7-Year Experience with Automated Pupillometry and Direct Integration With the Hospital Electronic Medical Record.

Area of Science:

• Neurology
• Medical Informatics
• Critical Care

Background:

• Manual pupillary assessment is crucial for neurologic evaluation in critically ill patients.
• Automated pupillometry offers reliable and consistent measurement of light response.
• Integrating pupillometry data into electronic medical records (EMR) can streamline clinical workflows.

Purpose of the Study:

• To report the implementation of a computer interface for direct download of pupillometer data to the hospital EMR.
• To evaluate nursing acceptance and workflow efficiency following the integration of automated pupillometry data into the EMR.

Main Methods:

• Developed a computer interface for direct pupillometer data download to the EMR.
• Conducted a prospective study surveying nurses using pupillometers in 2015, 2018, and 2020.
• Utilized a 5-point Likert-style scale to assess the acceptance and utility of the implemented system.

Main Results:

• Initial surveys (2015) indicated reluctance due to lack of devices and data entry labor intensity.
• Post-implementation surveys (2018, 2020) showed significant improvement, with 78% of nurses reporting enhanced neurologic exam efficiency.
• During the COVID-19 pandemic, 58% of nurses found pupillometry reassuring for limiting infectious exposure during neurologic assessments.

Conclusions:

• This study represents the first report of a direct interface for downloading pupillometer data to an EMR.
• The implemented system positively impacts nursing workflow and the documentation of pupillary findings.
• Automated pupillometry integration enhances efficiency and provides reassurance, particularly in infectious disease contexts.