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Related Experiment Video

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A replicable, open-source, data integration method to support national practice-based research & quality improvement

Marta Fernandes1, Maria A Donahue2, Dan Hoch3

  • 1Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States.

Epilepsy Research
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

A new data integration method successfully extracts structured epilepsy data from clinical notes for a national learning healthcare system, improving reporting and participation for epilepsy patients.

Keywords:
EpilepsyInternational League Against EpilepsyLearning Health SystemQuality ImprovementSeizure controlText analytics

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Area of Science:

  • Neurology
  • Health Informatics
  • Data Science

Background:

  • The Epilepsy Learning Healthcare System (ELHS) aims to improve epilepsy outcomes, but data system fragmentation hinders progress.
  • Integrating real-world clinical data into national research and quality improvement (QI) systems is crucial for advancing epilepsy care.

Purpose of the Study:

  • To test the feasibility of an open-source Data Integration (DI) method for connecting epilepsy clinical data to national systems.
  • To overcome barriers in data fragmentation for improved reporting and participation in the ELHS.

Main Methods:

  • Developed and validated a DI method to extract, preprocess (using text mining), and structure epilepsy data from the Electronic Health Record (EHR) Enterprise Data Warehouse (EDW).
  • Utilized EPIC SmartPhrases/Forms to collect standardized epilepsy data, including diagnosis, seizure type, frequency, and medication adherence.
  • Matched extracted data fields using the ELHS data dictionary to create a structured dataset for uploading to the national ELHS registry.

Main Results:

  • The DI method automated data mining, preprocessing, and exporting of structured data for weekly access by clinicians and quality improvers.
  • During the study period (12/2018-12/2021), 1806 patients were included, with 5168 clinic visits documented.
  • The structured dataset, containing seizure control information, was successfully uploaded to the national ELHS registry.

Conclusions:

  • Structured epilepsy patient data can be feasibly extracted from clinical text notes.
  • This DI method enables weekly reporting to a national learning healthcare system, facilitating quality improvement and research in epilepsy care.