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Related Experiment Video

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Diagnosis clarification by generalization to patient-friendly terms and definitions: Validation study.

Hugo J T van Mens1, Savine S M Martens2, Elisabeth H M Paiman2

  • 1Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, Netherlands; Department of Research & Development, ChipSoft B.V., Amsterdam, Netherlands.

Journal of Biomedical Informatics
|April 16, 2022
PubMed
Summary

Generalizing medical diagnoses into patient-friendly terms can improve understanding of health records. However, errors in generalization require clinical validation to ensure accuracy and patient safety.

Keywords:
DiagnosesHealth literacyPatient access to recordsPatient-friendly terminologyPersonal health recordsSNOMED CT

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Area of Science:

  • Medical Informatics
  • Clinical Terminology
  • Patient Education

Background:

  • Patients increasingly access healthcare records, necessitating clear explanations.
  • Patient-friendly terms and definitions can aid understanding of medical data.
  • Creating patient-friendly descriptions for medical terms is costly and time-consuming.

Purpose of the Study:

  • To assess the medical validity of generalizing diagnoses to patient-friendly terms using SNOMED CT.
  • To identify factors contributing to invalid diagnosis clarifications.

Main Methods:

  • Developed an algorithm using the SNOMED CT hierarchy to generalize diagnoses.
  • Two raters evaluated a random sample of 1,131 patient-friendly clarifications for correctness and acceptability.
  • Analyzed errors related to terminology, algorithms, and clarification quality.

Main Results:

  • Errors were found in 12.7% of clarifications, with 14.3% deemed unacceptable for patient viewing.
  • Low interrater reliability (ICC 0.34 for correctness, 0.43 for acceptability) was observed.
  • Errors stemmed from patient-friendly terms, terminology mappings, modeling, and the algorithm itself.

Conclusions:

  • Generalizing diagnoses can produce many correct and acceptable patient-friendly clarifications.
  • Clarification quality is highly dependent on terminology mapping and modeling.
  • Clinical validation and quality improvement are crucial before implementing generalized clarifications.