AI-Assisted View Alert Management
Description
By analyzing and prioritizing alerts, this tool will reduce unnecessary notifications and help clinicians focus on what matters most, enhancing care for Veterans.
Detailed example
Switchboard will build new AI models trained on annotated message data. Message annotation will be conducted in a structured process using a dataset of View Alerts (subject to design). The end-state AI models will apply the following label types to messages: - Label assignment of 5 core labels: Clinically Urgent, Clinician, Scheduling, Form, and Refill - Additional label assignment of VA-specific labels based on review of the dataset where patterns are identified. These will include: a) No potential provider action b) Medication nearing expiration c) Non-critical alerts for inactive providers Or additional view alert labels that are relevant to the VA workflow: - Flagging of clinically irrelevant View Alerts - Flagging of duplicative View Alerts
AI / analytics pattern
Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
- Reduce alert fatigue and clinician burnout by minimizing unnecessary distractions. - Improve clinical efficiency and workflows through targeted triage and routing of critical alerts. - Enhance patient safety and resource allocation by prioritizing meaningful and actionable view alerts. - Increase return on existing technology investments by optimizing the performance of the current alerting functionality.
Controls / human review
ATO: Not reported; PIA: Not published