Toward Reliable Symptom Coding in Electronic Health Records for Symptom Assessment and Research: Identification and Categorization of International Classification of Diseases, Ninth Revision, Clinical Modification Symptom Codes

Comput Inform Nurs. 2024 Sep 1;42(9):636-647. doi: 10.1097/CIN.0000000000001146.

Abstract

To date, symptom documentation has mostly relied on clinical notes in electronic health records or patient-reported outcomes using disease-specific symptom inventories. To provide a common and precise language for symptom recording, assessment, and research, a comprehensive list of symptom codes is needed. The International Classification of Diseases, Ninth Revision or its clinical modification ( International Classification of Diseases, Ninth Revision, Clinical Modification ) has a range of codes designated for symptoms, but it does not contain codes for all possible symptoms, and not all codes in that range are symptom related. This study aimed to identify and categorize the first list of International Classification of Diseases, Ninth Revision, Clinical Modification symptom codes for a general population and demonstrate their use to characterize symptoms of patients with type 2 diabetes mellitus in the Cerner database. A list of potential symptom codes was automatically extracted from the Unified Medical Language System Metathesaurus. Two clinical experts in symptom science and diabetes manually reviewed this list to identify and categorize codes as symptoms. A total of 1888 International Classification of Diseases, Ninth Revision, Clinical Modification symptom codes were identified and categorized into 65 categories. The symptom characterization using the newly obtained symptom codes and categories was found to be more reasonable than that using the previous symptom codes and categories on the same Cerner diabetes cohort.

MeSH terms

  • Clinical Coding / methods
  • Clinical Coding / standards
  • Diabetes Mellitus, Type 2 / diagnosis
  • Electronic Health Records* / statistics & numerical data
  • Female
  • Humans
  • International Classification of Diseases*
  • Male
  • Middle Aged
  • Symptom Assessment* / methods
  • Unified Medical Language System