De-Identified Electronic Health Records (EHR) Datasets for AI & ML Projects
Commercially licensed, HIPAA-compliant Electronic Health Record data — ready for clinical AI, NLP, and predictive modeling.
What Is EHR Data & Why Does It Matter for AI?
Electronic Health Records (EHRs) are longitudinal, digital patient records maintained by healthcare providers across the full care continuum — hospitals, outpatient clinics, specialist practices, and labs. Unlike Electronic Medical Records (EMRs), which are single-provider snapshots, EHR data spans the complete patient journey, capturing interactions across multiple healthcare settings.
Shaip’s de-identified EHR data catalog covers both — giving your team a single, compliance-ready source for the full spectrum of healthcare AI development.
EHR datasets contain two critical data types for AI development: structured data (demographics, ICD-10 diagnosis codes, DRG codes, medication lists, lab values, vital signs) and unstructured data (clinical notes, discharge summaries, radiology reports, physician dictation). Approximately 80% of EHR information is unstructured, making it the primary fuel for clinical NLP model training.
Find the right Electronic Health Records (EHR) Data For Your Healthcare AI
Improve your machine learning models with best-in-class training data. Shaip offers commercially available, de-identified Electronic Health Record (EHR) datasets designed specifically for AI and machine learning teams. Our off-the-shelf EHR data catalog provides structured, research-ready patient records across 20+ medical specialties — covering diagnoses, prescriptions, lab results, radiology reports, immunization history, and clinical notes — all fully de-identified under HIPAA Safe Harbor and GDPR standards.
Whether you’re building clinical decision support systems, training NLP models on physician notes, developing disease-prediction algorithms, or powering healthcare automation tools, Shaip’s EHR datasets give you the depth, diversity, and compliance assurance your AI project demands. Available for immediate licensing, custom cohort selection, or sample download.
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
| Location | Text Documents |
|---|---|
| NorthEast | 4,473,573 |
| South | 1,801,716 |
| MidWest | 781,701 |
| West | 1,509,109 |
| Major Diagnosis Category | Text Documents |
|---|---|
| Alcohol/Drug Use & Alcohol/Drug-Induced Organic Mental Disorders | 48,717 |
| Total including everything (Cases with & without MDC category) | 8,566,687 |
| Cases without reimbursement generated (MDC not specified) | 790,697 |
| Outpatient Cases (MDC not specified) | 1,980,606 |
| Cases using a specialty grouper such as 3M (MDC not specified) | 1,619,682 |
| Total with MDC | 4,175,702 |
| Alcohol/Drug Use or Induced Mental Disorders | 48,717 |
| Burns | 444 |
| Eye | 3,549 |
| Male Reproductive System | 9,230 |
| Human Immunodeficiency Virus Infections | 12,422 |
| Myeloproliferative Diseases & Disorders, Poorly Differentiated Neoplasms | 15,620 |
| Factors Influencing Health Status & Other Contacts with Health Services | 21,294 |
| Female Reproductive System | 17,010 |
| Ear, Nose, Mouth & Throat | 22,987 |
| Multiple Significant Trauma | 27,902 |
| Circulatory System | 589,730 |
| Blood, Blood Forming Organs & Immunologic Disorders | 48,990 |
| Injuries, Poisonings & Toxic Effects of Drugs | 64,097 |
| Skin, Subcutaneous Tissue & Breast | 89,577 |
| Hepatobiliary System & Pancreas | 127,172 |
| Endocrine, Nutritional & Metabolic Diseases & Disorders | 142,808 |
| Newborns & Other Neonates with Conditions Originating in the Perinatal Period | 163,605 |
| Pregnancy, Childbirth & the Puerperium | 165,303 |
| Kidney & Urinary Tract | 209,561 |
| Mental Diseases & Disorders | 282,501 |
| Nervous System | 316,243 |
| Digestive System | 346,369 |
| Musculoskeletal System & Connective Tissue | 329,344 |
| Respiratory System | 561,983 |
| Infectious & Parasitic Diseases | 559,244 |
We deal with all types of Data Licensing i.e., text, audio, video, or image. The datasets consist of Medical datasets for ML: Physician Dictation Dataset, Physician Clinical Notes, Medical Conversation Dataset, Medical Transcription Dataset, Doctor-Patient Conversation, Medical Text Data, Medical Images – CT Scan, MRI, Ultra Sound (collected basis custom requirements).
Real-World Applications of EHR Datasets in AI/ML
- Disease Prediction and Diagnosis: Train AI models to predict diseases such as diabetes, cancer, and cardiovascular conditions.
- Clinical Decision Support: Train models to surface diagnosis recommendations, flag drug interactions, and assist treatment planning using structured EHR data.
- Personalized Medicine: Use demographic and diagnosis data to recommend personalized treatment plans.
- Healthcare Automation: Automate administrative tasks like appointment scheduling or billing with NLP-powered tools trained on EHR datasets.
Predictive Healthcare Modelling — Build risk stratification and disease-prediction models using longitudinal patient records, DRG codes, and severity-of-illness scores.
Real-World Evidence (RWE) Studies — Generate post-market evidence and pharmacovigilance insights by analysing EHR outcomes data across patient cohorts.
NLP for Clinical Notes — Extract entities, conditions, and procedures from unstructured physician notes and discharge summaries using annotated EHR training data.
Why Choose Shaip for EHR Datasets?
Expert Workforce
Skilled professionals ensure accurate and high-quality data annotation.
Regulatory Compliance
Fully de-identified datasets adhering to HIPAA and GDPR.
Competitive Pricing
Cost-effective solutions delivered without compromising quality.
Bias-Free Data
Strict protocols eliminate bias, ensuring reliable AI outcomes.
Fast & Accurate
Streamlined processes ensure quick delivery of diverse, high-quality data.
Availability & Delivery
High network up-time & on-time delivery of data, services & solutions.
Proven at scale
Trusted by Google and leading health AI companies. Quality controlled by 6 Sigma Black Belt processes and medical expert review.
Commercially ready
Shaip's off-the-shelf EHR catalog is licensed, de-identified, and ready to download or access via the Databricks Marketplace today.
Full lifecycle support
Need annotation on top of raw data? Shaip provides de-identification, clinical NER labeling, and data augmentation from a single partner.
Can’t find what you are looking for?
New off-the-shelf medical datasets are being collected across all data types
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Frequently Asked Questions (FAQ)
1. What are EHR datasets used for in AI?
EHR datasets are used to train AI models for disease prediction, clinical decision-making, and personalized treatments.
2. How is EHR data used in AI/ML projects?
EHR data is used to train AI models for clinical decision support, disease prediction, personalized treatment planning, and healthcare automation.
3. Is EHR data de-identified?
Yes, all EHR data is de-identified to remove Personally Identifiable Information (PII) and comply with privacy regulations.
4. What are the key components of EHR data?
EHR data contains details like patient demographics, medical history, diagnoses, treatment plans, lab test results, radiology images (e.g., CT, MRI, X-rays), prescriptions, and immunization records.
5. Does the data comply with HIPAA and other regulations?
Yes, the data adheres to HIPAA, GDPR, and other global privacy standards to ensure secure and ethical usage.
6. Can EHR datasets be customized?
Yes, datasets can be tailored based on specific medical specialties, regions, patient demographics, or project requirements.
7. Can the data integrate into my AI models?
Yes, the datasets are provided in standard formats (e.g., JSON, CSV) for easy integration into AI and ML workflows.
8. How is data quality assured?
Data undergoes rigorous validation and quality checks to ensure accuracy, consistency, and reliability.
9. What is the cost of EHR datasets?
Costs depend on factors like data volume, customization, and project scope. We request that you fill out the “Contact Us” form with your requirements to receive the best quote.
10. What are the delivery timelines for EHR datasets?
Delivery timelines vary based on project size and complexity but are designed to meet agreed deadlines.
11. How can EHR datasets improve healthcare AI solutions?
EHR datasets enable AI systems to provide better diagnostics, predictive insights, and personalized treatment, improving patient outcomes and healthcare efficiency.
12. Can I get customized EHR datasets?
Yes, Shaip offers tailored EHR datasets based on specialty, age group, geography, or project requirements.
13. What is the difference between an EHR dataset and an EMR dataset?
Electronic Medical Records (EMRs) contain clinical data from a single provider; Electronic Health Records (EHRs) span the full care journey across multiple providers, settings, and time periods. Shaip provides both EHR and EMR dataset variants, with multi-provider longitudinal records available for complex AI training requirements.
14. How is the EHR data de-identified?
All records are de-identified using the HIPAA Privacy Rule Safe Harbor Method — removing all 18 PHI identifiers including name, date of birth, address, and medical record numbers. The anonymised MRN field is retained for record linkage within the dataset, allowing longitudinal analysis without re-identification risk.
