Computer Science > Artificial Intelligence
arXiv:2603.09052 (cs)
[Submitted on 10 Mar 2026]
Title:From Days to Minutes: An Autonomous AI Agent Achieves Reliable Clinical Triage in Remote Patient Monitoring
Authors:Seunghwan Kim (1), Tiffany H. Kung (1 and 2), Heena Verma (1), Dilan Edirisinghe (1), Kaveh Sedehi (1), Johanna Alvarez (1), Diane Shilling (1), Audra Lisa Doyle (1), Ajit Chary (1), William Borden (1 and 3), Ming Jack Po (1) ((1) AnsibleHealth Inc., San Francisco, USA (2) Stanford School of Medicine, Stanford, USA (3) George Washington University, Washington, D.C., USA)
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Abstract:Background: Remote patient monitoring (RPM) generates vast data, yet landmark trials (Tele-HF, BEAT-HF) failed because data volume overwhelmed clinical staff. While TIM-HF2 showed 24/7 physician-led monitoring reduces mortality by 30%, this model remains prohibitively expensive and unscalable.
Methods: We developed Sentinel, an autonomous AI agent using Model Context Protocol (MCP) for contextual triage of RPM vitals via 21 clinical tools and multi-step reasoning. Evaluation included: (1) self-consistency (100 readings x 5 runs); (2) comparison against rule-based thresholds; and (3) validation against 6 clinicians (3 physicians, 3 NPs) using a connected matrix design. A leave-one-out (LOO) analysis compared the agent against individual clinicians; severe overtriage cases underwent independent physician adjudication.
Results: Against a human majority-vote standard (N=467), the agent achieved 95.8% emergency sensitivity and 88.5% sensitivity for all actionable alerts (85.7% specificity). Four-level exact accuracy was 69.4% (quadratic-weighted kappa=0.778); 95.9% of classifications were within one severity level. In LOO analysis, the agent outperformed every clinician in emergency sensitivity (97.5% vs. 60.0% aggregate) and actionable sensitivity (90.9% vs. 69.5%). While disagreements skewed toward overtriage (22.5%), independent adjudication of severe gaps (>=2 levels) validated agent escalation in 88-94% of cases; consensus resolution validated 100%. The agent showed near-perfect self-consistency (kappa=0.850). Median cost was $0.34/triage.
Conclusions: Sentinel triages RPM vitals with sensitivity exceeding individual clinicians. By automating systematic context synthesis, Sentinel addresses the core limitation of prior RPM trials, offering a scalable path toward the intensive monitoring shown to reduce mortality while maintaining a clinically defensible overtriage profile.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| ACM classes: | I.2.0; J.3 |
| Cite as: | arXiv:2603.09052 [cs.AI] |
| (or arXiv:2603.09052v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09052
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