Journal of Medical Internet Research · 2024 · 19 citations · 21 references
ComputingClinical SciencesHealth InformaticsClinical Decision SupportClinical InformaticsRheumatologyClinical Decision Support SystemsDecision Support In HealthcareDiagnostic Decision SupportMobile Clinical Decision SupportRheumatoid ArthritisSymptom-based ScreeningArtificial Intelligence-assisted DiagnosisDiagnostic Accuracy StudiesDiagnostic AlgorithmsDiagnostic Testing AccuracyPrimary Outcome AnalysisSymptom Checker SystemsSymptom CheckersSymptom-based Disease Detection
BACKGROUND: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently. OBJECTIVE: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs. METHODS: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists. RESULTS: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport's disease suggestion and Ada's top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada's D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada's diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs. CONCLUSIONS: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs. TRIAL REGISTRATION: German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.
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Interrater reliability: the kappa statistic
Marry L. McHugh · Biochemia Medica · 2012 · 18.6K citations · Full text
Interrater reliability: the kappa statistic.
Mary L. McHugh · PubMed · 2012 · 9.2K citations · Full text
Evaluation of symptom checkers for self diagnosis and triage: audit study
Hannah L. Semigran, Jeffrey A. Linder, Courtney A. Gidengil et al. · BMJ · 2015 · 509 citations · Full text
Jan Sørensen, Merete Lund Hetland · Annals of the Rheumatic Diseases · 2014 · 183 citations · Full text