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Electronic Health Record Optimization for Artificial Intelligence.

Anand S Dighe1

  • 1Department of Pathology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114-2696, USA.

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Summary
This summary is machine-generated.

Implementing effective laboratory clinical decision support (CDS) requires standardized electronic health record (EHR) data. Structured EHR data is crucial for sustainable laboratory CDS and AI algorithm integration.

Keywords:
Artificial intelligenceClinical decision supportClinical laboratoryElectronic health record

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Area of Science:

  • Medical Informatics
  • Clinical Laboratory Science
  • Artificial Intelligence in Healthcare

Background:

  • Laboratory clinical decision support (CDS) systems primarily utilize data from electronic health records (EHRs).
  • The effectiveness and sustainability of laboratory CDS programs are contingent upon data quality and accessibility within EHRs.

Purpose of the Study:

  • To highlight the critical need for standardization and harmonization of EHR data elements for successful laboratory CDS implementation.
  • To address the limitations imposed by unstructured EHR data on the direct application of artificial intelligence (AI) in CDS.
  • To emphasize the challenges associated with data preprocessing for laboratory-based AI algorithms due to EHR data heterogeneity.

Main Methods:

  • Analysis of current laboratory CDS practices and their reliance on EHR data.
  • Evaluation of the impact of data standardization and harmonization on CDS effectiveness.
  • Assessment of the requirements for integrating AI algorithms into laboratory CDS, focusing on data structuring.

Main Results:

  • A significant barrier to sustainable laboratory CDS and AI integration is the heterogeneity and lack of structure in typical EHR data.
  • Standardization and harmonization of key EHR data elements are foundational requirements for effective laboratory CDS.
  • The direct application of AI in CDS is currently limited by the need for structured EHR data, necessitating careful data preprocessing.

Conclusions:

  • Effective laboratory CDS necessitates a foundational commitment to standardizing and harmonizing EHR data.
  • The integration of AI into laboratory CDS is feasible only with structured EHR data, requiring robust preprocessing strategies.
  • Addressing EHR data heterogeneity is paramount for advancing laboratory CDS and leveraging AI capabilities.