Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS—Rheuma Care Manager (RCM)—including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients’ flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence.

Early and effective discrimination (triage) of patients with inflammatory rheumatic diseases (IRD) and other diseases (non-IRD) is essential for successful treatment and preventing damage. The aim of this study was to investigate diagnostic delays and pre-diagnosis treatment in patients newly presenting to rheumatology outpatient clinics. A total of 600 patients newly presenting to one university hospital and two non-academic centers were included. Time from onset of symptoms to rheumatology consultation “total delay” as well as medical treatment before consultation were recorded. Median time from symptom onset to rheumatologist appointment (total delay) was 30 weeks. Median time to online search, first physician appointment request and first physician appointment was 2, 4 and 5 weeks, respectively. Total delay was significantly shorter for IRD patients compared to non-IRD patients, 26 vs 35 weeks (p = 0.007). Only 17.7% of all patients and 22.9% of IRD patients had a delay of less than 12 weeks. Total delay was significantly lower in patients seen in non-academic centers compared to the university center, 20 vs 50 weeks (p < 0.0001). 32.2% of IRD patients received medical treatment that eased their symptoms prior to the rheumatology appointment. These findings highlight the persistent diagnostic delays in rheumatology; however, they also suggest that current triage strategies effectively lead to earlier appointments for IRD patients. Improvement of triage methods and pre-diagnosis treatment could decrease overall burden of disease in IRD patients.

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

Objective: To investigate the prevalence of depressive symptoms and its association with clinical and psychological factors in patients with rheumatoid arthritis (RA) in Germany and in Brazil. Method: A convenience sample of 267 RA patients, 176 from Germany (age 62.4 ± 12.3 years) and 91 from Brazil (age 56.3 ± 12.6 years), was used in this cross-sectional study. The following questionnaires were used: Beck Depression Inventory (BDI), painDETECT test, Perceived Stress Questionnaire, fatigue questionnaire (FACIT), Health Assessment Questionnaire Disability Index (HAQ-DI), and the SF–36 questionnaires (Short-Form 36 Health Survey). Disease activity score (DAS 28-CRP) and visual analogue scale (VAS) for pain were also evaluated. Statistical analysis is based on comparison of means and proportions. Statistical significance for non-normal data was evaluated by non-parametrical tests. Results: Depressive symptoms were more prevalent in the Brazilian sample (44% vs 22.9%, p = 0.025). Compared to German patients, the Brazilian ones also experienced more pain (current pain status on VAS: 4.67 ± 3.4 vs 3.67 ± 2.31 respectively, p < 0.01), were physically more limited (1.89 ± 1.85 vs 1.01 ± 0.75, p = 0.012), and had higher C-reactive protein levels (7.78 ± 18.3 vs 5.82 ± 10.45, p = 0.028). Despite receiving a more intensive treatment, German patients presented similar disease activity when compared to Brazilian patients (DAS28-CRP: Brazil 3.4 ± 1.5 vs Germany 3.3 ± 1.3, p = 0.307). Conclusion: Depressive symptoms are frequent in RA patients from different countries and interact with psychological disorders and the experience of pain. They contribute negatively to their well-being suggesting the need for psychoeducational strategies. Key Points• New psychoeducational strategies for RA management.• Higher inflammation marker in rheumatoid arthritis patients is associated with depression.• Medical treatment in RA influences depressive symptoms.• Depressive symptoms are dependent on population group.• High disease activity is related to depression.

While artificial intelligence (AI) is gaining attention in rheumatology, little is known about patient perspectives. This study addresses this gap by examining patients’ experiences and attitudes toward AI. A nationwide, cross-sectional, web-based survey was conducted between March and May 2025 among adult patients with rheumatic diseases in Germany. Data were analyzed descriptively and with cluster analysis. A total of 778 patients completed the survey (70.4% female, mean age 51.3 years). The most common diagnosis was rheumatoid arthritis (31.7%). While 26.8% reported current AI use for health-related purposes, 57.8% expressed interest in using it. Patients were particularly interested in AI-based symptom checkers (64.3%), therapy recommendations (50.6%), and chatbots for medical inquiries (44.5%). 57.6% of patients indicated that they would welcome their rheumatologists using AI-based clinical suppport. The most frequently cited benefits of AI included improved information access (63.5%) and faster diagnosis (57.7%), while concerns centered on faulty AI (74.3%) and reduced human interaction (59.6%). Cluster analysis identified three distinct patient profiles: ‘AI-savvy’ (41.4%), ‘AI-pragmatic’ (44.8%), and ‘AI-skeptical’ (13.8%). Cluster membership was significantly associated with age and education, with younger patients more often belonging to the ‘AI-savvy’ group. Patients with rheumatic diseases showed substantial interest in AI-supported care, although actual use in medical contexts remained limited. Age and education differences highlight the need for tailored implementation strategies to ensure equitable and patient-centered adoption of AI in rheumatology.

Background: The exponential growth of telehealth is revolutionizing health care delivery, but its evaluation has not matched the pace of its uptake. Various forms of assessment, from single-item to more extensive questionnaires, have been used to assess telehealth and digital therapeutics and their usability. The most frequently used questionnaire is the “Telehealth Usability Questionnaire” (TUQ). The use of the TUQ is limited by its restricted availability in languages other than English and its feasibility. Objective: The aims of this study were to create a translated German TUQ version and to derive a short questionnaire for patients—“Telehealth Usability and Perceived Usefulness Short Questionnaire for patients” (TUUSQ). Methods: As a first step, the original 21-item TUQ was forward and back-translated twice. In the second step, 13 TUQ items were selected for their suitability for the general evaluation of telehealth on the basis of expert opinion. These 13 items were surveyed between July 2022 and September 2023 in 4 studies with patients and family members of palliative care, as well as patients with chronic autoimmune diseases, evaluating 13 health care apps, including digital therapeutics and a telehealth system (n1=128, n2=220, n3=30, and n4=12). Psychometric exploratory factor analysis was conducted. Results: The analysis revealed that a parsimonious factor structure with 2 factors (“perceived usefulness in health care” and “usability”) is sufficient to describe the patient’s perception. Consequently, the questionnaire could be shortened to 6 items without compromising its informativeness. Conclusions: We provide a linguistically precise German version of the TUQ for assessing the usability and perceived usefulness of telehealth. Beyond that, we supply a highly feasible shortened version that is versatile for general use in telehealth, mobile health, and digital therapeutics, which distinguishes between the 2 factors “perceived usefulness in health care” and “usability” in patients. Trial Registration: German Clinical Trials Register DRKS00030546; https://drks.de/search/de/trial/DRKS00030546