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.<Table Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry nameend="c2" namest="c1"> 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. </entry> </row> </tbody> </tgroup> </Table>

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

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2025年8月16日

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

摘要

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.