Identifying Emergency Department Symptom-Based Diagnoses with the Unified Medical Language System
Abstract
Introduction: Many patients who are discharged from the emergency department (ED) with asymptom-based discharge diagnosis (SBD) have post-discharge challenges related to lack of adefinitive discharge diagnosis and follow-up plan. There is no well-defined method for identifyingpatients with a SBD without individual chart review. We describe a method for automated identificationof SBDs from ICD-10 codes using the Unified Medical Language System (UMLS) Metathesaurus.
Methods: We mapped discharge diagnosis, with use of ICD-10 codes from a one-month period ofED discharges at an urban, academic ED to UMLS concepts and semantic types. Two physicianreviewers independently manually identified all discharge diagnoses consistent with SBDs. Wecalculated inter-rater reliability for manual review and the sensitivity and specificity for our automatedprocess for identifying SBDs against this “gold standard.”
Results: We identified 3642 ED discharges with 1382 unique discharge diagnoses that correspondedto 875 unique ICD-10 codes and 10 UMLS semantic types. Over one third (37.5%, n = 1367) of EDdischarges were assigned codes that mapped to the “Sign or Symptom” semantic type. Inter-raterreliability for manual review of SBDs was very good (0.87). Sensitivity and specificity of our automatedprocess for identifying encounters with SBDs were 84.7% and 96.3%, respectively.
Conclusion: Use of our automated process to identify ICD-10 codes that classify into the UMLS “Signor Symptom” semantic type identified the majority of patients with a SBD. While this method needsrefinement to increase sensitivity of capture, it has potential to automate an otherwise highly timeconsumingprocess. This novel use of informatics methods can facilitate future research specific topatients with SBDs.