License High-quality Healthcare/Medical Data for AI & ML Models
Off-the-shelf Healthcare/Medical Datasets to jumpstart your Healthcare AI project
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Medical and Healthcare datasets for Machine Learning
Physician Dictation Audio Data
Our de-identified dataset for healthcare include 31 different specialties audio files dictated by physicians describing patients’ clinical condition and plan of care based on physician-patient encounters in the hospital/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 and patient conversation, transcription of medical reports and medical assessment. It helps in mapping the medical history of the patient for future visits and also acts as a refence point for the doctors. It helps the doctor to 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
- 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
CT Scan Image Dataset
Doctors use the CT scan image to diagnose and detect abnormal or normal conditions in a patient’s body (i.e., to identify disease or injury within various body parts). In the computerized image processing diagnosis, a CT-scan image goes through sophisticated phases, viz., acquisition, image enhancement, extraction of important features, Region of Interest (ROI) identification, result interpretation, etc.
Shaip provides high-quality CT scan image datasets essential for research and medical diagnosis. Our datasets include thousands of high-resolution images collected from real patients and processed with state-of-the-art techniques. These datasets are designed to help medical professionals and researchers improve their knowledge and understanding of various medical conditions, including cancer, neurological disorders, and cardiovascular diseases. With Shaip, you can access reliable and accurate medical data to enhance your research and improve patient outcomes.
MRI Image Dataset
Computer vision models are designed to derive meaningful information from digital images and videos, according to IBM. It allows extensive use of healthcare image data to provide better diagnosis, treatment, and prediction of diseases. It can use context from the image sequence, texture, shape, and contour information, as well as past knowledge, to produce 3D and 4D information that aids in improved human understanding. Like CT scans, MRIs are also used to diagnose and detect abnormal or normal conditions in a patient’s body (i.e., to identify disease or injury within various body parts).
Shaip provides high-quality MRI image datasets essential for research and medical diagnosis. Our datasets include thousands of high-resolution images collected from real patients and processed with state-of-the-art techniques.
X-Ray Image Dataset
X-ray testing is used to verify the internal structure and integrity of the object. X-ray images of a test object can be generated at different positions and different energy levels to diagnose and detect abnormal conditions in a patient’s body.
Shaip provides high-quality X-Ray image datasets essential for research and medical diagnosis. Our datasets include thousands of high-resolution images collected from real patients and processed with state-of-the-art techniques. With Shaip, you can access reliable and accurate medical data to enhance your research and improve patient outcomes.
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Frequently Asked Questions (FAQ)
A healthcare dataset is a collection of health-related data, often structured and gathered for analysis, research, and decision-making in medical and healthcare domains.
Examples include electronic health records (EHRs), medical imaging databases, genomic sequences, patient demographics, and datasets from wearable health devices.
Healthcare datasets support medical research by providing insights into disease patterns, treatment outcomes, patient behavior, drug efficacy, and more, thereby aiding in medical advancements and policy formation.
Common formats include CSV, Excel, DICOM (for medical imaging), and HL7 (for health records).
Privacy concerns arise from the potential misuse of sensitive patient data, leading to identity theft, discrimination, or unwanted exposure to personal health information.
Patient information is protected using de-identification (removing personally identifiable information), encryption, strict access controls, and adherence to regulations like HIPAA (in the U.S.).
To ensure quality, regularly validate and clean the dataset, use standardized data collection methods, cross-reference with reliable sources, and involve domain experts for verification.