Real-time algorithmic exchange and processing of pharmaceutical quality data and information
Bibliographic record
Abstract
Herein, a modern method is proposed for exchanging and processing real-time medicinal product information using Health Level 7 International's (HL7) Fast Healthcare Interoperability Resources (FHIR®) standard, Application Programming Interfaces (API), digitization and artificial intelligence. FHIR is presently in use largely to facilitate interactions between patient-facing healthcare institutions, such as hospitals, doctor's offices, and laboratories, for electronic health record management and exchange. There are several ongoing efforts to adapt the FHIR standard for regulatory use cases to support the needs of the global biopharmaceutical industry, including the exchange of Electronic Product Information (ePI); chemistry, manufacturing, and controls (CMC) data; and adverse event reporting. Once in place, this new method of data exchange is expected to (1) improve efficiency by reducing the time and effort needed to manage regulatory information; (2) accelerate decision making; (3) encourage innovation in pharmaceutical manufacturing; (4) improve the ability and agility of information exchange. Currently, the end-to-end timescale for the pharmaceutical regulatory workflow is measured in months and years. This new paradigm will use FHIR APIs and other supporting technologies to reduce the potential time for data exchange from months to days, hours, minutes, and eventually sub-seconds. With such drastic improvements in speed provided by digitization, automation, and interoperability, the biopharmaceutical industry can reach more patients, and more quickly than at any time in the industry's 100+ year history. The present work will focus on examining specific real-world implementation examples for using FHIR to support exchange of CMC information within and across the biopharmaceutical industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".