How AI in healthcare needs patient leadership to succeed

The healthcare industry is adopting artificial intelligence at a breakneck pace, but Donna R. Cryer warns that this rapid integration is happening without the governance structures needed to support it responsibly. Hospitals, insurers, pharmaceutical companies, and digital health firms are rolling out AI systems into clinical and operational settings without meaningfully involving the people most impacted by those decisions: the patients.

Cryer, a healthcare executive, attorney, board advisor, and founder of CryerHealth and the nonprofit Global Liver Institute, believes the sector now stands at a critical crossroads. Leaders can either repeat the long-standing pattern of excluding patients from major healthcare decisions, or they can seize the emergence of AI as a chance to build proper governance frameworks from the ground up.

“We have seen the benefits of engaging patients. But actually having patients in leadership roles is the next frontier,” she says. Cryer points to the evolution of patient engagement in clinical trials and healthcare innovation as proof that involving patients improves outcomes.

According to her, pharmaceutical companies and health systems have increasingly recognized that patient inclusion strengthens recruitment strategies and the overall relevance of clinical studies. Research has shown that trial designs informed by patients can boost enrollment efficiency, lead to more patient-focused endpoints, and help achieve health equity. These efforts have also improved clinical adoption and acceptance by health assessors and payers.

Despite this progress, Cryer argues that patient engagement is still often treated as an afterthought. “There’s lived experience that I would bring into the C-suite team that you can’t buy, and you can’t train,” she explains. “You have to live it.”

Her focus has sharpened on AI implementation. Cryer notes that many healthcare organizations are deploying AI systems without consistent governance models or intentional patient representation. The industry is moving quickly to integrate automation and predictive tools while leaving critical questions about consent, accountability, data use, and oversight unresolved.

In Cryer’s view, these concerns are already visible in healthcare settings where patients encounter ambient AI recording systems and algorithm-driven workflows without fully understanding how their information is processed or retained. She also believes many patients are far more technologically engaged than healthcare leaders assume.

“The question is not whether patients are using AI. It’s how they’re using it and which systems work best,” she says. According to Cryer, patients managing chronic and complex illnesses are already incorporating AI into daily healthcare decisions, from organizing medical records to analyzing biometric data and evaluating treatment options. Surveys show that one in three adults already uses AI for health information.

Cryer believes healthcare institutions should view this momentum as an opportunity rather than a risk. “We need to apply patient-centric design to AI, and we need to apply it quickly. Otherwise, we’re going to lose a lot of value in healthcare and a lot of opportunities to efficiently make care better,” she explains.

Operational pressures are driving the rapid adoption of AI across healthcare. Workforce shortages, financial strain, and hospital closures continue to stress the system nationwide. Cryer acknowledges that AI can support care coordination, administrative efficiency, and operational capacity during this difficult period. Her argument centers on how those systems are designed and governed.

“If you just do that in a haphazard fashion without involving patients, you will miss the mark,” Cryer warns.

Part of Cryer’s proposed solution involves formalizing patient leadership at the executive level. She has long championed the Chief Patient Officer role, a leadership position designed to integrate patient experience directly into organizational strategy, governance, advisory, and decision-making. Cryer argues that many organizations already have patient insight groups and community data resources but fail to fully leverage them.

“There’s a whole separate ecosystem of information that’s missing that could be applied to solving problems, whether you’re a pharma company, a health system, or a payer,” she says.

She also believes AI implementation must be tied to measurable improvements in patient outcomes, not just operational metrics. Cryer insists that healthcare organizations should evaluate AI systems based on whether they improve access to care, identify gaps in treatment, support adherence, and strengthen long-term health outcomes.

Cryer ultimately frames the current moment as an opportunity for healthcare leaders to build stronger partnerships between institutions, medical professionals, patients, and policymakers before AI infrastructure becomes deeply embedded across the system.

Healthcare’s AI future, from her perspective, “is a race to see whether the space will be shaped by regulation or technical advancement.” Cryer believes another factor may ultimately determine whether the technology fulfills its promise: whether the people most affected by healthcare systems are finally given a seat at the table before the architecture becomes permanent.