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Non-specific low back pain (NLBP) is prevalent among patients with rheumatic conditions. Digital health applications (DiGAs) provide reimbursed, personalized home treatment for patients, promising to overcome limitations of traditional healthcare systems. However, the adoption and effectiveness of back pain-specific DiGAs in rheumatology are not well understood. This study aims to explore the experiences and perspectives of a diverse group of rheumatology stakeholders regarding the use of DiGAs for back pain management. Qualitative interviews and a focus group discussion were conducted with a wide range of stakeholders including rheumatic patients, rheumatologists, nurses and DiGA producers. The data were analysed using qualitative content analysis. The study included 15 interviews (10 rheumatic patients, 4 rheumatologists, 1 DiGA producer) and 1 focus group with mixed participants (n = 12). Most stakeholders valued the instant access to personalized and effective back pain treatment provided by DiGAs. Patients appreciated the flexibility and ease of use of DiGAs which can be used anywhere and anytime. Concerns were raised about insufficient guidance regarding correct execution of exercises, which was seen as potentially dangerous and unsettling for patients. Healthcare professionals (HCPs) highlighted barriers, such as the lack of reimbursement, time constraints, and inadequate DiGA-specific education as barriers to prescribing DiGAs. Additionally, poor patient onboarding often led to delays, increased skepticism, and premature discontinuation of therapy. Stakeholders emphasized the challenges of current care driven by a shortage of HCPs and generally supported usage of back pain DiGAs. Various barriers and solution approaches were identified to enhance the performance, usability, and implementation of DiGAs in rheumatology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The correction notice in the journal "Rheumatology International" addresses an error in the numbering of figures in an article discussing digital health applications for treating back pain in rheumatic patients. The corrected version of the article can be accessed online, and the correction has been made to accurately reflect the content. The correction was reported by multiple authors involved in the study. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

Patient-reported outcomes (PRO) represent a cornerstone in the management of patients with rheumatoid arthritis (RA). However, PRO are currently recorded mainly on paper and only during on-site appointments. Electronic PRO (ePRO) enable continuous remote monitoring and could improve shared decision-making (SDM) and implementation of a treat-to-target (T2T) approach. This study aims to investigate patient and physician experiences, perceived drawbacks and benefits of using an ePRO web-app (ABATON RA) to digitally support SDM and T2T. A qualitative study embedded in a multicenter randomized controlled trial (RCT) consisting of interviews with RA patients and physicians that were subsequently analyzed using deductive-inductive qualitative content analysis. Between August 2021 and May 2022, interviews with ten RA patients and five physicians were completed. Three key themes emerged in the analysis: (i) App user experiences; (ii) perceived drawbacks of app-supported rheumatology care; and (iii) perceived benefits of app-supported rheumatology care. Continuous ePRO collection and a high level of standardization strained some RA patients. Certain ePRO seemed outdated and were hard to understand. Patients and physicians appreciated having an improved overview of disease activity, capturing disease flares and continuous remote monitoring. Paper- and time-saving were associated with using ePRO. Physicians feared to become too focused on ePRO data, stressed the lack of ePRO monitoring reimbursement and app interoperability. For RA patients and physicians, benefits seemed to outweigh observed drawbacks of the digitally supported SDM using ePRO. The software was easy to use and could lead to a better understanding of the individual disease course, resource allocation and treatment of rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Vignette-based comparative analysis of ChatGPT and specialist treatment decisions for rheumatic patients: results of the Rheum2Guide study" published in Rheumatology International. The correction states that the first and last author names were swapped and incorrectly written. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are listed as Hannah Labinsky, Lea-Kristin Nagler, Martin Krusche, Sebastian Griewing, Peer Aries, Anja Kroiß, Patrick-Pascal Strunz, Sebastian Kuhn, Marc Schmalzing, Michael Gernert, and Johannes Knitza. [Extracted from the article]