An Ontology-Guided Multi-Agent Framework for Hospital-at-Home Decision Support: Development and Formative Evaluation Study (Preprint)

BACKGROUND Clinical decision-support systems usually assume electronic health records rich in vital signs and coded diagnoses. Hospital-at-home (HaH) programmes often rely instead on a hand-maintained daily census, where evidence of deterioration, prolonged stays, and missing safety decisions is scattered across days and free text. We frame daily census review as longitudinal, capacity-constrained triage and propose an ontology-guided multi-agent framework in which four cooperating agents turn unstructured operational records into an auditable, ranked worklist. An intake agent reconstructs care episodes and maps free-text diagnoses to the Human Disease Ontology; specialist Stay, Risk, and Safety agents combine supervised learning, large-language-model (LLM) reading, and explicit clinical rules; a coordinator agent ranks patients within review capacity. We validate every architectural component through a five-part ablation study on 23 months of Johns Hopkins Aramco Healthcare data (N=,1,404 episodes, 20,290 patient-days). Removing episode-trajectory features reduces Stay-agent AUC by 0.070 and Risk-agent AUC by 0.064 (five-fold CV), confirming that longitudinal signals are necessary. Replacing the programme's static admission-day risk score with dynamic daily reassessment improves AUC from 0.514 to 0.721 (+0.207 at 1,705 decision points). The Safety Agent's rule-judge architecture achieves precision=1.00 at 0.2 alerts per day, versus precision=,0.11 and 0.6 alerts per day when an LLM judges documentation gaps directly an 89% false-positive reduction. The multi-agent coordinator recovers 78.9% of documentation-gap episodes within a daily review budget of 30, compared with 5.3% for a monolithic any-problem model a 15-fold improvement while maintaining full precision (all 30 flagged episodes carry at least one confirmed adverse event). Stay-agent held-out AUC is 0.849; Risk-agent held-out AUC is 0.632. These results demonstrate that ontology-grounded, ablation-validated multi-agent coordination can transform sparse, text-heavy HaH records into safe, auditable clinical decision support without requiring structured EHRs. The system is intended to support, rather than replace, clinical judgment. Code and results: {https://github.com/SarahayadAOU/HomeCare_JHA. OBJECTIVE This study aimed to develop an ontology-guided multi-agent framework that frames daily census review as longitudinal, capacity-constrained triage and turns unstructured operational records into an auditable, ranked worklist, and to evaluate the contribution of each architectural component. METHODS In the framework, an intake agent reconstructs care episodes and maps free-text diagnoses to the Human Disease Ontology; specialist Stay, Risk, and Safety agents combine supervised learning, large language model (LLM) reading, and explicit clinical rules; and a coordinator agent ranks patients within review capacity. We validated every architectural component through a five-part ablation study on 23 months of retrospective daily census data from the Johns Hopkins Aramco Healthcare HaH programme in Saudi Arabia (1,627 episodes, of which 1,404 had a known outcome; 20,290 patient-days). Discrimination was reported as the area under the receiver operating characteristic curve (AUC), and system-level performance as recall and precision within a fixed daily review budget. RESULTS Removing episode-trajectory features reduced Stay-agent AUC by 0.070 and Risk-agent AUC by 0.064 (five-fold cross-validation), confirming that longitudinal signals are necessary. Replacing the programme’s static admission-day risk score with dynamic daily reassessment improved AUC from 0.514 to 0.721 (+0.207 at 1,705 decision points). The Safety Agent’s rule-judge architecture achieved precision = 1.00 at 0.2 alerts per day, versus precision = 0.11 and 0.6 alerts per day for a simulated LLM-alone baseline that judges documentation gaps directly, an 89% false-positive reduction. The multi-agent coordinator recovered 78.9% of documentation-gap episodes within a daily review budget of 30, compared with 5.3% for a monolithic any-problem model, a 15-fold improvement, while maintaining full precision (all 30 flagged episodes carry at least one confirmed adverse event). Stay-agent held-out AUC was 0.849; Risk-agent held-out AUC was 0.632. CONCLUSIONS These results demonstrate that ontology-grounded, ablation-validated multi-agent coordination can transform sparse, text-heavy HaH records into safe, auditable clinical decision support without requiring structured electronic health records. The system is intended to support, rather than replace, clinical judgment.