Artificial intelligence is entering healthcare at a pace that, according to Donna R. Cryer, risks outpacing the governance structures needed to support it responsibly. Cryer believes hospitals, payers, pharmaceutical companies, and digital health organizations are introducing AI systems into clinical and operational environments without sufficiently involving the people most affected by those decisions, which are the patients.

Cryer, a healthcare executive, attorney, board advisor, and founder of organizations including CryerHealth and the nonprofit Global Liver Institute, believes the healthcare industry now faces an important choice. Leaders can either repeat longstanding mistakes tied to excluding patients from major healthcare decisions, or they can use the emergence of AI as an opportunity to build governance structures correctly from the beginning.

“We have seen the benefits of engaging patients. But actually having patients in leadership roles is the next frontier,” she shares. Cryer points to the evolution of patient engagement across clinical trials and healthcare innovation as evidence that patient involvement 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, too, has shown that patient-informed trial design can improve enrollment efficiency and lead to more patient-focused endpoints, helping achieve health equity. These efforts have also shown to improve enrolment efficacy, improve clinical adoption, while improving acceptance by health assessors and payers.

Even with that progress, Cryer argues that patient engagement often remains positioned as a supplementary exercise. “There’s lived experience that I would bring into the C-suite team that you can’t buy, and you can’t train,” Cryer says. “You have to live it.”

Her concerns have become increasingly focused on AI implementation. Cryer notes that many healthcare organizations are deploying AI systems without consistent governance models or intentional patient representation. She believes the industry is moving quickly to integrate automation and predictive systems while leaving critical questions unanswered regarding consent, accountability, data use, and oversight.

In Cryer’s view, those concerns are already visible in healthcare environments 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 explains. According to Cryer, patients managing chronic and complex illnesses are already integrating AI into daily healthcare decisions, from organizing medical records to analyzing biometric data and evaluating treatment information. Surveys have shown growing consumer adoption of AI-enabled health tools, with one in three adults already using AI for health information.

Cryer believes healthcare institutions should view that momentum as an opportunity instead of a liability. “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, she adds, are contributing to the rapid adoption of AI across healthcare. Workforce shortages, financial strain, and hospital closures continue to place pressure on healthcare systems nationwide. Cryer acknowledges that AI can support care coordination, administrative efficiency, and operational capacity during a difficult period for the industry. 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 says.

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

She says, “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 also believes AI implementation must be tied to measurable improvements in patient outcomes rather than solely 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 establish 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, “we are in 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.