A computer implemented system for monitoring and governing prescription events to detect and intervene in potential prescription-related misuse, non-compliance, or regulatory non-conformance, comprising:
- (a) one or more artificial-intelligence (AI) agents configured to:
- (i) receive structured, real-time prescription and dispensing data from at least one healthcare information system, the data comprising structured prescription and dispensing attributes associated with a patient and a prescribed medication;
- (ii) access patient health or medication history information and provider behavioral patterns;
- (iii) apply machine-learning or statistical models to compute a risk score for each prescription event based on patient-specific attributes;
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- (iv) compare the computed risk score with a configurable threshold defined by institutional policy to identify candidate alerts for potential non-compliance;
- (b) a secure communication interface configured for transmitting candidate alerts or intervention requests to authorized clinicians or regulatory recipients and for receiving override justifications or intervention feedback;
- (c) a policy-governance module configured to apply a stored policy grammar that encodes dosage caps, dispensing intervals, or cumulative sourcing limits to determine whether issuance of a candidate alert is permitted under applicable jurisdictional or institutional constraints, and, when a violation is detected, to temporarily suspend prescription dispensing or alert issuance pending manual review or override by an authorized clinician or regulator;
- (d) a medical-data governance and audit module configured for cryptographically recording, in a chained tamper-evident log, each prescription event, computed risk score, applied policy grammar, and reviewer action for regulatory compliance and retrospective analysis;
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- (e) a configurable logic module configured to automatically update detection criteria, policy thresholds, and machine-learning parameters of the AI agents based on reviewer feedback and override outcomes, thereby providing a feedback-driven adaptation of the system, wherein the feedback updates are cryptographically linked in the audit log,
- (f) an omission-event detection engine configured to evaluate, for each prescription event, whether one or more policy-defined required system actions—including required acknowledgments, required reviews, required confirmations, or required override entries specified by the institutional policy grammar—are missing or not completed within a policy-defined time window, thereby generating a policy-defined omission event;
- (g) an authorization-state controller configured to enforce, via a machine-executable state-transition model, a prescription-authorization state for each prescription event, wherein execution of a prescription event or a corresponding dispensing action is automatically prevented or suspended until the cryptographically chained audit log contains a policy-verified entry indicating completion of all required system actions or an authorized override of the omission event;
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- (h) a cryptographic validation engine configured to verify, prior to enabling any state transition that clears an alert, resolves an omission event, or authorizes dispensing, that a corresponding audit-log entry includes:
- (i) a hash referencing at least one prior entry in the log, (ii) a digital signature associated with the authenticated role of the reviewer, and (iii) a timestamp satisfying the institutional policy grammar;
- Wherein the system controls authorization states for prescription events such that execution of a prescription or dispensing action is contingent upon cryptographically verifiable satisfaction of policy-defined system requirements, thereby providing a technical improvement in secure healthcare data processing through traceable, policy-constrained, and tamper-resistant operation of an AI-driven prescription-monitoring system.