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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Consultation analysis: use of free text versus coded text.

Pablo Millares Martin1

  • 1Whitehall Surgery, Wortley Beck Health Centre, Leeds, LS12 5SG UK.

Health and Technology
|February 1, 2021
PubMed
Summary

Improving clinical coding in UK general practice requires understanding clinician needs. A new tool assesses text quantity and quality in electronic health records, revealing coding patterns and informing tailored training to enhance data accuracy.

Keywords:
Clinical codingElectronic health recordFamily practiceGeneral practiceRecordsSystematized Nomenclature of Medicine

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Area of Science:

  • Health Informatics
  • General Practice
  • Clinical Documentation

Background:

  • Electronic health records (EHRs) have been used in UK general practice for over 20 years.
  • Poor coding of clinical information in EHRs remains a significant challenge.
  • Lack of clinician interest and inadequate training are key barriers to improving coding.

Purpose of the Study:

  • To demonstrate a tool for assessing text quantity and quality in medical consultations.
  • To understand clinicians' coding use and identify specific training needs.
  • To provide a method for assessing changes in coding practices.

Main Methods:

  • Development of a tool using word processing and spreadsheet software to analyze consultation text.
  • Quantitative analysis of free text and coded content within EHRs.
  • Qualitative comparison of coded versus free text using word clouds.
  • A preliminary study involved a randomized sample of five consultations from thirteen clinicians.

Main Results:

  • Average free text per consultation was 68.2 words; only 6% of text was coded on average.
  • Significant variation in coding percentages (0-13%) and text quantity observed among clinicians.
  • Identified patterns in clinician coding use and demonstrated differences in text application via word clouds.
  • Nomenclature issues, such as expressing time, may hinder coding accuracy.

Conclusions:

  • The demonstrated tool enables quantitative and qualitative assessment of clinical coding in EHRs.
  • Understanding individual clinician preferences and needs is crucial for developing effective, tailored training programs.
  • The findings highlight the potential to improve coding uptake and data quality in general practice.