Mobile health applications and digital therapeutics (DTx) aim to improve current patient care. Real-world data on DTx are, however, scarce. The aim of this study was to evaluate the adherence, acceptance, and efficacy of DTx in a clinical routine rheumatology setting. We conducted a prospective observational cohort study assessing the use, adherence, acceptance, and efficacy of the DTx DiGA (Digitale Gesundheitsanwendungen) by survey over 12 weeks. Patients included had to have a rheumatic disease and had been prescribed a DiGA. Acceptance was assessed using the Net promoter score (NPS). 48 patients were prescribed DiGA. Of these, 39/48 (81%) completed the follow-up survey. 21/39 (54%) patients downloaded the DTx and 20/39 (51%) used the DTx at least once. 9/39 (23%) of patients stopped quickly afterward and 5/39 (13%) reported having completed the whole DTx program. Lack of time and commitment were reported as the main reasons for non-use. Overall acceptance of DiGA was high (Net promoter score (NPS) mean (SD) 7.8/10 (2.3)). While the majority of patients (60%) reported no improvement, one subgroup of patients (7/20, 35%) who regularly used an exercise-based DTx for back pain reported symptom improvement. Acceptance of DTx in patients with rheumatic diseases is high, however onboarding to DTx use and adherence to DTx is still challenging in patients with rheumatic diseases. In a subgroup of patients with back pain, however, the use of an exercise-based DTx led to symptom improvement.

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.

Background: Patients with axial spondyloarthritis (axSpA) benefit from regular home-based exercise (HbE). In spite of recommendations, a relevant proportion of German axSpA patients does not adhere to recommended HbE practices. To enhance HbE care, we developed the novel digital therapeutic (DTx) “Axia” compliant with the European medical device regulation (MDR). Axia offers a modern app-based HbE solution with patient educative content and further integrated features. Objective: We aimed to assess Axia’s efficacy, attractiveness, and functionality through a survey among axSpA-patients involved in the first user tests. Methods: A mixed-method online questionnaire with 38 items was administered to 37 axSpA volunteers after using Axia. Numeric rating scales (NRS) and likelihood scales were primarily used. Results: HbE frequency significantly increased from a median of 1 day/week to 6 days/week (p < 0.001) by using Axia. Existing HbE practitioners also increased their frequency (median of 4 days/week before, 6 days/week with Axia, p < 0.05). Axia received a median rating of 5 out of 5 stars. On NRS scales, Axia scored a median of 9 for intuitiveness and design, and a median of 8 for entertainment. 64.9% reported improved range of motion, 43.2% reported reduced pain, and 93.6% enhanced disease-specific knowledge. All users recommended Axia to other patients. Conclusion: Axia increases axSpA patients HbE frequency, possibly due to its good intuitiveness and design, leading to reduction in pain and subjective improvement of range of motion. This warrants further investigation in large randomized controlled interventional trials to establish its efficacy conclusively and patients adherence to HbE.

Background: The complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support. Objective: To compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB). Design/methods: Fictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans’ safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale. Results: 20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB’s treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4’s plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4’s first-line treatment plans. Rheumatologists’ plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed. Conclusion: GPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions.

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

Background: The goal of the study was to investigate patients’ with systemic rheumatic diseases and healthcare professionals’ experiences and preferences regarding self-sampling of capillary blood in rheumatology care. Methods: Patients performed a supervised and consecutive unsupervised capillary blood self-collection using an upper arm based device. Subsequently, patients (n = 15) and their attending health care professionals (n = 5) participated in an explorative, qualitative study using problem-centered, telephone interviews. Interview data were analyzed using structured qualitative content analysis. Results: Interviewed patients reported easy application and high usability. Patients and health care professionals alike reported time and cost savings, increased independence and flexibility, improved monitoring and reduction of risk of infection during Covid-19 as benefits. Reported drawbacks include limited blood volume, limited usability in case of functional restrictions, and environmental concerns. Older, immobile patients with long journeys to traditional blood collection sites and young patients with little time to spare for traditional blood collection appointments could be user groups, likely to benefit from self-sampling services. Conclusions: At-home blood self-sampling could effectively complement current rheumatology telehealth care. Appropriateness and value of this service needs to be carefully discussed with patients on an individual basis. Trial Registration: WHO International Clinical Trials Registry: DRKS00024925. Registered on 15/04/2021.