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Electronic health record (EHR) diagnostic coding accuracy is low, with over half of codes being appropriate and a quarter omitted. Improving clinician training and EHR design can enhance coding precision.

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Area of Science:

  • Health Informatics
  • Medical Coding
  • Clinical Documentation

Background:

  • Accurate diagnostic coding is crucial for healthcare reimbursement, research, and quality measurement.
  • Electronic Health Records (EHRs) are widely used, but their impact on coding accuracy requires further investigation.

Purpose of the Study:

  • To evaluate the accuracy and completeness of diagnostic coding performed by ambulatory clinicians using EHR systems.
  • To analyze clinician search strategies and identify patterns of coding errors and omissions.

Main Methods:

  • A simulation study involving 23 ambulatory clinicians coding six standardized scenarios across two EHRs.
  • Analysis of clinician interactions with EHR query interfaces, focusing on search terms, code accuracy, and omissions.

Main Results:

  • Only slightly over half of entered diagnostic codes were appropriate for the given scenarios.
  • Approximately 25% of codes were omitted, with higher omission rates for secondary diagnoses.
  • Crohn's disease and diabetes scenarios exhibited the highest rates of inappropriate coding and code variation.
  • Significant variation was observed in search terms used for querying the same diagnoses.

Conclusions:

  • Current EHR systems and clinician training may contribute to suboptimal diagnostic coding accuracy.
  • Improvements in EHR interface design and clinician education are recommended to reduce coding errors and omissions.