Optimizing Care Delivery and Outcomes DIGH 7300

This course focuses on decision-support principles, standards and best practices to optimize quality-driven care and outcomes. Learners evaluate frameworks for continuous improvement and innovation in health care (e.g., learning health system). The course covers methods of workflow analysis, and the principles of workflow re-engineering. Learners explore data associated with workflow processes and clinical context and the development of data flow diagrams. Learners examine person-facing health information applications (e.g., patient portals, mobile health apps and devices, disease management, patient education, behaviour modification). Learners also examine methods (e.g., portals, apps, strategies for patient engagement, education and outreach) to connect health information systems to patients. Learners examine the use of clinical data registries for quality improvement and decision-making. Learners explore how specialized and emerging data sources can be used to enhance clinical decision-making for improved health outcomes. The course discusses how better use of health data through specialized and emerging data sources, data quality, and standards compliance can help improve outcomes. The course explores the importance and implications of perception and cognition related to data visualization in optimizing care delivery and outcomes. Learners study collaborative change and principles and practices of quality improvement (e.g., Six Sigma, Lean, the Plan-Do Study-Act (PDSA) cycle, root cause analysis) and their use in digital health projects and implementations to optimize care outcomes.

Domestic fees

$963.23