PS6:124 Algorithms to identify sle from ehr data. (21st March 2018)

• Record Type: Journal Article
• Authors: Ramsey-Goldman, R; Walunas, T; Jackson, K; Chung, A; Erickson, D; Mancera-Cuevas, K; Kho, A
• Is Part Of: Lupus science & medicine. Volume 5(2018)Supplement 1
• Page Start: A97
• Page End: A97
• Publication Date: 2018-03-21
• Subjects: Systemic Lupus Erythematosus -- Algorithms -- Electronic Medical Record; Systemic lupus erythematosus -- Periodicals; 616.772005
• DOI: 10.1136/lupus-2018-abstract.167
• Languages: English
• ISSNs: 2398-8851

Abstract

Abstract : Background: SLE is difficult to diagnose because of the diverse manifestations occurring over time and across care sites. Electronic health records (EHR) present a rich source of patient information which can be mined for diagnosis and identification to improve quality of care or to enrol patients in studies. Aim: Build a rule-based algorithm for each revised 1982/1997 ACR classification criteria for SLE using EHR data. Methods: We included patients from the Chicago Lupus Database (CLD) fulfilling 4 or more of the ACR classification criteria for SLE who also had records in the Northwestern Medicine Electronic Data Warehouse (NMEDW) EHR. ICD-9 codes and lab test results for each ACR SLE criterion were ascertained. We queried patient diagnoses, lab results and used a simple chart string for lab test results from physician notes. Results: Data from 515/783 patients in CLD and the NMEDW EHR were included. When using ICD 9 codes only 8.8% of patients from CLD/NMEDW were identified. With the addition of lab results to the query concordance increased to 54.7%, and a simple text string query to search physician notes for additional lab results increased identification to 57.5%. Conclusion: Using ICD codes plus laboratory data from NMEDW increased fulfilment of classification criteria but is still not optimal for patient identification. Additional strategies such as using natural language processing (NLP) or examining fulfilment of SLICC classification criteria for SLE which includes more lab results than ACR may yield an improved rule-based algorithm for the identification of SLE patients in EHR data.