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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Veröffentlicht in:	Clinical rheumatology Jg. 44; H. 10; S. 4203 - 4214
Hauptverfasser:	Lauer, Kendra R., Driest, Kyla, Pratt, Laura R., Taxter, Alysha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.10.2025 Springer Nature B.V
Schlagworte:	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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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.
AbstractList	ObjectivesTo 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.MethodsAll 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.ResultsOf 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.ConclusionUtilizing 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. 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.OBJECTIVESTo 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.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.METHODSAll 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.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.RESULTSOf 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.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.CONCLUSIONUtilizing 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. 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. 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. 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. 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. 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.
Author	Lauer, Kendra R. Taxter, Alysha Driest, Kyla Pratt, Laura R.
Author_xml	– sequence: 1 givenname: Kendra R. orcidid: 0009-0006-0998-4503 surname: Lauer fullname: Lauer, Kendra R. organization: Division of Pediatric Rheumatology, Nationwide Children’s Hospital – sequence: 2 givenname: Kyla orcidid: 0000-0002-6514-4182 surname: Driest fullname: Driest, Kyla organization: Division of Pediatric Rheumatology, Nationwide Children’s Hospital, Department of Pediatrics, The Ohio State University – sequence: 3 givenname: Laura R. orcidid: 0000-0002-9990-9032 surname: Pratt fullname: Pratt, Laura R. organization: Division of Pediatric Rheumatology, Nationwide Children’s Hospital, Department of Pediatrics, The Ohio State University – sequence: 4 givenname: Alysha orcidid: 0000-0002-2429-116X surname: Taxter fullname: Taxter, Alysha email: Alysha.Taxter@nationwidechildrens.org, taxter.1@osu.edu organization: Division of Pediatric Rheumatology, Nationwide Children’s Hospital, Department of Pediatrics, The Ohio State University, Division of Clinical Informatics, Nationwide Children’s Hospital
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Snippet	Objectives To develop a predictive algorithm for diagnosing chronic pediatric rheumatic conditions using patient-reported, historical, and referral data in the... To develop a predictive algorithm for diagnosing chronic pediatric rheumatic conditions using patient-reported, historical, and referral data in the electronic... ObjectivesTo develop a predictive algorithm for diagnosing chronic pediatric rheumatic conditions using patient-reported, historical, and referral data in the...
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SubjectTerms	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
Title	Utilizing electronic health record pre-consultation data to create a predictive algorithm for diagnosis of chronic pediatric rheumatic conditions
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