Work

Utilizing electronic health record pre-consultation data to create a predictive algorithm for diagnosis of chronic pediatric rheumatic conditions

Year: 2025

Type: article

Abstract: OBJECTIVES: To develop a predictive algorithm for diagnosing chronic pediatric rheumatic conditions using patient-reported, historical, and referral data in the electronic health record (EHR) to address current lengthy consultation wait times. METHODS: All new rheumatology patient evaluations from 2021 to 2023 were retrospectively reviewed to identify the reason for the visit, patient-recorded outcomes, and international classification of disease codes. The data sample was randomly split into 80% derivation and 20% validation sets. Logistic regression evaluated the association of diagnosis and referral data; variables with p < 0.2 in univariate were included in a multivariate model. Complete data are reported. RESULTS: Of the 3139 subjects, 2064 (66%) were female, with a median age of 13 [IQR 8, 16]. Patients diagnosed with inflammatory arthritis numbered 319 (10%), while 55 (2%) were diagnosed with systemic lupus erythematosus (SLE). The median time from the first visit to diagnosing inflammatory arthritis and SLE was 88 days [35, 210] and 42 [17, 132], respectively. In univariate analysis, a referral reason for swelling was positively associated with a new inflammatory arthritis diagnosis. In contrast, antinuclear antibody positivity, rash, and lupus were positively associated with a new SLE diagnosis. Referral data had low sensitivity and high specificity for both inflammatory arthritis and SLE diagnoses, with areas under the curve of 0.59 and 0.65, respectively. CONCLUSION: Utilizing the EHR to create a predictive algorithm for chronic rheumatic disease presents a promising solution to existing patient care challenges. This approach suggests that integrating such models to the referral process could help expedite access to pediatric rheumatology services. Key Points • Patient referrals to pediatric rheumatology specialists often lead to non-rheumatic diagnosis. • Patient-reported data within the electronic health record can be utilized to predict likelihood of rheumatic disease. • Electronic algorithms to predict rheumatic disease could expedite patient care access to pediatric rheumatology, which currently has a physician shortage and potentially long wait times.

Source: Clinical Rheumatology

DOI: 10.1007/s10067-025-07631-5

Authors: Kendra R. Lauer, Kyla Driest, Laura R. Pratt, Alysha Taxter

Institutions: Nationwide Children's Hospital, The Ohio State University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,890