Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings
Journal:
arXiv
Published Date:
Dec 17, 2024
Abstract
This study introduces an AI-driven platform for continuous and passive
patient monitoring in hospital settings, developed by LookDeep Health.
Leveraging advanced computer vision, the platform provides real-time insights
into patient behavior and interactions through video analysis, securely storing
inference results in the cloud for retrospective evaluation. The dataset,
compiled in collaboration with 11 hospital partners, encompasses over 300
high-risk fall patients and over 1,000 days of inference, enabling applications
such as fall detection and safety monitoring for vulnerable patient
populations. To foster innovation and reproducibility, an anonymized subset of
this dataset is publicly available. The AI system detects key components in
hospital rooms, including individual presence and role, furniture location,
motion magnitude, and boundary crossings. Performance evaluation demonstrates
strong accuracy in object detection (macro F1-score = 0.92) and patient-role
classification (F1-score = 0.98), as well as reliable trend analysis for the
"patient alone" metric (mean logistic regression accuracy = 0.82 \pm 0.15).
These capabilities enable automated detection of patient isolation, wandering,
or unsupervised movement-key indicators for fall risk and other adverse events.
This work establishes benchmarks for validating AI-driven patient monitoring
systems, highlighting the platform's potential to enhance patient safety and
care by providing continuous, data-driven insights into patient behavior and
interactions.
