In the wake of a global pandemic, disrupted supply chains and a shifting workforce, healthcare systems are grappling with growing complexity and the accelerated introduction of artificial intelligence (AI). These challenges underscore the urgency of adopting learning health systems (LHSs) that integrate research with care delivery through continuous feedback. By combining new technologies, iterative learning and patient engagement, LHSs offer a framework for sustainable, responsive and efficient healthcare transformation. A collaborative effort by the Future of Health and the Duke-Margolis Institute for Health Policy has identified three core areas to advance this vision: data-driven decision-making, supportive organisational culture and meaningful patient involvement.
Enabling Smarter Decisions Through Data
The foundation of modern learning health systems lies in harnessing real-world data and advanced analytics to guide clinical decisions. While the proliferation of electronic health records and diagnostic tools has expanded data availability, barriers such as data silos and limited access still impede progress. Synthetic data offers a potential solution by replicating the structure of real-world data without exposing patient identities. Institutions like Sheba Medical Center in Israel and The Ottawa Hospital in Canada have successfully deployed synthetic datasets to accelerate operational and clinical insights. These examples show how data analytics tools can enable frontline teams to rapidly test hypotheses, identify cost-saving measures and improve outcomes.
Must Read: Advancing Healthcare Systems: EHMA Framework for Health Management
The implementation of AI is another pillar of data-driven care. Systems such as Sheba's ADAMS Center are using AI to analyse imaging and drug use patterns to inform decisions and reduce unnecessary hospitalisations. These applications, however, depend on access to diverse and representative datasets to avoid bias and ensure safe integration. Collaborative efforts like the Coalition for Health AI aim to address these concerns by developing shared standards and best practices, creating an ecosystem that supports ethical and effective use of technology in healthcare.
Building a Culture That Supports Continuous Learning
Beyond technology, learning systems rely on a culture of openness, leadership and alignment. A supportive environment empowers clinicians and staff to experiment, evaluate and refine practices based on data and feedback. The Healthier Singapore initiative exemplifies this approach by encouraging population-wide preventive care and coordination across medical and community services. Institutions involved in this initiative, such as SingHealth, have embraced iterative evaluation methods to measure progress and guide improvements.
This transformation is reinforced by robust leadership that champions transparency and learning. Studies conducted within Singapore’s healthcare system have explored governance models, frontline experiences and the barriers to long-term improvement. These insights highlight the importance of leadership that enables distributed decision-making and fosters a commitment to shared goals. Through such cultural shifts, health systems can move from reactive service delivery to proactive, data-informed care.
Placing Patients at the Centre of Learning
An effective LHS must include patient and caregiver voices to ensure that health improvements reflect real-world priorities. Patient engagement is not only about feedback but about collaboration throughout the care improvement process. International networks such as Healthier Together, coordinated by the James M. Anderson Center at Cincinnati Children’s Hospital, illustrate how involving patients can improve care quality across institutions. These networks bring together clinicians, families and scientists to co-create solutions that are both scientifically sound and practically relevant.
Examples of coproduction in paediatric heart failure and inflammatory bowel disease show how patients can help develop education tools and care strategies. Additionally, hospitals like The Ottawa Hospital and Massachusetts General have embedded patient advisory councils to shape operational decisions, such as improving digital communications and patient experience. These initiatives confirm that when patients help design care processes, the resulting systems are more accessible, equitable and aligned with their needs.
Learning health systems offer a transformative path for global healthcare by promoting data-informed decisions, fostering an adaptive culture and anchoring improvements in patient experience. By investing in technologies like synthetic data and AI, cultivating leadership and organisational alignment and engaging patients as active partners, healthcare organisations can evolve into resilient, continuously improving entities. This shift promises a more responsive, efficient and person-centred future for health systems worldwide.
Source: Health Affairs Scholar
Image Credit: iStock
