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PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows
LTM Limited, Texas, USA
*whom correspondence should be addressed. E-mail: muthuchand2013@gmail.com
Received: 3 September 2026, Revised: 17 October 2026, Accepted: 19 October 2026, Published Online: 31 October 2026
(This article belongs to the Special Issue on Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26) and the Section Artificial Intelligence – Computer Science (AIC))
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Chandrasekaran, M. (2026). PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows. Journal of Engineering Research and Sciences, 5(10), 1–10. https://doi.org/10.55708/js0510001
Chandrasekaran, M. (2026). PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows. Journal of Engineering Research and Sciences, 5(10), 1–10. https://doi.org/10.55708/js0510001
Chicago/Turabian Style
Muthukumarapandian Chandrasekaran. "PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows." Journal of Engineering Research and Sciences 5, no. 10 (October 2026): 1–10. https://doi.org/10.55708/js0510001
Muthukumarapandian Chandrasekaran. "PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows." Journal of Engineering Research and Sciences 5, no. 10 (October 2026): 1–10. https://doi.org/10.55708/js0510001
IEEE Style
M. Chandrasekaran, "PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows," Journal of Engineering Research and Sciences, vol. 5, no. 10, pp. 1–10, Oct. 2026, doi: 10.55708/js0510001.
M. Chandrasekaran, "PRISM: Probabilistic Reasoning through Intelligent Situational Modeling - A Dynamic Context Engineering Framework for Uncertainty-Aware Agentic AI in Healthcare Administrative Workflows," Journal of Engineering Research and Sciences, vol. 5, no. 10, pp. 1–10, Oct. 2026, doi: 10.55708/js0510001.
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Abstract
Large language models (LLMs) used in healthcare administrative workflows often fail because the context they receive is incomplete, not because the model lacks capability. In prior authorization, a retrieved document set can be topically relevant yet still miss the evidence that a payer policy requires. Physicians report completing about 45 prior authorizations per week and 94% report associated care delays, so reliable automation matters. This paper presents PRISM (Probabilistic Reasoning through Intelligent Situational Modeling), a Dynamic Context Engineering (DCE) framework that runs as pre-inference middleware between healthcare data systems and an LLM. PRISM composes six mechanisms: Context Evidence Sufficiency scoring (CES), a Clinical Criteria Chain (CCC), Criteria-Seeded Gap retrieval (CSG), Context Mutation Tracking (CMT), Adversarial Context Hardening (ACH), and Uncertainty-Governed Escalation (UGE). Together they check a context bundle against policy requirements, retrieve missing evidence, record every transformation, and escalate when context quality is insufficient. On 1,200 synthetic prior authorization cases authored against InterQual 2024 criteria, PRISM reaches a determination F1 of 0.912 against 0.769 for dense retrieval with chain-of-thought prompting (18.6% relative improvement) and 0.710 for BM25 retrieval (28.5%), and lowers context brittleness from 0.158 to 0.049 (69.0%). Because the cases and the PRISM policy templates share one source, these results are an upper-bound estimate that requires confirmation on independently annotated and real-world data.
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