The Q‑OSI Model: A Layered Systems Framework for HEDIS & UDS Closure (Preprint)

UNSTRUCTURED An emerging systems-engineering framework, the Q‑OSI (Quality Open Systems Interoperability) Model reconceptualizes HEDIS and UDS “gaps in care” as layered failures across a quality performance stack rather than isolated clinical or documentation problems. Drawing an analogy to the OSI model in network communication, Q‑OSI defines seven interdependent layers—Compliance & Reporting, Measure Logic, Structured Data, Workflow Execution, Clinical Decision, Care Coordination, and Patient Activation—through which a quality “signal” must successfully transmit for a measure to close. In current practice, missed HEDIS and UDS targets are often attributed globally to “clinical performance” or “poor documentation,” obscuring where in the end‑to‑end pipeline failures actually occur and leading to diffuse, non-specific interventions. The Q‑OSI Model instead asserts that most gaps are interoperability issues between technical, workflow, and behavioral layers: for example, an A1C result documented as free text (Structured Data failure), a mammogram order never scheduled (Workflow Execution failure), or a patient never contacted for outreach (Care Coordination failure), even when the underlying clinical decision is appropriate. By providing a simple, memorable, seven-layer map, the framework enables quality and informatics teams to classify defects by layer, align interventions more precisely (eg, templates and coding at Layer 3, standing orders at Layer 4, SMS automation at Layer 6), and monitor whether remediation efforts are addressing the true bottleneck. For public health informatics, Q‑OSI offers a practical bridge between population health measurement, data standards, clinical operations, and patient-facing engagement, positioning quality improvement as an engineering discipline grounded in layered interoperability rather than a reactive cycle of measure chasing. This Viewpoint introduces the Q‑OSI Model, illustrates its use with common HEDIS scenarios, and outlines how it could inform maturity models, dashboard design, and implementation research in settings such as Federally Qualified Health Centers and safety-net systems.