Emergency Medicine Data, Machine-Scored for AI Training
Scored against a 10-rule, physician-designed clinical checklist. Designed for clinical decision models and health-tech engineering pipelines.
The Problem
"AI is only as good as its training data. But PubMed has 30 million articles."
AI healthcare startups face a massive problem: drowning in irrelevant, low-quality medical literature while missing the high-impact studies that matter.
Engineers waste weeks filtering noise. Models hallucinate on edge cases. Clinical decision support systems fail because they're trained on broad, unspecific data.
The Solution
"Emergency Medicine Records, 10 Quality Scores, Machine-Scored"
- Records across 6 emergency specialties, including oncology ER complications: febrile neutropenia, spinal cord compression, cancer thrombosis, hypercalcemia, and cancer pain
- 10 quality scores applied to every single record for custom subsets
- Each record annotated with physician_notes, generated from physician-designed scoring criteria and, for records held for review, human reviewer notes
- Formatted in ready-to-ingest JSON and CSV formats for AI training, academic research, and regulatory submissions
How It Works
"Machine-Scored Against a Physician-Designed Checklist"
1. Multi-source scrape
Raw records are collected from ClinicalTrials.gov, PubMed, and OpenFDA across 6 emergency specialties.
2. Automated scoring
Every record passes 10 hardcoded logic rules assessing study type, data completeness, evidence levels, and ER relevance.
3. Held for review
Records that fail the automated scorecard are held for human review; the rest are released on the strength of the scorecard alone.
The 10 Quality Scores
"10 Scoring Rules That Filter the Noise"
| Rule | What It Measures |
|---|---|
| ER Applicability | Relevance to emergency medicine (0-10 score) |
| Guideline Alignment | Matches current ACLS/ATLS clinical guidelines |
| Statistical Integrity | Verifies adequate cohort size (n ≥ 30) and significance (p < 0.05) |
| Outcome Relevance | Measures patient-centered metrics vs clinical surrogate outcomes |
| Bias Detection | Flags conflicts of interest, industry sponsorship, and single-center limits |
| Clinical Plausibility | Cross-references outliers against established emergency literature |
| Actionability | Categorizes care priorities as STAT, Routine, or N/A |
| Evidence Grade | Assigns grades A-F based on study design (A=Meta-analysis, F=Expert opinion) |
| Population Fit | Validates age and acuity matches typical ER demographics |
| Recency Weight | Gives higher weights to modern publications (< 5 years old) |
Data Specifications & Pricing Tiers
"What You Get"
| Tier | Records | Price | Includes |
|---|---|---|---|
| Mini | 50 | Free | Physician notes, 10 quality scorecards, requires manual approval |
| Starter | 250 | $2,000 | Full dataset with annotations, dynamic filters |
| Growth | 500 | $3,500 | Extended dataset + CSV/JSON format downloads |
| Enterprise | 1,000+ | $10,000+ | Full API access, custom filtering, quarterly updates |
Frequently Asked Questions
Q: Who reviews the data?
A: Every record is scored against a 10-rule checklist designed by a physician. Records that fail the checklist are held for human review before release; passing records are machine-scored and released without further manual review.
Q: What sources are used?
A: ClinicalTrials.gov, PubMed, and OpenFDA. We're actively expanding to include global registries (EU Clinical Trials Register, WHO ICTRP) for complete global coverage.
Q: Can I filter by disease/condition area or evidence grade?
A: Yes, use our A La Carte filter tool during checkout to select exactly what you need.
Q: Is this data suitable for AI training?
A: Yes. We hear the disclaimer "AI makes mistakes" everywhere. Our datasets are designed to reduce that – by providing high-quality training data, machine-scored against physician-designed criteria, that filters out noise and low-evidence studies.
Q: What format is the data in?
A: CSV and JSON, ready for any AI pipeline. We also offer UDS (Universal Document) format for customers requiring cryptographic verification.
Q: How many records are currently available?
A: Records across 6 emergency specialties, growing as the scraper runs. See the live counter on the homepage for the current total.
Q: Do you provide updates?
A: Enterprise customers (defined as any dataset purchase of $3,500+ or custom volume) receive quarterly updates and priority support.
Q: How do I know the data is accurate?
A: Every record carries its full 10-rule scorecard so you can see exactly how it was scored. Records that fail the scorecard are held for human review before release. We also cryptographically seal each dataset so you can verify its integrity in transit.
Q: Can I see a sample before buying?
A: Yes. Request a free 50-record Mini dataset. We'll send it within 24 hours.
Q: Do I need a license agreement?
A: Yes – standard research and AI training license. No commercial resale.
Q: How does this compare to raw PubMed data?
A: Raw PubMed is uncurated noise. We score, grade, and annotate every record against a 10-rule, physician-designed checklist, and hold anything that fails it for human review. You get quality, not volume.
Ready to Train Your AI on Machine-Scored ER Data?
Deploy models that reflect true emergency department clinical decision-making.