Automated electronic health record-based application for sedation triage in routine colonoscopy

Area of Science:

• Anesthesiology
• Gastroenterology
• Health Informatics

Background:

• Routine endoscopy procedures commonly utilize moderate sedation (MS) or monitored anesthesia care (MAC).
• Choosing between MS and MAC can be complex, influenced by patient data and practitioner preference.
• Cognitive burden exists for practitioners in selecting the appropriate sedation type.

Purpose of the Study:

• To improve decision-making for moderate sedation (MS) versus monitored anesthesia care (MAC) selection.
• To decrease practitioners' cognitive burden in choosing between MS and MAC.
• To leverage patient data within an electronic health record (EHR) for automated sedation decision support.

Main Methods:

• Developed an Excel-based tool using patient data (demographics, comorbidities, medications) from the EHR.
• Implemented the tool as an automated application within the EHR to predict sedation type.
• Utilized written anesthesia-use guidelines and practitioner preferences in the decision-making process.

Main Results:

• Pre-implementation: Nurses spent 4 minutes, practitioners 5 minutes reviewing EHR for sedation decisions.
• Post-implementation: Time spent reviewing EHR reduced to 2 minutes.
• MS use increased substantially, with comparable and low adverse event rates for MS (0.5%) versus MAC (0.6%).

Conclusions:

• The EHR-based application automates and standardizes sedation type determination, reducing subjectivity.
• Adverse event rates and sedation failure did not increase with the application's use.
• Increased use of MS over MAC is expected to decrease healthcare costs.