Operationalizing the Pan-Canadian Health Data Charter Through Common Standards and AI-Enabled Nursing Data

Area of Science:

• Health Informatics
• Nursing Informatics
• Artificial Intelligence in Healthcare

Background:

• Pan-Canadian Health Data Charter emphasizes common standards for health data interoperability.
• Nursing documentation is crucial for patient safety and care continuity but remains largely unstructured and siloed.
• Structured datasets often lack early indicators of patient deterioration and social context captured in nursing notes.

Purpose of the Study:

• To explore the potential of Artificial Intelligence (AI) in standardizing nursing documentation.
• To examine how AI can overcome barriers to health data interoperability and portability.
• To demonstrate the benefits of leveraging AI for clinical decision-making and patient care.

Main Methods:

• Review of current evidence on AI applications in healthcare data.
• Analysis of institutional examples of AI implementation for clinical documentation.
• Examination of AI's capability to translate narrative nursing notes into standardized formats.

Main Results:

• AI tools can effectively translate unstructured nursing narratives into standardized, interoperable data.
• Leveraging AI offers a practical alternative to traditional standardization methods requiring behavioral change.
• AI can unlock the value of nursing data for improved patient safety and continuity of care.

Conclusions:

• Artificial Intelligence presents a viable strategy to achieve common health data standards.
• Standardizing nursing data through AI enhances its utility for patient safety and care coordination.
• AI implementation can realize the benefits of data interoperability without increasing clinician workload.