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