Medical Data Catalog for Healthcare AI
Off-the-shelf Healthcare/Medical Datasets to jumpstart your Healthcare AI project
Medical and Healthcare datasets for Machine Learning
What the Shaip Medical Data Catalog Includes?
The Shaip Medical Data Catalog is a HIPAA-ready, off-the-shelf library of de-identified healthcare training data spanning 31 medical specialties, including 257,977 hours of physician dictation audio, transcribed medical records, electronic health records, and multimodal datasets. Each dataset is licensed for commercial AI training and ships with Safe Harbor or Expert Determination de-identification.
Physician Dictation Audio Data
Our de-identified dataset for healthcare includes audio files across 31 specialties dictated by physicians describing patients’ clinical condition & plan of care based on physician-patient encounters in clinical setting.
Off-the-Shelf Physician Dictation Audio Files:
- 257,977 hours of Real-world Physician Dictation Speech Dataset from 31 specialties to train Healthcare Speech models
- Dictation audio captured from various devices like Telephone Dictation (54.3%), Digital Recorder (24.9%), Speech Mic (5.4%), Smart Phone (2.7%) and Unknown (12.7%)
- PII Redacted Audio & Transcripts adhering to Safe Harbor Guidelines in conformance with HIPAA
Transcribed Medical Records
Transcribed medical records refers to transcription of physician & patient conversation, transcription of medical reports and medical assessment. It helps in mapping medical history of the patient for future visits and also acts as a reference point for the doctors. It helps evaluate the present condition of the patient and suggest a suitable treatment.
Off-the-Shelf Transcribed Medical Records:
- Transcription of 257,977 hours of Real-world Physician Dictation from 31 specialties to train Healthcare Speech models
- Transcribed Medical Records from various work types like Operative Report, Discharge Summary, Consultation Note, Admit Note, ED Note, Clinic Note, Radiology Report, etc.
- PII Redacted Audio & Transcripts adhering to Safe Harbor Guidelines in conformance with HIPAA
Electronic Health Records (EHR)
Electronic Health Records or EHR are medical records that contains patient’s medical history, diagnoses, prescription, treatment plans, vaccination or immunization dates, allergies, radiology images (CT Scan, MRI, X-Rays), and laboratory tests & more.
Off-the-Shelf Electronic Health Records (EHR):
- 5.1M+ Records and physician audio files in 31 specialties
- Real-world gold-standard medical records to train Clinical NLP and other Document AI models
- Metadata information like MRN (Anonymized), Admission Date, Discharge Date, Length of Stay days, Gender, Patient Class, Payer, Financial Class, State, Discharge Disposition, Age, DRG, DRG Description, $ Reimbursement, AMLOS, GMLOS, Risk of mortality, Severity of illness, Grouper, Hospital Zip Code, etc.
- Medical Records from various US states and region- North East (46%), South (9%), Midwest (3%), West (28%), Others (14%)
- Medical Records belonging to all Patient Classes covered- Inpatient, Outpatient (Clinical, Rehab, Recurring, Surgical Day Care), Emergency.
- Medical Records belonging to all Patient Age Groups <10 yrs (7.9%), 11-20 yrs (5.7%), 21-30 yrs (10.9%), 31-40 yrs (11.7%), 41-50 yrs (10.4%), 51-60 yrs (13.8%), 61-70 yrs (16.1%), 71-80 yrs (13.3%), 81-90 yrs (7.8%), 90+ yrs (2.4%)
- Patient Gender ratio of 46% (Male) and 54% (Female)
- PII Redacted Documents adhering to Safe Harbor Guidelines in conformance with HIPAA
Five reasons buyers license Shaip instead of stitching it together.
The Shaip Medical Data Catalog exists because most healthcare AI teams lose nine to twelve months sourcing compliant training data before a single model is trained. Here is what changes when those months are returned.
Catalog scale most teams can’t match.
The Shaip catalog spans 257,977 hours of physician dictation, transcriptions from 31 medical specialties, and EHR records covering every patient age band — the kind of volume that lets buyers train and evaluate models without stitching together a dozen open datasets.
Compliance is a starting condition, not a feature.
Every Shaip medical dataset ships with HIPAA Safe Harbor de-identification by default, Expert Determination on request, GDPR-aligned handling, and BAA-readiness for covered entities. Buyers do not need to retrofit compliance after the fact.
Healthcare-trained specialists, not generic crowd workers.
Annotation, transcription, and QA on the Shaip medical catalog are performed by healthcare-trained specialists. The Shaip workflow includes multi-layer QA and human-in-the-loop validation calibrated to clinical accuracy standards.
Off-the-shelf today, custom on demand.
Buyers can license existing Shaip datasets immediately or commission custom collection scoped to specific demographics, geographies, languages, and modalities — without switching vendors or re-running the compliance review.
Available where data teams already work.
Shaip’s de-identified EHR and physician dictation datasets are available on the Databricks Marketplace, with delivery in formats data and ML teams already use — JSON, CSV, WAV. Sample datasets are available before any commitment.
Security & Compliance
Can’t find what you are looking for?
New off-the-shelf medical datasets are being collected across all data types
Contact us now to let go of your healthcare training data collection worries
Frequently Asked Questions (FAQ)
1. What are medical datasets?
Medical datasets are healthcare data used to train, evaluate, and improve AI/ML models. They may include physician dictation audio, transcribed medical records, electronic health records, synthetic physician-patient dialogues, and multimodal healthcare datasets that combine relevant text, speech, and structured clinical data.
2. What is included in the Shaip Medical Data Catalog?
The Shaip Medical Data Catalog includes physician dictation audio, transcribed medical records, electronic health records, synthetic physician-patient dialogues, and multimodal datasets that link text, speech, and structured clinical data at the patient or encounter level. It includes 257,977 hours of physician dictation audio across 31 medical specialties and is available for commercial AI training.
3. Are Shaip’s medical datasets HIPAA compliant?
Yes. Shaip’s medical datasets are de-identified under HIPAA Safe Harbor by default, removing the 18 categories of identifiers specified in the HIPAA Privacy Rule. Expert Determination de-identification is also available when statistical certification is required, and Shaip is BAA-ready for covered entities.
4. Can Shaip’s medical datasets support GDPR and other healthcare data requirements?
Yes. Shaip’s medical datasets can be prepared to support HIPAA, GDPR, and other applicable healthcare data requirements depending on project scope, geography, data type, and contractual requirements.
5. Can I buy healthcare datasets off the shelf, or do they need to be collected?
Both options are available. Shaip offers off-the-shelf healthcare datasets through the Shaip Medical Data Catalog for commercial AI training. If a project requires a specific language, demographic, specialty, modality, or clinical setting, Shaip can also run custom medical data collection under the same compliance standards.
6. Can Shaip medical datasets be customized?
Yes. Shaip can customize medical datasets by specialty, patient age band, gender, geography, language, modality, clinical setting, format, volume, and project requirements. Custom datasets are scoped through a statement of work and follow applicable de-identification and compliance standards.
7. Can I see a sample dataset before licensing?
Yes. Shaip provides representative sample datasets under NDA so AI teams can evaluate format, quality, demographic coverage, and model fit before licensing. Sample access is typically the first step before a Standard License or Custom Collection engagement.
8. In what formats does Shaip deliver medical datasets?
Shaip delivers medical datasets in AI-ready formats, including JSON, CSV, and FHIR for structured records; WAV files with paired transcripts for audio; and transcript files for speech and language datasets. Multimodal datasets may include manifest files that link text, audio, and structured clinical records.
9. How does Shaip ensure the quality of medical datasets?
Shaip ensures medical dataset quality through expert review, domain-specialist annotation, validation workflows, and structured QA checks. These processes help ensure accuracy, reliability, and model readiness for healthcare AI development.
10. Are Shaip’s medical datasets scalable for large AI/ML projects?
Yes. Shaip’s medical datasets are scalable for both small pilots and enterprise AI/ML projects. They can support projects requiring large volumes of medical records, structured clinical data, transcripts, or hundreds of thousands of hours of physician dictation audio.
11. Can Shaip medical datasets integrate into existing AI models and workflows?
Yes. Shaip delivers medical datasets in ready-to-use formats such as JSON, CSV, FHIR, WAV, and transcript files. These formats support integration into existing AI, ML, NLP, speech, healthcare LLM, and multimodal model development workflows.
12. How long does it take to receive a Shaip medical dataset?
Off-the-shelf medical datasets can typically be delivered within days after sample review, contract signature, and licensing finalization. Custom collection timelines depend on project scope, dataset size, modality, compliance requirements, and complexity, and are defined in the statement of work.
13. How much do medical datasets cost?
The cost of medical datasets depends on dataset type, modality, volume, customization requirements, licensing terms, delivery timeline, and compliance needs. Teams can share their requirements through the Contact Us form to receive a custom quote.
14. Why are medical datasets important for AI/ML in healthcare?
High-quality medical datasets are essential for training accurate, reliable, and clinically useful healthcare AI models. They help improve medical documentation, clinical NLP, speech recognition, summarization, decision support, automation, patient care workflows, and healthcare data intelligence.
