Electronic Health Records (EHR) Datasets for AI & ML Projects
Off-the-shelf Electronic Health Records (EHR) Datasets to Jumpstart your Healthcare AI project.
Plug in the data source you’ve been missing today
Find the right Electronic Health Records (EHR) Data For Your Healthcare AI
Improve your machine learning models with best-in-class training data. 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. Our Off-the-shelf data catalog makes it easy for you to get medical training data you can trust.
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
EHR Data by Location
| Location | Text Documents |
|---|---|
| NorthEast | 4,473,573 |
| South | 1,801,716 |
| MidWest | 781,701 |
| West | 1,509,109 |
EHR Data by Major Diagnosis Category
| EHR Data by 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: Enhance decision-making by providing AI systems with rich patient histories and lab results.
- 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.
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.
Customizable Solutions
Tailored datasets based on demographics, specialties, or regions.
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.
Global Workforce
With a pool of onshore & offshore resources, we can build and scale teams as required for various use cases.
People, Process & Platform
With the combination of a global workforce, robust platform, & operational processes designed by 6 sigma black belts, Shaip helps launch the most challenging AI initiatives.
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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.