At Source
Documents Arrive in Every Format
A registry receives phone photographs, fax images, multi-page discharge summaries and handwritten scripts. Template-based OCR was built for clean printed forms and breaks on all of them.
AI-Powered Clinical Document Capture
Clinical Document OCR for RWE-Ready Data.
Clinera Prescribe turns handwritten prescriptions, scanned records and physician notes into structured research data. It extracts clinical entities, maps them to RxNorm and ICD-10, redacts identifiers and keeps every field ready for review.
- 99.4% Extraction Accuracy
- Automatic PII Redaction
- RxNorm & ICD-10 Coding
The Problem
Where Real-World Data Collection Stalls.
Observational studies, RWE registries and post-market surveillance programmes all depend on records that were never written for research: paper charts, illegible scripts and scanned PDFs that arrive by fax. The data exists. Getting it into a dataset is the bottleneck.
In Data Management
Coding Is Slow and Expensive
Coders retype drug names, strengths and diagnoses, then map each one to RxNorm and ICD-10 by hand. Cost scales linearly with volume, and transcription errors enter the dataset silently.
Before Storage
Identifiers Travel With the Image
A scanned chart carries the patient name, date of birth and address in the image itself. Storing it in a research repository without redaction creates exposure that is hard to unwind later.
The Solution
One Pipeline From Scan to Coded Record.
Vision, entity extraction and medical coding run in sequence, so a raw scan becomes a standardised, 21 CFR Part 11 compliant record in seconds. Every field carries a confidence score and sits beside the original image for a coder to confirm or correct.
01
Reads Handwriting
Multimodal vision handles handwritten scripts, dosage instructions and signatures, not just clean printed text.
02
Codes as It Extracts
Drug names map to RxNorm and ATC, diagnoses to ICD-10 and ICD-11, at the point of extraction.
03
Redacts Before Storage
Patient identifiers are located by pixel coordinates and masked on the image before the record reaches the repository.
04
Flags What It Is Unsure Of
Per-field confidence scores route uncertain extractions to human review instead of pushing them downstream unchecked.
How It Works
From Upload to Export in Four Stages.
The pipeline is the same whether a physician photographs one script on a phone or a data manager uploads a batch of 500 scanned charts.
Capture
Upload a photo, scan, fax image or multi-page PDF, or dictate a clinical summary by voice for automatic transcription.
Parse
Multimodal vision OCR reads the page and clinical entity extraction pulls out medication, prescriber, dosing and vital sign fields.
Code and Redact
Drugs map to RxNorm and ATC, diagnoses to ICD-10 and ICD-11, and identifier regions are detected and masked on the source image.
Verify and Export
A coder reviews flagged fields against the original image, then the record exports as JSON, CSV or CDISC ODM, or writes straight into the EDC.
Capabilities
Core Features and Capabilities.
Four feature blocks cover the reading engine, the extraction and coding layer, the privacy controls that run before storage, and the workspace where physicians and coders verify what the model produced.
Feature 01
Gemini Multimodal Vision OCR and Dictation Engine
Parse clinical documents regardless of format, layout or handwriting style.
- Handwritten Prescription Parsing - High-precision recognition of handwritten scripts, dosage instructions and prescriber signatures.
- Multi-Page Document Scans - Bulk PDF processing for discharge summaries, lab reports and physician progress notes.
- Speech-to-Text Dictation - Prescribers record a verbal clinical summary that is transcribed and parsed into the same structured fields.
Capture Queue
Mixed Clinical Inputs
Phone Photo
ParsedHandwritten Script
Multi-Page PDF
ProcessingDischarge Summary
Dictation
QueuedPhysician Note
Fax Image
ParsedMedication List
Feature 02
Structured Entity Extraction and Medical Coding
Pull the clinical data elements a registry needs, already standardised.
- Prescription Entities - Doctor name, NPI, specialty, date, patient header, medication name, strength, form, route, frequency, duration and special instructions.
- Standardised Coding - Extracted drug names map to RxNorm and ATC categories, parsed diagnoses to ICD-10 and ICD-11 codes.
- Vital Signs Parsing - Blood pressure, heart rate, SpO2, temperature and weight are identified wherever they appear in the document.
Extraction Layer
Mapped Clinical Concepts
EntityCodeScore
Metformin 500 mgRxNorm 86097599%
Type 2 DiabetesICD-10 E11.997%
AmlodipineATC C08CA0194%
Dose TimingUnresolved78%
Feature 03
Automatic PII Bounding Box Redaction
Remove identifiers before anything reaches the research repository.
- Spatial Detection - The model returns pixel coordinates for identifier regions including name, national ID, date of birth and address.
- Automated Masking - Those regions are redacted or blurred on the stored image, so the research copy never carries the identifiers the original did.
Detected Regions
Name, DOB, Address
Research Copy
Masked Before Storage
Feature 04
Prescriber Onboarding and Verification Workspace
Give physicians and coders a review environment that takes seconds, not minutes.
- Prescriber Onboarding - Tokenised registration links invite participating physicians, with profile and licensing certificate management built in.
- Side-by-Side Verification - The original scan sits next to the parsed fields, so a reviewer checks the value against the source without switching screens.
- Confidence Scoring and Warnings - Per-field accuracy scores highlight low-confidence extractions, which keeps human attention on the fields that need it.
Review Queue
Flagged Fields Only
Dr. MehtaLicense VerifiedActive
RX-1842Frequency Needs Review82%
RX-1848Approved100%
Batch 0742 of 50 Complete84%
Use Cases
Where Teams Put Clinera Prescribe to Work.
Four recurring jobs across observational research and post-market programmes.
RWE Registry Build-Out
Convert historical paper charts and prescription records into a coded dataset at the volume a registry needs, without hiring a coding team to match.
Faster Registry Population
Post-Market Surveillance
Ingest prescribing records and physician notes from participating sites to track real-world usage patterns and safety signals after launch.
Compliant PMS Evidence
Concomitant Medication Capture
Digitise the medication lists participants bring to site visits, coded to RxNorm, so con-med reconciliation stops being a manual transcription task.
Cleaner Con-Med Data
Prescribing Trend Analysis
Aggregate coded prescribing data across a prescriber network to analyse patterns by molecule, indication, specialty or region.
Analysable Real-World Data
Workflow
Role-Based Workflow and Benefits.
The same extraction serves three different jobs: the physician wants it over quickly, the coder wants it verifiable, the sponsor wants it standardised.
RoleKey Capabilities in Clinera PrescribeKey Benefit
Prescribing Physician
Upload prescriptions and notes by mobile scan or voice dictation, then verify the parsed output in seconds.
Minimal administrative burden with instant digital archival.
RWE Data Coder
Review batch uploads, verify RxNorm and ICD-10 mappings, export clean RWE datasets.
80% reduction in manual data entry time for observational registries.
Study Sponsor
Ingest large-scale real-world prescription data, track prescriber enrolment, analyse prescribing trends.
Fast, compliant aggregation of high-volume real-world data.
Why Clinera Prescribe
What Makes Clinera Prescribe Different.
01
Coded Output, Not Just Text
Generic OCR returns characters and leaves the mapping work to a coder. Prescribe returns a medication already resolved to an RxNorm concept and a diagnosis already carrying its ICD-10 code.
02
Privacy Applied to the Image
Most tools redact text fields and store the original scan intact. Detecting identifier regions by pixel coordinate means the stored image is masked too, which is what a research repository actually needs.
03
Review Built Into the Pipeline
Confidence scoring and a side-by-side workspace are part of the product, not a spreadsheet bolted on afterwards. Reviewers spend their time on the fields the model was unsure about.
Specifications
Technical Specifications.
Clinera Prescribe is deployed as part of the Clinera platform and inherits its security, audit and access-control model.
- AI Engine
- Gemini Multimodal Vision API with a custom medical entity extraction pipeline.
- Supported File Formats
- PNG, JPEG, PDF, TIFF and WebP, single page or multi-page.
- Throughput
- A standard clinical prescription or note is processed in under 3.5 seconds.
- Coding Taxonomies
- RxNorm and ATC for medications, ICD-10 and ICD-11 for diagnoses.
- Export Formats
- Structured JSON, CSV, CDISC ODM, and direct integration with the Clinera EDC database.
- Privacy Controls
- Spatial bounding box detection of patient identifiers with automated masking applied before data is committed to research storage.
FAQ
Frequently Asked Questions.
Can AI-Extracted Data Be Used in a Regulated Observational Study, or Does It Need Re-Entry?+
The extracted record is designed to be traceable rather than standalone. Each field keeps a link to the source document and the reviewer action that confirmed it, and the whole chain is captured under 21 CFR Part 11 logging, so a monitor can trace any value in the dataset back to the page it came from. Where a study protocol requires verification of specific critical fields, those fields can be routed to mandatory review rather than auto-accepted.
How Are Low-Confidence Extractions Handled, and Who Decides the Threshold?+
Every field carries a confidence score. Fields above the auto-accept threshold pass through, and anything below it is flagged in the verification workspace with the original image alongside for comparison. The threshold is set per study and per field type, because the cost of an error on medication strength is not the cost of an error on a prescriber's specialty.
What Happens With Brand Names, Local Drug Names or Combination Products That Do Not Map Cleanly to RxNorm?+
Unmapped and ambiguous terms are not silently dropped or forced to the nearest match. They surface as an unresolved mapping with the candidate concepts and their scores, and the coder makes the call. Study-specific alias lists can be loaded so recurring local brand names resolve automatically on subsequent documents.
What Scan Quality Does It Need, and What Breaks It?+
Phone photographs, flatbed scans, fax images and multi-page PDFs are all handled, including moderate skew and mixed print and handwriting on the same page. Accuracy drops with the same inputs that defeat a human reader: heavy shadow, cropped edges that cut off a field, very low resolution, and ink that has bled through from the reverse side. Documents that fall below usable quality are flagged at upload rather than parsed into unreliable values.
Where Is Document Data Processed, and Is It Used to Train Models?+
Parsing runs through the Gemini Multimodal Vision API, and images and extracted fields are stored in your Clinera study repository under the same access controls as the rest of the study record. Redaction runs before research storage, so identifiers are removed from the retained copy. Data residency, retention periods and the processing agreement are confirmed per deployment, and we will walk through the specifics with your quality and IT teams before any study data is loaded.
Get Started
Schedule a Clinera Prescribe Demo.
Bring five of your least legible documents. We will run them through the pipeline on the call and show you the extraction, the coding and the confidence scores on your own paperwork.