Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture

Area of Science:

• Health Informatics
• Clinical Research
• Health IT Infrastructure

Background:

• The need for robust data infrastructure to support patient-centered outcomes research (PCOR) is critical.
• Leveraging federal investments in health information technology (IT) can enhance clinical data research networks.
• Existing research networks require adaptation and extension to meet evolving research demands.

Purpose of the Study:

• To describe the architecture of the Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS) clinical data research network.
• To demonstrate how SCILHS leverages health IT to create a queryable semantic data model for research.
• To highlight the role of innovative applications in facilitating PCOR and point-of-care research functions.

Main Methods:

• Development of a queryable semantic data model across 10 health systems, covering over 8 million patients.
• Integration of the network at the point of care to enable real-time data generation and research participation.
• Creation of specialized 'apps' to enhance PCOR methods and support functions like consent, enrollment, and patient-reported outcomes.

Main Results:

• The SCILHS network provides a scalable and collaborative infrastructure for learning healthcare systems.
• The architecture enables seamless integration of clinical data for research purposes across multiple health systems.
• Innovative applications facilitate clinician and patient engagement in research during patient encounters.

Conclusions:

• The SCILHS architecture effectively enables patient-centered outcomes research by integrating diverse data sources.
• The use of modular, open-source components and innovative apps supports a flexible and scalable research infrastructure.
• SCILHS demonstrates a successful model for leveraging health IT to advance research and improve patient care.