Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In this approach, information from electronic health records, clinical trials and day-to-day hospital operations is analyzed in real-time to uncover insights that continuously improve patient care.

The perspective, published June 17 in npj Health Systems, reasons that a smarter, more efficient and more equitable model of care can be created by harnessing existing data to support system-wide learning. Yet, adoption of this model remains limited.

Dr. Peter Steel

“Integrating diverse databases is part of creating a dynamic health care system,” said lead author Dr. Peter Steel, associate professor of clinical emergency medicine at Weill Cornell Medicine and an emergency medicine physician at NewYork-Presbyterian/Weill Cornell Medical Center. “Practitioners will be able to more easily and quickly see what’s working and what’s not; and what’s driving up unnecessary costs.”

Also, contributing to the perspective are Dr. Robert Harrington, the Stephen and Suzanne Weiss Dean of Weill Cornell Medicine, and Dr. Christopher Longhurst and Dr. Gabriel Wardi, both from the University of California, San Diego.

The authors say establishing a learning health system is especially important now as academic institutions are facing financial strain caused by rising research costs, declining margins and growing patient expectations. The perspective is a call to action for academic health centers to make systemic changes by rethinking how they generate and apply knowledge.

Barriers to Implementation

The idea behind this approach is not new—medical researchers first envisioned learning health systems when hospitals transitioned from paper to electronic health records. However, electronic health records were designed primarily for the convenience of clinicians and patients, rather than for researchers and quality improvement initiatives. Data siloes further complicate establishing learning health systems. Information—patient histories, lab results, imaging or billing records—is stored in separate, disconnected systems that don’t communicate with each other.

Consequently, it can often take years to gather and analyze data needed to improve patient care, the authors noted. A functioning learning health system could shrink this time frame to weeks while maintaining ethical, patient-centric research and using strong security systems to ensure patient privacy. Those insights can then be used to revise treatment guidelines, enhance patient safety and spur innovations.

Dr. Robert Harrington

Part of the issue, the authors argue, is insufficient integration between the people focused on clinical care, research and education. Ideally, future doctors could be taught how to use data to efficiently ask and answer clinical questions that will bring together different stakeholders to collaborate.

“A learning health system, powered by AI, has the potential to elevate clinical care and outcomes,” said Dr. Harrington. “When we enable future clinicians to learn from every clinical encounter, we can improve quality and effectiveness in ways we couldn’t before.”

Beyond data organization and analysis, the cost of implementing a learning health system may reach tens of millions of dollars. But the long-term return may be strategic: Years after implementation, health care organizations that successfully utilize this approach could become significantly more competitive than those that do not, the authors said.

Artificial Intelligence is Driving Change

Despite the challenges, the recent advances in artificial intelligence make learning health systems adoption more critical. Patients are starting to expect doctors to leverage AI to deliver personalized, proactive care, but AI depends on clean, well-structured, real-world data. “AI can only fulfill its promise if it’s built on a foundation of learning infrastructure,” Dr. Steel said.

AI tools can analyze huge volumes of medical data quickly, helping doctors spot early warning signs of illness, streamline operations and make faster, more individualized decisions. A learning health system enables essential quality control, ensuring AI tools are continuously monitored for safety, bias and effectiveness.

“Academic medical centers face a rapidly changing funding landscape, even as the costs of technological transformation and administration in health care continue to rise,” Dr. Steel said. “Implementing the learning health system is no longer a theoretical goal, but a strategic imperative.”