EHRs are digital compilations of a patient’s medical history, shared across different healthcare settings. Aggregated, these records form vast, longitudinal electronic health record datasets. Researchers and public health analysts use this data to study disease patterns, evaluate treatment effectiveness, and develop predictive models. Accessing these sensitive datasets is not a simple download, but a structured procedure. Strict protocols are necessary to ensure patient confidentiality while maximizing research utility.
Types of Accessible EHR Datasets
EHR datasets contain a mixture of structured and unstructured information, requiring different analytical approaches.
Structured data includes standardized, quantifiable elements that are easily searchable and computable. Examples include laboratory test results, medication lists, patient demographics, and diagnosis codes (e.g., ICD-10). This format is highly desirable for large-scale statistical analysis and machine learning applications.
Unstructured data consists of free-text clinical notes, physician narratives, and radiology or pathology reports. This data holds richer context about a patient’s care but requires complex processing, such as natural language processing (NLP), to extract meaningful insights. Researchers often combine both data types to build a complete picture of a patient’s clinical journey.
Data custodians provide two distinct formats to mitigate privacy risks when preparing data for public download. De-identified data is derived from real patient records but has specific personal identifiers removed or altered to prevent re-identification. In contrast, synthetic data is artificially generated, replicating the statistical properties of the original EHR data without containing actual individual information. Synthetic datasets are inherently private and are used for initial tool development and testing before moving to the more restricted de-identified data.
Major Public Data Repositories
Several publicly accessible repositories offer downloadable EHR datasets, specializing in different patient populations or clinical domains. PhysioNet is a recognized resource hosting large critical care databases, including MIMIC-IV (Medical Information Mart for Intensive Care) and the eICU Collaborative Research Database.
MIMIC-IV contains de-identified data from patients admitted to a large tertiary care hospital ICU. This dataset includes vital signs, medications, laboratory measurements, and survival data, making it a valuable tool for acute illness research. The eICU Collaborative Research Database provides multi-center critical care data, covering a broader range of ICUs across the United States.
Government sources also provide extensive health data, often aggregated rather than raw patient records. The NIH All of Us Research Program offers access to a diverse, longitudinal dataset including EHRs, genomic data, and survey responses. Access is managed through the secure Researcher Workbench platform.
Other government portals, such as HealthData.gov and Data.CDC.gov, provide numerous datasets. These include public use files from the National Electronic Health Records Survey (NEHRS). These files typically focus on high-level statistics and EHR system adoption rather than individual patient-level data.
Prerequisites for Data Access
Downloading high-utility EHR data requires researchers to undergo a formal credentialing process for responsible use and compliance. For repositories like PhysioNet, researchers must register as a credentialed user. This is followed by mandatory ethics training, such as the CITI (Collaborative Institutional Training Initiative) course module focused on Data or Specimens Only Research.
The user must then formally sign a Data Use Agreement (DUA). This legally binding contract outlines permitted data uses, mandates against attempting re-identification, and stipulates conditions for data storage and sharing. This multi-step process acts as a gatekeeping mechanism to protect patient privacy.
Access to the NIH All of Us Research Program dataset involves similar structured requirements. The researcher’s affiliated institution must have a Data Use and Registration Agreement (DURA) with the program. Individual researchers must complete specific Responsible Conduct of Research training and sign a Data User Code of Conduct (DUCC).
All analysis of individual-level All of Us data must be performed entirely within the secure, cloud-based Researcher Workbench environment. This workspace prevents researchers from downloading or exporting individual participant data, ensuring the information remains protected. Completing these steps confirms the researcher has the ethical knowledge and institutional backing to handle sensitive health information.
Understanding Patient Privacy and De-Identification
Rigorous access procedures protect Protected Health Information (PHI), which includes any information that could potentially identify an individual. PHI includes obvious identifiers (names, social security numbers) and less obvious ones (full dates of birth, small geographic subdivisions). The U.S. Health Insurance Portability and Accountability Act (HIPAA) privacy rules govern the use and disclosure of this information.
To make data available for research, providers must de-identify it by removing or obscuring all direct identifiers. This is accomplished either by removing 18 specific categories of identifiers (the Safe Harbor method) or by having a qualified statistical expert confirm the risk of re-identification is very small. The goal is to maximize data utility for research while minimizing patient risk.
The principles of de-identification align with global standards, such as the EU’s General Data Protection Regulation (GDPR), which emphasizes anonymization. Since even de-identified data carries a small theoretical risk of re-identification, Data Use Agreements legally prohibit researchers from attempting to link the data back to any specific person. This framework ensures health research benefits the public without compromising individual confidentiality.