EMR data extraction can be a complex task. Apart from the wide range of options available to you, your EMR contains a mixture of structured and unstructured data.

While most EMR data extraction activities require pulling data from normalized, structured data elements, there’s a significant volume of unstructured data that must not be ignored.

In a study published in the Journal of American Medical Informatics Association (JAMIA), there was high predictive value in unstructured real-world data.

This article will look at some methods for taking out vital information from your EMR for decision support and efficient patient care.

1. Use NLP for Text Notes

NLP stands for natural language processing. NLP is an effective way to extract data from clinical notes in free-text format.

Generally, machine learning algorithms work best on structured data. But in healthcare, tons of relevant information can only be obtained from free-text documents written in an unstructured form.

Many free-text notes contain data that analysts and clinicians can use to predict disease conditions and recommend prompt treatment.

NLP algorithms can find relevant healthcare-specific keywords in a text document. These include drug codes, diagnosis, and clinical procedures.

After obtaining the data from clinical notes, the algorithm can resolve them into a single record. Then a machine learning application can make patterns evident.

In a study on automated decision support for sepsis case prediction, the researchers used NLP to pull out data from clinical text. The results showed that the area under the curve was greater with NLP than without it.

2. Work With AI to Extract Unstructured Data

Various AI tools are being developed to extract value from the real-world evidence embedded in unstructured EHR data. In a study published in JAMIA, the researchers said that overall, structured EHR data did not meet the requirements for regulatory grade criteria while unstructured data did.

In another study published in JAMIA, scientists at the University of Michigan developed a framework to preprocess data extracted from an EHR. The essence of the framework is to solve problems associated with EHR data extraction, such as:

  • Missing values
  • Unstructured data
  • Multiple data types
  • Irregularities in sampled data

Using a preprocessor for EHR data extraction helps prepare the data into a format suitable for established machine learning techniques.

3. Invest in APIs

An application programming interface (API) can simplify the process of extracting data from your EHR. With APIs, you can extract data from your EMR and transfer it to an archive or send it to another provider.

Similarly, patients can access and compile their data from different providers and view them in a single portal. The data compiled will allow physicians to make effective decisions and recommendations from complete information.

When a primary care doctor and a specialist want to exchange information about a patient, they usually do so with documents that have hundreds of pages. But with APIs, the physician can extract the relevant portions for the specialist to connect his EHR to the physician’s EHR and pick up the information pertinent to the patient’s treatment.

Big data is now an essential part of healthcare. However, you need various tools to pull out the hidden value in the data stored in your EHR.

Discover More EMR Data Extraction Techniques

Choosing the most suitable tool for effective data extraction requires expert help. Call MediQuant at 844.286.8683 today to book a free consultation. Reach out to us through our contact page to discuss your data extraction needs.

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