Artificial Intelligence in Perioperative Medication-Related Clinical Decision Support

Area of Science:

• Healthcare technology
• Patient safety
• Medical informatics

Background:

• Medication errors in the operating room (OR) pose significant patient harm risks.
• Clinical decision support (CDS) effectively reduces medication errors outside the OR.
• Perioperative medication errors represent a critical area for safety improvement.

Purpose of the Study:

• To evaluate the potential of clinical decision support (CDS) in preventing medication errors within the operating room (OR).
• To explore the role of artificial intelligence (AI) in enhancing CDS for perioperative safety.
• To identify barriers and requirements for implementing AI-driven CDS in the OR.

Main Methods:

• Review of existing literature on CDS effectiveness in medication error prevention.
• Analysis of the potential application and integration of AI into perioperative CDS.
• Discussion of data requirements, regulatory considerations, and human oversight for AI in CDS.

Main Results:

• CDS has the potential to prevent up to 95% of perioperative medication errors.
• AI can enhance CDS capabilities in data parsing, risk stratification, and patient monitoring.
• Significant challenges exist, including the need for widespread perioperative CDS adoption to generate sufficient training data for AI.

Conclusions:

• Clinical decision support (CDS) offers a promising strategy to mitigate medication errors in the operating room (OR).
• Artificial intelligence (AI) integration into CDS requires substantial perioperative data and regulatory frameworks.
• Human oversight and widespread CDS implementation are crucial for the successful deployment of AI in OR safety.