Identifying Emergency Department Symptom-Based Diagnoses with the Unified Medical Language System

Benjamin Slovis,Katherine Piserchia,Danielle Mccarthy,Amanda Doty,Garrison Nord,Kristin Rising

doi:10.5811//westjem.2019.8.44230

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.

Highlights

Many patients who are discharged from the emergency department (ED) with a symptom-based discharge diagnosis (SBD) have post-discharge challenges related to lack of a definitive discharge diagnosis and follow-up plan
We describe a method for automated identification of symptom-based diagnosis (SBD) from ICD-10 codes using the Unified Medical Language System (UMLS) Metathesaurus
We identified 3642 ED discharges with 1382 unique discharge diagnoses that corresponded to 875 unique ICD-10 codes and 10 UMLS semantic types

Summary

Introduction

Patients are commonly discharged from the emergency department (ED) without a pathological diagnosis to explain their symptoms, with one study finding that over one third of patients leave the ED with a symptom-based diagnosis (SBD).[1] Studies exploring reasons for return ED visits have identified high levels of patient uncertainty related to lack of a definitive diagnosis as one cause for return.[2,3,4] These findings suggest the need for further research regarding the impact of and needs associated with receiving a SBD at the time of ED discharge and on patient transitions home from the ED Research on this topic is challenging, because electronic health records (EHR) do not have a unique identifier for SBDs, and there is no agreed upon classification system for these conditions. The International Statistical Classification of Diseases and Health Related Problems, 10th edition (ICD-10)[8] code “R07.4 – Chest Pain” and Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT)[9] code “29857009 – Chest Pain (finding)” both map to the UMLS concept unique identifier (CUI) “C0008031 – Chest Pain.”

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