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—the patients.
Key Facts
- Donna R. Cryer is a healthcare executive, attorney, board advisor, and founder of CryerHealth and the nonprofit Global Liver Institute.
- She argues that patient engagement has improved clinical trial recruitment and outcomes, but formal patient leadership at the executive level remains rare.
- Many healthcare organizations deploy AI without consistent governance models or intentional patient representation, risking consent, accountability, and data use issues.
- Cryer advocates for a Chief Patient Officer role to integrate patient experience into strategy, governance, and decision-making.
- She insists AI systems must be evaluated on patient outcomes, not just operational metrics.
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. For example, patient-informed trial design has been shown to improve enrollment efficiency and lead to more patient-focused endpoints, helping achieve health equity. These efforts have also improved clinical adoption and 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 own journey as a patient living with a chronic condition gives her unique insight into the gaps between institutional decision-making and real-world patient needs. She has testified before Congress, advised the FDA, and served on numerous boards, always advocating for the patient voice at the highest levels.
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. For instance, ambient AI recording systems in clinics can capture conversations without patients fully understanding how their information is processed or retained. Algorithm-driven workflows may prioritize efficiency over equity, potentially reinforcing biases present in historical data.
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. Surveys show that one in three adults already uses AI for health information, and patients managing chronic and complex illnesses are integrating AI into daily decisions—from organizing medical records to analyzing biometric data and evaluating treatment options.
“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. This grassroots adoption signals that patients are ready to be co-designers of AI tools, not passive recipients.
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. She points to successful examples of patient co-design in telehealth platforms and medication management apps, where user input led to higher adherence and satisfaction. Extending that approach to AI could yield similar benefits.
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, 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. A Chief Patient Officer could bridge the gap between patient advisory councils and the C-suite, ensuring that patient perspectives shape everything from product design to regulatory strategy.
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.” This ecosystem includes not only formal feedback but also the lived experiences of patients navigating complex systems—knowledge that cannot be gleaned from surveys or analytics alone. For AI governance, such insight is particularly valuable because it can identify unintended consequences that clinical data might miss, such as emotional distress caused by a cold, automated interaction.
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. For example, an AI triage tool should be judged not just on speed but on whether it reduces disparities in wait times across demographic groups. A predictive model for hospital readmission should demonstrate that it helps patients stay healthier at home, not just that it flags high-risk individuals.
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. She draws parallels to the early days of electronic health records, when lack of patient input led to systems that were clunky, non-interoperable, and often frustrating for both clinicians and patients. Learning from that history, she believes the AI era can be different if leadership is inclusive from the start.
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. This means involving patients not just as testers or advisors, but as decision-makers with equal power in governance bodies. It also requires investing in health literacy and digital inclusion so that all patients, regardless of background, can meaningfully participate.
The path forward, according to Cryer, is to treat patient leadership as a strategic imperative rather than an optional add-on. Organizations that pioneer this approach may gain a competitive advantage by building AI that earns trust, reduces liability, and delivers outcomes that truly matter to the people they serve. Those that ignore the call risk repeating the mistakes of the past, but at a scale and speed that could cause far greater harm.