Strategies for Health System Transformation: Creating a Learning Health System

Why It Matters

Embedding a Learning Health System turns Mount Sinai’s massive patient flow into a real‑time engine for quality and cost improvement, giving the organization a decisive edge in value‑based care.

Key Takeaways

• Launch of inaugural Learning Health System Grand Rounds at Mount Sinai
• Emphasis on data accessibility and real‑time analytics for care improvement
• Cross‑disciplinary governance unites clinicians, researchers, and administrators across institutions
• AI and dashboard integration aim to surface actionable insights
• Quarterly meetings will drive continuous feedback loops and cultural change

Summary

Mount Sinai kicked off its first Learning Health System (LHS) Grand Rounds, a quarterly forum announced by CEO Dr. Brendan Carr. The event brings together clinicians, researchers, administrators and nurses to align the health system around a data‑driven, continuous‑learning model.

Carr highlighted the scale of the enterprise—6 million ambulatory visits, 150 000 inpatient stays—and the need to turn every encounter into actionable intelligence. He praised the new board chair, John Hes, and the data‑infrastructure team for making information visible, searchable and ready for rapid analysis.

Dr. Leora Harwitz, a national authority on LHS, defined the model as the seamless loop from data to knowledge, knowledge to practice, and practice back to data. She warned that the real challenge is not data scarcity but data silos, and she cited AI‑enhanced dashboards as a prototype for surfacing hidden trends in free‑text notes.

If executed, the LHS framework promises faster cycle times for quality improvement, measurable reductions in readmissions, and a culture where nurses and frontline staff can influence system‑wide decisions. For Mount Sinai, the initiative represents a strategic shift toward higher value care, stronger competitive positioning, and compliance with emerging value‑based reimbursement models.

Original Description

This session examines how health systems can transform everyday care into a continuous engine for learning and improvement. Drawing on examples from NYU Langone Health, Dr. Leora Horwitz will highlight how informatics, AI-driven prediction models, and rapid A/B testing improve outcomes, embed best practices into clinical workflows, and create feedback loops that make innovation a routine part of care delivery.