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What Is EHR Data?

Electronic health records (EHR) data refers to the records of medical patients. It is also known as EMR (electronic medical records) data.

Where Does EHR Data Come From?

Clinics and hospitals maintain EHR databases based on their own records and patient reports.

These databases may be specific to private clinics or part of a national health service. Some also work with both private and public services.

What Types of Columns/Attributes Should I Expect When Working with This Data?

Most EHR data relates to individual patients. For example, patient diagnoses, demographics, progress notes, and immunization records.

Good software programs that integrate EHR databases also offer other useful features, like automatic alerts for potential drug interactions.

What Is This Data Used For?

The main purpose of maintaining this data is to provide better care for patients.

Medical professionals may also find that user-friendly EHR systems allow them to spend less time on records-keeping which in turn enables them to focus more on patient care.

Additionally, records of equipment use and patient feedback can help clinics and hospitals make decisions about equipment purchases or employee shifts.

How Should I Test the Quality of EHR Data?

One of the most often cited tests of EHR data quality are the 3×3 DQA Guidelines. This guideline says, in essence, that a good database should be complete, correct, and current for patients, time, and variable (e.g., diagnosis).

Most importantly, you should consider that medical personnel and administrators will interact with the EHR system throughout the day, and will not have much time to input information properly. In other words, make sure that the database is always user-friendly.

Interesting Case Studies and Blogs to Look Into

Solo Family Practitioner Demonstrates Care Coordination with Referring Physicians | HealthIT.gov
Infosys: Electronic Health Record Case Study

Tangible Examples of Impact

Researchers from the University of Michigan have developed an open-source framework that streamlines the preprocessing of data extracted from the electronic health record.

The framework, which the researchers call FIDDLE (Flexible Data-Driven Pipeline), has the power to greatly speed up EHR data preprocessing and assist machine learning (ML) practitioners working with health data, according to a study published this week in the Journal of the American Medical Informatics Association.

Healthcare IT News: New framework helps streamline EHR data extraction

Relevant datasets

Verisk Life Insurance

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Verisk Life Insurance provides assistance to clients in managing risk in the life insurance market.

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EMIS Health EMIS Web

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EMIS Health EMIS Web allows healthcare providers, community care services and hospitals to share expertise and information between their varying areas improving customer care and safety.

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EMIS Health Hospital Medicines Management

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Hospital Medicine Management services by EMIS Health allows clinicians to stay informed of vital patient data to make informed prescription decisions that reduce risks and streamline processes.

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