Trust-constrained Intelligence for Realtime Clinical Reasoning in Centralized Healthcare Information Systems: A Pilot Study

We advance a trust-governed clinical decision support architecture designed for centralized healthcare information environments. Existing centralized systems such as Clinical Patient Information Systems (CPIS) offer rich data integration but often lack safeguards that ensure automated intelligence is deployed responsibly. To address this gap, the study proposes a layered framework that treats inference as a supervised institutional function rather than a detached algorithmic computation. The model combines tightly regulated clinical data ingestion, parallel ensemble-based predictive reasoning, latency-aware aggregation, and a trust enforcement layer that verifies user legitimacy, clinical context, and institutional policy alignment prior to alert delivery. Unlike conventional CDSS implementations that emphasize accuracy metrics in isolation, the presented approach frames decision support as a time-bound and policy-constrained workflow. The reasoning engine employs tree-based and probabilistic models to capture heterogeneous patterns in structured patient data, while the trust layer controls intelligence emission through real-time validation and audit metadata generation. Empirical motivation and literature support illustrate that such integration enhances interpretability, strengthens clinician confidence, and mitigates misuse or automation bias. The architecture aligns computational deliverables with governance requirements, demonstrating that safety and reliability emerge from system design choices rather than post-hoc oversight alone. By embedding measurement instrumentation for latency and access provenance, the system enables transparent evaluation and future extensibility. Overall, the study contributes a replicable path toward accountable, auditable, and operationally viable CDSS deployment within centralized institutional infrastructures. Keywords: Clinical Decision Support Systems, Trust Governance, Electronic Health Record, Safety Latency-Constrained, Ensemble Inference, Clinical Data Security CISDI Journal Reference Format Ejeh, P.O., Onyekweli, O.V. & Isizoh, A. (2026): Trust-constrained Intelligence for Realtime Clinical Reasoning in Centralized Healthcare Information Systems: A Pilot Study. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 17 No 1, Pp 1-24. Available online at www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V17N1P1