Computer-Based Natural Language Search Applied to the Electronic Medical Record for Tonsil Triage

Abstract

Abstract Objectives To determine significant histologic findings in tonsils and categorize clinical settings in which they occur to identify cases benefiting from histopathologic examination using a computer-based natural language search (NLS) applied to the electronic medical record. Methods The pathology database was queried for tonsillectomy cases accessioned between 2002 and 2018. Tonsils with microscopic examination were reviewed, and indication for examination and diagnoses were tallied. Clinical risk of malignancy was correlated with findings. A NLS was used to interrogate preoperative clinical records of the same group of patients. The search identified cases at risk of significant histologic findings and was implemented as part of standard practice. Results Of the 18,733 bilateral tonsillectomies identified in the pathology database, 494 were palatine tonsils that underwent microscopic examination, 134 had indications concerning for malignancy, and 14 had significant findings on histologic examination. When the NLS was applied to the medical record of the same group, 223 cases were identified as having risk of malignancy, including all flagged by surgeons and pathologists and 89 additional cases. Clinical implementation resulted in identification of all cases benefiting from examination. Conclusions A NLS applied to the electronic medical record to select tonsils for examination was superior to relying on surgeons and pathologists.