Reply to the comment on “Accuracy and usability of a diagnostic decision support system in the diagnosis of three representative rheumatic diseases: a randomized controlled trial among medical students”

With great interest, we read the comment by Gilbert and Wicks on our recent publication [1] testing the accuracy and usability of Ada’s symptom checker among medical students

Apps und ihre Anwendungsgebiete in der Rheumatologie

Zusammenfassung Mit der steigenden Verwendung von Smartphones einhergehend, nimmt auch die Nutzung von mobilen Applikationen (Apps) rapide zu. Im medizinischen Kontext könnten chronisch kranke Patienten von dem Einsatz dauerhaft profitieren. Verstärkt wird diese Entwicklung durch das Digitale-Versorgung-Gesetz (DVG), wonach Patienten ab Q4/2020 einen Rechtsanspruch auf bestimmte Apps, sog. digitale Gesundheitsanwendungen (DiGAs), haben, die von den gesetzlichen Krankenkassen erstattet werden. Besonders im Bereich der Rheumatologie bieten sich für das Management chronischer Erkrankungen und ihrer Komorbiditäten verschiedene Anknüpfungspunkte. Nicht nur unter rheumatologischen Patienten ist das Interesse an App-Angeboten groß, sondern auch unter deutschen Rheumatologen zeigt sich eine steigende Bereitschaft, Apps im Berufsalltag anzuwenden und Patienten zu empfehlen. Dieser Artikel will einen Überblick über die Entwicklung der App-Landschaft in der deutschsprachigen Rheumatologie vermitteln

Telemedizin in der Rheumatologie

Zusammenfassung Der Ausbruch der COVID-19-Pandemie geht mit tief greifenden Einschnitten im Alltag und im Berufsleben einher – sowohl gesamtgesellschaftlich als auch speziell im Gesundheitswesen. Im Fokus der Pandemieeindämmung haben sich vielerorts rheumatologische Routineabläufe verändert. Um den entsprechenden Infektionsschutz der Patienten und des medizinischen Personals gewährleisten zu können, wurde hier verstärkt Telemedizin (insbesondere Telefon- und Videosprechstunde) eingesetzt. Weiterhin stehen durch die Digitale-Gesundheitsanwendungen-Verordnung (DiGAV) voraussichtlich in den kommenden Monaten neue, abrechnungsfähige telemedizinische Anwendungsmöglichkeiten wie Apps und Wearables zur Verfügung. Der Artikel soll einen Überblick über telemedizinische Versorgungsmöglichkeiten in der Rheumatologie (mit besonderem Fokus auf die Videosprechstunde) geben. Weiterhin wird Bezug auf die vorhandene Evidenzlage sowie Chancen und Limitation der Telemedizin im Fachgebiet genommen

Positionspapier der Kommission Digitale Rheumatologie der Deutschen Gesellschaft für Rheumatologie e. V.: Aufgaben, Ziele und Perspektiven für eine moderne Rheumatologie

Zusammenfassung Die Digitalisierung im Gesundheitswesen ist für die Rheumatologie eine ebenso große Herausforderung wie für andere medizinische Fachgebiete. Die Deutsche Gesellschaft für Rheumatologie e. V. (DGRh) will diesen Prozess aktiv gestalten und davon profitieren. Mit der Gründung der Kommission Digitale Rheumatologie hat sie ein Gremium geschaffen, das die damit verbundenen Aufgaben bearbeitet, die DGRh zu Fragestellungen berät und sich positioniert. Für die DGRh berührt dies verschiedenste Bereiche der Digitalisierung in Medizin und Rheumatologie. Dieses Positionspapier legt die aktuell von der Kommission bearbeiteten Themengebiete, Entwicklungen und identifizierten Aufgaben dar

Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study

Background: Mobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases. Objective: The aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases. Methods: Between December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences. Results: Of the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information. Conclusions: Patients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology

Machine learning-based improvement of an online rheumatology referral and triage system

IntroductionRheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy.Materials and methodsData from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP).ResultsA complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification.ConclusionIn summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage

Toward earlier diagnosis using combined eHealth tools in rheumatology: the joint pain assessment scoring tool (JPAST) project

Outcomes of patients with inflammatory rheumatic diseases have significantly improved over the last three decades, mainly due to therapeutic innovations, more timely treatment, and a recognition of the need to monitor response to treatment and to titrate treatments accordingly. Diagnostic delay remains a major challenge for all stakeholders. The combination of electronic health (eHealth) and serologic and genetic markers holds great promise to improve the current management of patients with inflammatory rheumatic diseases by speeding up access to appropriate care. The Joint Pain Assessment Scoring Tool (JPAST) project, funded by the European Union (EU) European Institute of Innovation and Technology (EIT) Health program, is a unique European project aiming to enable and accelerate personalized precision medicine for early treatment in rheumatology, ultimately also enabling prevention. The aim of the project is to facilitate these goals while at the same time, reducing cost for society and patients.Pathophysiology and treatment of rheumatic disease

Long-term safety of COVID vaccination in individuals with idiopathic inflammatory myopathies: results from the COVAD study

Limited evidence on long-term COVID-19 vaccine safety in patients with idiopathic inflammatory myopathies (IIMs) continues to contribute to vaccine hesitancy. We studied delayed-onset vaccine adverse events (AEs) in patients with IIMs, other systemic autoimmune and inflammatory disorders (SAIDs), and healthy controls (HCs), using data from the second COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. A validated self-reporting e-survey was circulated by the COVAD study group (157 collaborators, 106 countries) from Feb-June 2022. We collected data on demographics, comorbidities, IIM/SAID details, COVID-19 history, and vaccination details. Delayed-onset (> 7 day) AEs were analyzed using regression models. A total of 15165 respondents undertook the survey, of whom 8759 responses from vaccinated individuals [median age 46 (35-58) years, 74.4% females, 45.4% Caucasians] were analyzed. Of these, 1390 (15.9%) had IIMs, 50.6% other SAIDs, and 33.5% HCs. Among IIMs, 16.3% and 10.2% patients reported minor and major AEs, respectively, and 0.72% (n = 10) required hospitalization. Notably patients with IIMs experienced fewer minor AEs than other SAIDs, though rashes were expectedly more than HCs [OR 4.0; 95% CI 2.2-7.0, p < 0.001]. IIM patients with active disease, overlap myositis, autoimmune comorbidities, and ChadOx1 nCOV-19 (Oxford/AstraZeneca) recipients reported AEs more often, while those with inclusion body myositis, and BNT162b2 (Pfizer) recipients reported fewer AEs. Vaccination is reassuringly safe in individuals with IIMs, with AEs, hospitalizations comparable to SAIDs, and largely limited to those with autoimmune multimorbidity and active disease. These observations may inform guidelines to identify high-risk patients warranting close monitoring in the post-vaccination period