Artificial intelligence is entering healthcare at an unprecedented pace, but according to healthcare executive and patient advocate Donna R. Cryer, the industry risks outpacing the governance structures needed to deploy it responsibly. Cryer argues that hospitals, insurers, pharmaceutical companies, and digital health organizations are introducing AI systems into clinical and operational settings without sufficiently involving the people most affected by those decisions: the patients.
Cryer, who is an attorney, board advisor, and founder of CryerHealth and the nonprofit Global Liver Institute, believes the healthcare industry now faces a critical fork in the road. Leaders can either repeat longstanding mistakes of excluding patients from major decisions, or they can seize the emergence of AI as an opportunity to build governance structures correctly from the outset.
The evolution of patient engagement
Patient engagement in healthcare has advanced significantly over the past decade. Cryer points to clinical trials as a prime example. Pharmaceutical companies and health systems have increasingly recognized that involving patients in trial design improves recruitment, retention, and the relevance of study endpoints. Research shows that patient-informed trial design can boost enrollment efficiency and lead to outcomes that better reflect what matters to patients, such as quality of life and functional status. These approaches also help advance health equity by ensuring that diverse populations are represented.
Despite this progress, Cryer argues that patient engagement remains largely a supplementary exercise. Too often, patients are asked for feedback after key decisions have already been made. Their insights are gathered through surveys or advisory panels that lack real decision-making power. "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."
AI in healthcare: a governance vacuum
Cryer's concerns have become increasingly focused on AI implementation. She notes that many healthcare organizations are deploying AI systems without consistent governance models or intentional patient representation. The pace of adoption is driven by promises of efficiency, cost savings, and improved outcomes, but critical questions about consent, accountability, data use, and oversight remain unanswered.
These issues are already visible in everyday healthcare. Patients encounter ambient AI recording systems during consultations, algorithm-driven workflows that determine treatment pathways, and predictive models that influence insurance coverage. Yet they often have little understanding of how their information is processed or retained. Cryer believes that healthcare leaders underestimate how technologically engaged patients have become. "The question is not whether patients are using AI. It's how they're using it and which systems work best," she explains.
Surveys indicate that one in three adults already uses AI for health information, such as analyzing biometric data from wearables, organizing medical records, or evaluating treatment options. Patients managing chronic and complex conditions, in particular, are integrating AI into their daily healthcare decisions. Cryer argues that healthcare institutions should view this momentum as an opportunity rather than 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 says.
The role of operational pressures
Operational pressures are accelerating the adoption of AI across healthcare. Workforce shortages, financial strain, and hospital closures continue to stress the system. Cryer acknowledges that AI can support care coordination, administrative efficiency, and operational capacity during this difficult period. However, she emphasizes that the way these systems are designed and governed matters immensely. "If you just do that in a haphazard fashion without involving patients, you will miss the mark," she warns.
AI applications range from automating administrative tasks like scheduling and billing to complex clinical decision support tools that analyze medical images or predict patient deterioration. Without patient input, these tools may embed biases, misunderstand patient preferences, or fail to address real-world needs. For example, an algorithm designed to allocate resources might prioritize efficiency over equity, leaving vulnerable populations underserved.
Formalizing patient leadership
Part of Cryer's proposed solution involves formalizing patient leadership at the executive level. She has long advocated for the creation of a Chief Patient Officer role, a position designed to integrate patient experience directly into organizational strategy, governance, advisory, and decision-making. Cryer stresses that this is not about tokenism; it is about harnessing a unique perspective that data alone cannot provide. Many organizations already have patient insight groups and community data resources, but Cryer argues they 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.
Several major health systems and pharmaceutical companies have begun appointing Chief Patient Officers, but the role remains uncommon. Cryer believes that as AI becomes more embedded, the need for such leadership will only grow. The Chief Patient Officer would ensure that patient voices are heard during the design, implementation, and evaluation of AI systems, from data collection to outcome measurement.
Measuring what matters
Cryer also insists that AI implementation must be tied to measurable improvements in patient outcomes, not just operational metrics. 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. This shift requires rethinking success criteria. Currently, many AI systems are judged by how much they reduce costs or shorten wait times, but patient-centered measures like satisfaction, health literacy, and shared decision-making are often overlooked.
For instance, an AI-powered chatbot that schedules appointments might reduce administrative burden but could frustrate patients who need human interaction for complex queries. A predictive model that flags patients at risk of readmission might be effective but could stigmatize individuals if not implemented with empathy and transparency. Patient involvement helps ensure that AI tools serve people, not just processes.
Historical context and the path forward
The healthcare industry has a history of excluding patients from decisions that profoundly affect their lives. From clinical trial design that ignores diverse populations to electronic health records that prioritize billing over usability, the pattern is clear. Cryer believes that AI presents an opportunity to break this cycle. "We are in a race to see whether the space will be shaped by regulation or technical advancement," she says. But she considers another factor even more decisive: whether the people most affected by healthcare systems are finally given a seat at the table before the architecture becomes permanent.
Regulatory frameworks for AI in healthcare are still evolving. The FDA has approved hundreds of AI-enabled medical devices, but oversight of algorithmic decision-making in administration and insurance remains fragmented. Cryer advocates for proactive governance that includes patients as co-creators, not just end users. This means involving patient representatives in algorithm design, data governance boards, ethics committees, and policy discussions.
Cryer's own career illustrates the power of patient leadership. Diagnosed with a chronic liver condition, she transformed her personal experience into advocacy and organizational leadership. The Global Liver Institute, which she founded, works to improve liver health through patient-centered research, education, and policy. Her perspective underscores that lived experience is not a weakness but a unique expertise that can inform better decisions.
As AI continues to reshape healthcare, the window for inclusive governance is narrowing. Cryer's message is clear: involve patients now, or risk building a system that repeats past failures at a larger scale. The technology is advancing rapidly, but the choices about how it is deployed remain in human hands. Those hands must include patients."