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

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 202...

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Bibliographic Details
Published in:Clinical rheumatology Vol. 44; no. 10; pp. 4203 - 4214
Main Authors: Lauer, Kendra R., Driest, Kyla, Pratt, Laura R., Taxter, Alysha
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.10.2025
Springer Nature B.V
Subjects:
Adolescent
Algorithms
Antinuclear antibodies
Arthritis
Autoimmune diseases
Child
Childhood
Chronic Disease
Complaints
Diagnosis
Electronic Health Records
Electronic medical records
Female
Fibromyalgia
Humans
Inflammation
Lupus
Lupus Erythematosus, Systemic - diagnosis
Male
Medicine
Medicine & Public Health
Original Article
Pain
Patients
Pediatrics
Prediction Algorithms
Questionnaires
Referral and Consultation
Regression analysis
Retrospective Studies
Rheumatic diseases
Rheumatic Diseases - diagnosis
Rheumatology
Sensitivity analysis
Systemic lupus erythematosus
Workforce
United States > US
Systemic lupus erythematosus
Electronic health record
Pediatric rheumatology
Juvenile idiopathic arthritis
ISSN:0770-3198, 1434-9949, 1434-9949
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Summary: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.
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ISSN:0770-3198
1434-9949
1434-9949
DOI:10.1007/s10067-025-07631-5