System for governance of medical data with secure capture, validation, normalization, export, and compliance auditing including ai integration across medical systems
A system for medical data governance that unifies secure capture, validation, arbitration, and export of multimodal patient data in regulated environments. The system comprises secure capture modules configured to ingest high-frequency clinical inputs, validation subsystems that apply patient identification and schema enforcement, and a central repository that normalizes governed datasets. Data are bound to compliance tokens carrying provenance, jurisdictional metadata, and cryptographic signatures. Outputs are processed by arbitration engines that resolve conflicting AI analyses under encoded policy thresholds. Glyph rendering subsystems generate visual, auditory, haptic, or machine-readable indicators of consent, compliance, or override events, each tied to immutable audit records. An append-only ledger records token issuance, arbitration decisions, and export transactions, including ANSI X12 billing flows, with cascading revocation across federated validators. The disclosed architecture ensures that governed inputs and outputs remain verifiable, auditable, and enforceable, enabling trustworthy AI deployment in clinical and regulatory settings.
1 . A system for collecting, validating, storing, and providing patient data, the system comprising:
a) one or more secure capture modules, each secure capture module comprising at least one of a digital black box, a device interface, or a manual approval interface, and each secure capture module being configured to record patient data;
b) a patient identification and data validation subsystem configured to associate the recorded patient data with an individual patient and to validate the recorded patient data to generate identified and validated data sets;
c) a central repository configured to store the identified and validated data sets;
d) a data analysis engine configured to remove personal health information from the identified and validated data sets and to execute machine-based data-affinity analysis operations on the data to detect association errors or other data-integrity issues; and
e) an export driver subsystem comprising a plurality of export drivers, each export driver being configured to execute machine-formatting, normalization, filtering, and consistency-checking operations on the identified and validated data sets processed by the data analysis engine and to format the processed data for receipt by a corresponding receiving subsystem, wherein at least two of the receiving subsystems are configured to receive data in different data formats.
2 . The system of claim 1 , wherein the patient data recorded by the secure capture modules includes high-frequency data.
3 . The system of claim 1 , wherein at least one receiving subsystem comprises an artificial intelligence subsystem configured to execute machine-learning processes.
4 . The system of claim 2 , wherein the system is further configured to automatically route the high-frequency data to an artificial intelligence subsystem configured to execute machine-learning processes.
5 . The system of claim 1 , wherein the secure capture modules comprise a medical device data system configured to record patient data from medical devices.
6 . The system of claim 5 , wherein the medical device data system is configured to provide the recorded patient data to the patient identification and data validation subsystem.
7 . The system of claim 6 , wherein the recorded patient data includes environmental condition data machine-associated with the time the patient data was recorded.
8 . The system of claim 1 , wherein the recorded patient data includes a timestamp applied at the time of recording.
9 . The system of claim 1 , wherein the recorded patient data includes location information indicating where the data was recorded.
10 . The system of claim 1 , wherein each identified and validated data set includes patient identification data, a patient identification code, time-segment information, and patient data associated with respective time segments, and wherein the patient identification and data validation subsystem includes a human-input interface configured to receive human confirmation input in a machine-interpretable form for each identified and validated data set.
11 . The system of claim 1 , wherein the data analysis engine is configured to detect association errors or time-segment errors and to output machine-generated review data identified for presentation through the human-input interface.
12 . The system of claim 1 , wherein the system is configured to store a corrected data set after a correction is confirmed via the human-input interface and to apply a machine-executed cryptographic signature operation to the corrected data set before providing the corrected data set to a receiving subsystem.