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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...

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Related Experiment Video

Updated: Jan 18, 2026

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

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Published on: February 20, 2014

Identifying Electronic Health Record Tasks and Activity Using Computer Vision.

Liem Manh Nguyen¹, Amrita Sinha², Adam Dziorny³

¹Department of Pediatric Critical Care, Stanford University School of Medicine, Stanford, California, United States.

Applied Clinical Informatics

|September 10, 2025

Summary

This summary is machine-generated.

A new computer vision model accurately classifies electronic health record (EHR) tasks and quantifies active use time from screen recordings. This approach improves EHR analytics by distinguishing between active work and inactivity, offering a more precise measure of clinical activity.

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Area of Science:

Health Informatics
Computer Vision Applications
Clinical Workflow Analysis

Background:

Electronic health record (EHR) use metrics are crucial for assessing clinical activity but vendor-derived data may not reflect actual user experience.
Quantifying precise active EHR time is challenging due to difficulties in translating discrete audit log timestamps and measuring inactivity.
Existing methods lack sufficient data to accurately measure time spent actively engaged with EHR systems.

Purpose of the Study:

To develop and validate a computer vision-based model for classifying EHR tasks and detecting task changes.
To quantify active EHR use time from clinician session screen recordings.
To enable customized EHR use metrics using raw audit log data.

Main Methods:

Utilized YOLOv8, Tesseract OCR, and a predefined dictionary for task classification and change detection on simulated and real-world EHR screen recordings.
Developed a frame comparison algorithm to differentiate between active use and inactivity, enabling active time quantification.
Validated model performance against clinician annotations for task classification, task change identification, and active time measurement.

Main Results:

The model achieved 94% accuracy in classifying high-level EHR tasks and 90.6% sensitivity in detecting task changes.
Active-use quantification accuracy varied by task, with lower error for tasks exhibiting clear visual changes (e.g., Results Review).
Post hoc analysis indicated improved active-use quantification with adjusted inactivity thresholds.

Conclusions:

A computer vision approach is feasible for identifying EHR tasks and measuring active time spent within the EHR system.
This method offers a potential solution for defining context-sensitive thresholds to quantify clinically relevant active EHR time.
Future research should focus on refining task-specific thresholds and validating the model across diverse clinical settings.