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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.17234 |
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| _version_ | 1866912972315557888 |
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| author | Wang, Jane Keyes, Timothy Liang, April S Ma, Stephen P Shen, Jason Liu, Jerry Ambers, Nerissa Pandya, Abby Pandya, Rita Hom, Jason Steele, Natasha Chen, Jonathan H Schulman, Kevin |
| author_facet | Wang, Jane Keyes, Timothy Liang, April S Ma, Stephen P Shen, Jason Liu, Jerry Ambers, Nerissa Pandya, Abby Pandya, Rita Hom, Jason Steele, Natasha Chen, Jonathan H Schulman, Kevin |
| contents | Surgical co-management (SCM) is an evidence-based model in which hospitalists jointly manage medically complex perioperative patients alongside surgical teams. Despite its clinical and financial value, SCM is limited by the need to manually identify eligible patients. To determine whether SCM triage can be automated, we conducted a prospective, unblinded study at Stanford Health Care in which an LLM-based, electronic health record (EHR)-integrated triage tool (SCM Navigator) provided SCM recommendations followed by physician review.
Using pre-operative documentation, structured data, and clinical criteria for perioperative morbidity, SCM Navigator categorized patients as appropriate, not appropriate, or possibly appropriate for SCM. Faculty indicated their clinical judgment and provided free-text feedback when they disagreed. Sensitivity, specificity, positive predictive value, and negative predictive value were measured using physician determinations as a reference. Free-text reasons were thematically categorized, and manual chart review was conducted on all false-negative cases and 30 randomly selected cases from the largest false-positive category. Since deployment, 6,193 cases have been triaged, of which 1,582 (23%) were recommended for hospitalist consultation. SCM Navigator displayed high sensitivity (0.94, 95% CI 0.91-0.96) and moderate specificity (0.74, 95% CI 0.71-0.77). Post-hoc chart review suggested most discrepancies reflect modifiable gaps in clinical criteria, institutional workflow, or physician practice variability rather than LLM misclassification, which accounted for 2 of 19 (11%) false-negative cases. These findings demonstrate that an LLM-powered, EHR-integrated, human-in-the-loop AI system can accurately and safely triage surgical patients for SCM, and that AI-enabled screening tools can augment and potentially automate time-intensive clinical workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17234 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients Wang, Jane Keyes, Timothy Liang, April S Ma, Stephen P Shen, Jason Liu, Jerry Ambers, Nerissa Pandya, Abby Pandya, Rita Hom, Jason Steele, Natasha Chen, Jonathan H Schulman, Kevin Computers and Society Artificial Intelligence Surgical co-management (SCM) is an evidence-based model in which hospitalists jointly manage medically complex perioperative patients alongside surgical teams. Despite its clinical and financial value, SCM is limited by the need to manually identify eligible patients. To determine whether SCM triage can be automated, we conducted a prospective, unblinded study at Stanford Health Care in which an LLM-based, electronic health record (EHR)-integrated triage tool (SCM Navigator) provided SCM recommendations followed by physician review. Using pre-operative documentation, structured data, and clinical criteria for perioperative morbidity, SCM Navigator categorized patients as appropriate, not appropriate, or possibly appropriate for SCM. Faculty indicated their clinical judgment and provided free-text feedback when they disagreed. Sensitivity, specificity, positive predictive value, and negative predictive value were measured using physician determinations as a reference. Free-text reasons were thematically categorized, and manual chart review was conducted on all false-negative cases and 30 randomly selected cases from the largest false-positive category. Since deployment, 6,193 cases have been triaged, of which 1,582 (23%) were recommended for hospitalist consultation. SCM Navigator displayed high sensitivity (0.94, 95% CI 0.91-0.96) and moderate specificity (0.74, 95% CI 0.71-0.77). Post-hoc chart review suggested most discrepancies reflect modifiable gaps in clinical criteria, institutional workflow, or physician practice variability rather than LLM misclassification, which accounted for 2 of 19 (11%) false-negative cases. These findings demonstrate that an LLM-powered, EHR-integrated, human-in-the-loop AI system can accurately and safely triage surgical patients for SCM, and that AI-enabled screening tools can augment and potentially automate time-intensive clinical workflows. |
| title | Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2603.17234 |