their experience with healthcare as chasing referrals and repeating the same story from office to office. They mention waiting for results, then finding out one provider never received another provider’s notes.
Gallivan emphasizes that adherence to these delays is not only frustrating; it can be clinically dangerous. They also erode trust and force patients into the role of project manager for their own care.
AI can reduce that friction by handling coordination in ways that patients feel directly. For example, systems can schedule follow-up care automatically at the end of a visit based on clinical needs and patient availability. It can route lab results and visit notes instantly to every relevant clinician and care team member. It can flag missing documentation before a referral stalls or a procedure is delayed. And it can send reminders that match a patient’s capabilities, especially when cognition or daily functioning is compromised.
From the patient’s perspective, this translates into fewer repeated forms and fewer weeks of anxious waiting. Care begins to feel guided again, as if the system is truly tracking the entire journey.
How AI’s redundancy checks increase accuracy and safety with healthcare providers
One of AI’s most important benefits is its capacity to cross-check and flag inconsistencies across massive volumes of health data. That redundancy can make all the difference because modern care is complicated.
Today’s patients often see multiple specialists and maintain long medication lists. They also experience subtle changes in lab values that can be easy to miss in a rushed encounter. AI can act as a second set of eyes, continually scanning for medication interactions, missed follow-ups, abnormal trends, contraindications, or diagnostic possibilities that do not fit the typical script.
When AI removes administrative friction, it gives doctors back the time to know their patients again. “The most meaningful promise of AI is relational,” says Gallivan. “When systems reduce time-consuming administrative work, clinicians gain capacity to really see and hear their patients. They can take the time to follow each case and educate each patient. When we restore time to the clinical relationship, we can move healthcare beyond the 15-minute, twice-a-year pattern that leaves many patients feeling unknown.”
Gallivan imagines tools that will identify danger the very first day a cell becomes precancerous. These tools will dramatically change outcomes through earlier intervention and cleaner clinical decision-making.
Even before those advanced scenarios fully arrive, AI-driven redundancy can improve today’s patient safety with better clarity across teams and care decisions that align with the patient’s unique risks rather than generalized assumptions.
The author’s new book calls for a system that returns to medicine’s original purpose. “AI can be the tool that enables us to care about care,” he concludes. “It will never replace physicians, but it can restore attention to the patient as an individual and strengthen the relationship that makes care effective.”
