Healthcare organizations have never had more data about how people use their digital products. Product teams can see where a patient abandons an onboarding flow, how long a member lingers on a benefits page, and which screens clinicians skip. Yet in my work with health systems, payers, healthtech companies, and life sciences firms, one question consistently stops product teams cold: What was this patient feeling when she gave up?
Most teams can’t answer it. That gap, between knowing what people do and understanding why they do it, is quietly undermining the return on healthcare’s technology investments. And as AI makes capabilities easier to copy, it is becoming the gap that separates leaders from everyone else.
Product analytics were designed for a world in which users are customers making discretionary choices. Healthcare is different. A person using a patient portal or a health plan app is often in pain, frightened, exhausted, or acting on behalf of someone else. A 70-year-old woman with arthritis who abandons onboarding at step three may not be disengaged. She may be struggling to tap small buttons, fatigued by the fourth screen, or anxious that a handoff to her provider’s records system has lost her information.
To a dashboard, these situations look identical: a drop-off. To the person, they are entirely different experiences, and each calls for a different design response.
Healthcare has paid for this blind spot before. As Stanford School of Medicine dean Lloyd Minor has pointed out, the electronic health records required under the 2009 HITECH Act were built largely around billing and often neglected the practical needs of doctors and patients, adding to burnout. AI risks repeating the same mistake.
Empathy has long been treated as a soft skill. The evidence says otherwise. A global survey of nearly 12,000 consumers across 11 countries found that people’s sense that a company understands how they feel matters more to their purchasing decisions than reviews or recommendations. Yet 78% said businesses don’t genuinely care about them.
In healthcare, where people engage with organizations at their most vulnerable, that gap matters even more. A member who doesn’t feel understood doesn’t just churn. She delays care, disengages from programs designed to help her, and loses trust in the system.
Many organizations are turning to generative AI to speed up research and design. The appeal is understandable, but the results so far are sobering. An MIT report found that 95% of generative AI projects fail, and in a National Bureau of Economic Research survey of more than 6,000 executives, roughly 90% reported no measurable productivity gain from AI over three years.
In healthcare, the problem is not only return on investment but context. In one documented case, a generalist AI model reviewing prior authorizations denied a knee MRI because it couldn’t find six weeks of physical therapy. It missed that the patient’s prior ACL repair qualified them for imaging right away. The model read every word. It didn’t understand what the words meant together.
Technology alone is also no longer a lasting advantage. One precision-medicine company spent about $20 million over three years building a custom AI solution, only to watch a widely available model make key parts of it easy for competitors to copy. And in healthcare, the end user, a patient or clinician, is often not the one who pays. Products that win must work for both.
When capabilities are easy to copy, deep understanding of the people using a product becomes one of the few advantages that isn’t.
The organizations making real progress are adopting what I call digital empathy: helping the people who build healthcare products understand, and in some sense experience, the conditions of the people who use them.
Digital empathy rests on three principles.
When teams work this way, a broken user flow stops being an item in a backlog and becomes a person the team is motivated to help. That shift in motivation is often what separates products that get adopted from those that merely launch.
Digital empathy is less a technology investment than a leadership choice. Executives can begin with four questions:
None of these steps requires a large budget. They require curiosity, discipline, and a willingness to understand before optimizing.
Healthcare technology is entering a period in which features will be easy to copy and AI will be widely available. The difference will come from something harder to copy: a deep, validated understanding of the people behind every data point.
The organizations that lead the next decade will not be the ones with the most dashboards. They will be the ones that elevate patient, clinician, and member stories and build technology worthy of them.
Kristine Howell is the Founder of Oliven Labs, a women-owned, human-centered strategy consultancy serving health systems, payers, healthtech, and life sciences.
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