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# Electronic Health Record Data
"*EHRs are, at their simplest, digital (computerized versions) of a patients' paper charts. But EHRs, when fully up and running, are so much more than that.*" \[Only found iterations of this quote so unsure how to cite from our own powerpoint]
## Electronic Health Record (EHR) System \[[1](https://healthit.gov/health-it-basics/benefits-ehrs/)]
EHRs make information available instantly, “whenever and wherever it is needed.” And, they bring together in one place everything about a patient’s health.
EHRs can:
* Contain information about a patient’s medical history, diagnoses, medications, immunization dates, allergies, radiology images, and lab and test results
* Offer access to evidence-based tools that providers can use in making decisions about a patient’s care
* Automate and streamline providers’ workflow
* Increase the organization and accuracy of patient information
* Support key market changes in payer requirements and consumer expectations
One of the key features of an EHR is that it can be created, managed, and consulted by authorized providers and staff across more than one healthcare organization.
A single EHR can bring together information from current and past doctors, emergency facilities, school and workplace clinics, pharmacies, laboratories, and medical imaging facilities.
## EHR Data Uses
### Primary Uses
"Data collected about and used for the direct care of a patient"
* Patient care
* Delivery
* Management
* Support
* Billing and reimbursement
### Secondary Uses
"Data are used for purposes other than those for which they were originally collected"
* Decision support (development)
* Quality assurance
* Research
* Education
* Public Health
* Regulation
## Common and Emerging Data Types \[[2](https://www.ncbi.nlm.nih.gov/books/NBK551878/)]
### Patient identifiers
* Include patient’s full name, date of birth, and contact information
### Demographics
* Age, gender, and ethnicity/race
### Diagnoses
### Medications
* Information on prescriptions that are written
* Pharmacy claims data contain information on prescriptions that were filled
### Procedures
* Clinical procedures such as surgery, radiology, pathology, and laboratory
### Laboratory Data
### Vital Signs
* Physiological variables such as height, weight, body mass index, pulse rate, blood pressure, respiratory rate, and temperature
### Utilization/Cost
* Includes cost, hospitalization, readmission, emergency room admission or other significant healthcare events
### Surveys
* Involves self reported questionnaires,
* Standardized EHR-integrated surveys: Patient Reported Outcomes Measures (PROMs), Patient Health Questionnaires (PHQ), Health Risk Assessments (HRA), Life Event Checklist (LEC), and Generalized Anxiety Disorder (GAD) screening tools.
### Social Data
* Individual level factors (smoking status)
* Community level elements (socioeconomic status)
### Patient-generated Data
* Includes physical activity, sleep patterns, self-reported sign and symptoms, uploaded blood sugar levels
* Collected by integrated personal health records, mobile-health exchange platforms, wearable device interfaces
## An Expansion on Data Types \[[3,4](#references)]
In the previous section, we introduced two common categories of health data: structured and unstructured data. However, data may also be semi-structured. Semi-structured data do not follow the rigid tabular organization or predefined schema typically associated with relational databases. Instead, they use tags, keys, or other markers to separate semantic elements and establish relationships or hierarchies within the data. Examples include JavaScript Object Notation (JSON) and Extensible Markup Language (XML). Because semi-structured data retain some organization, they are generally easier to search, process, and analyze than fully unstructured data.
Metadata are another important concept related to data organization. Metadata can be understood as “data about data” because they provide information about a resource’s content, meaning, origin, format, and context. The Network of the National Library of Medicine describes metadata as “information that describes, explains, locates, classifies, contextualizes, or documents an information resource” [\[4\]](#references). Metadata can make resources easier to locate, interpret, manage, and share. Examples include the creator’s name, date of creation, source, file format, keywords, measurement units, and coding-system version. Metadata are especially important in biomedical and bioinformatics resources, where understanding a dataset’s provenance and conditions of collection is necessary for appropriate interpretation and reuse.
## References
1. Office of the National Coordinator for Health Information Technology. [Benefits of EHRs.](https://www.healthit.gov/health-it-basics/benefits-ehrs/) HealthIT.gov. Updated December 10, 2025. Accessed August 5, 2026.
2. Ehrenstein V, Kharrazi H, Lehmann H, et al. Obtaining Data From Electronic Health Records. In: Gliklich RE, Leavy MB, Dreyer NA, editors. Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide, 3rd Edition, Addendum 2 \[Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2019 Oct. Chapter 4 [Obtaining Data from Electronic Health Records](https://www.ncbi.nlm.nih.gov/books/NBK551878/).
3. Big Data Framework. [Data Types: Structured vs. Unstructured Data.](https://www.bigdataframework.org/data-types-structured-vs-unstructured-data/) Enterprise Big Data Framework. Updated July 17, 2024. Accessed August 19, 2026.
4. Network of the National Library of Medicine. [Metadata.](https://www.nnlm.gov/resources/data/data-glossary/metadata) NNLM Data Glossary. Updated August 13, 2026. Accessed August 19, 2026.
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