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BackgroundInflammatory arthritides (IA) such as rheumatoid arthritis or psoriatic arthritis are disorders that can be difficult to comprehend for health professionals and students in terms of the heterogeneity of clinical symptoms and pathologies. New didactic approaches using innovative technologies such as virtual reality (VR) apps could be helpful to demonstrate disease manifestations as well as joint pathologies in a more comprehensive manner. However, the potential of using a VR education concept in IA has not yet been evaluated. ObjectiveWe evaluated the feasibility of a VR app to educate health care professionals and medical students about IA. MethodsWe developed a VR app using data from IA patients as well as 2D and 3D-visualized pathological joints from X-ray and computed tomography–generated images. This VR app (Rheumality) allows the user to interact with representative arthritic joint and bone pathologies of patients with IA. In a consensus meeting, an online questionnaire was designed to collect basic demographic data (age, sex); profession of the participants; and their feedback on the general impression, knowledge gain, and potential areas of application of the VR app. The VR app was subsequently tested and evaluated by health care professionals (physicians, researchers, and other professionals) and medical students at predefined events (two annual rheumatology conferences and academic teaching seminars at two sites in Germany). To explore associations between categorical variables, the χ2 or Fisher test was used as appropriate. Two-sided P values ≤.05 were regarded as significant. ResultsA total of 125 individuals participated in this study. Among them, 56% of the participants identified as female, 43% identified as male, and 1% identified as nonbinary; 59% of the participants were 18-30 years of age, 18% were 31-40 years old, 10% were 41-50 years old, 8% were 51-60 years old, and 5% were 61-70 years old. The participants (N=125) rated the VR app as excellent, with a mean rating of 9.0 (SD 1.2) out of 10, and many participants would recommend use of the app, with a mean recommendation score of 3.2 (SD 1.1) out of 4. A large majority (120/125, 96.0%) stated that the presentation of pathological bone formation improves understanding of the disease. We did not find any association between participant characteristics and evaluation of the VR experience or recommendation scores. ConclusionsThe data show that IA-targeting innovative teaching approaches based on VR technology are feasible.

Real-world data are crucial to continuously improve the management of patients with rheumatic and musculoskeletal diseases (RMDs). The German RheumaDatenRhePort (RHADAR) registry encompasses a network of rheumatologists and researchers in Germany providing pseudonymized real-world patient data and allowing timely and continuous improvement in the care of RMD patients. The RHADAR modules allow automated anamnesis and adaptive coordination of appointments regarding individual urgency levels. Further modules focus on the collection and integration of electronic patient-reported outcomes in between consultations. The digital RHADAR modules ultimately allow a patient-centered adaptive approach to integrated medical care starting as early as possible in the disease course. Such a closed-loop system consisting of various modules along the whole patient pathway enables comprehensive and timely patient management in an unprecedented manner.

BackgroundThe worldwide burden of musculoskeletal diseases is increasing. The number of newly registered rheumatologists has stagnated. Primary care, which takes up a key role in early detection of rheumatic disease, is working at full capacity. COVID-19 and its containment impede rheumatological treatment. Telemedicine in rheumatology (telerheumatology) could support rheumatologists and general practitioners. ObjectiveThe goal of this study was to investigate acceptance and preferences related to the use of telerheumatology care among German rheumatologists and general practitioners. MethodsA nationwide, cross-sectional, self-completed, paper-based survey on telerheumatology care was conducted among outpatient rheumatologists and general practitioners during the pre-COVID-19 period. ResultsA total of 73.3% (349/476) of survey participants rated their knowledge of telemedicine as unsatisfactory, poor, or very poor. The majority of survey participants (358/480, 74.6%) answered that they do not currently use telemedicine, although 62.3% (291/467) would like to. Barriers to the implementation of telemedicine include the purchase of technology equipment (182/292, 62.3%), administration (181/292, 62.0%), and poor reimbursement (156/292, 53.4%). A total of 69.6% (117/168) of the surveyed physicians reckoned that telemedicine could be used in rheumatology. Surveyed physicians would prefer to use telemedicine to communicate directly with other physicians (370/455, 81.3%) than to communicate with patients (213/455, 46.8%). Among treatment phases, 64.4% (291/452) of participants would choose to use telemedicine during follow-up. Half of the participants would choose telecounseling as a specific approach to improve rheumatology care (91/170, 53.5%). ConclusionsBefore COVID-19 appeared, our results indicated generally low use but high acceptance of the implementation of telerheumatology among physicians. Participants indicated that the lack of a structural framework was a barrier to the effective implementation of telerheumatology. Training courses should be introduced to address the limited knowledge on the part of physicians in the use of telemedicine. More research into telerheumatology is required. This includes large-scale randomized controlled trials, economic analyses, and the exploration of user preferences.

BackgroundFinancial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries. ObjectiveThe aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records. MethodsTwo electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation. ResultsFor the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97). ConclusionsWe demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

BackgroundThe use of patient-reported outcomes (PROs) allows for patient-centered, measurable, and transparent care. Electronic PROs (ePROs) have many benefits and hold great potential to improve current usage of PROs, yet limited evidence exists regarding their acceptance, usage, and barriers among rheumatologists. ObjectiveThis study aims to evaluate the current level of acceptance, usage, and barriers among German rheumatologists regarding the use of ePROs. The importance of different ePRO features for rheumatologists was investigated. Additionally, the most frequently used PROs for patients with rheumatoid arthritis (RA) were identified. MethodsData were collected via an online survey consisting of 18 questions. The survey was completed by members of the Working Group Young Rheumatology of the German Society for Rheumatology (Arbeitsgemeinschaft Junge Rheumatologie der Deutschen Gesellschaft für Rheumatologie [DGRh]) at the 2019 annual DGRh conference. Only members currently working in clinical adult rheumatology were eligible to complete the survey. ResultsA total of 119 rheumatologists completed the survey, of which 107 (89.9%) reported collecting PROs in routine practice and 28 (25.5%) already used ePROs. Additionally, 44% (43/97) were planning to switch to ePROs in the near future. The most commonly cited reason for not switching was the unawareness of suitable software solutions. Respondents were asked to rate the features of ePROs on a scale of 0 to 100 (0=unimportant, 100=important). The most important features were automatic score calculation and display (mean 77.50) and simple data transfer to medical reports (mean 76.90). When asked about PROs in RA, the respondents listed pain, morning stiffness, and patient global assessment as the most frequently used PROs. ConclusionsThe potential of ePROs is widely seen and there is great interest in them. Despite this, only a minority of physicians use ePROs, and the main reason for not implementing them was cited as the unawareness of suitable software solutions. Developers, patients, and rheumatologists should work closely together to help realize the full potential of ePROs and ensure a seamless integration into clinical practice.

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.

BackgroundChronic rheumatic diseases need long-term treatment and professional supervision. Mobile apps promise to improve the lives of patients and physicians. In routine practice, however, rheumatology apps are largely unknown and little is known about their quality and safety. ObjectiveThe aim of this study was to provide an overview of mobile rheumatology apps currently available in German app stores, evaluate app quality using the Mobile Application Rating Scale (MARS), and compile brief, ready-to-use descriptions for patients and rheumatologists. MethodsThe German App Store and Google Play store were systematically searched to identify German rheumatology mobile apps for patient and physician use. MARS was used to independently assess app quality by 8 physicians, 4 using Android and 4 using iOS smartphones. Apps were randomly assigned so that 4 apps were rated by all raters and the remaining apps were rated by two Android and two iOS users. Furthermore, brief app descriptions including app developers, app categories, and features were compiled to inform potential users and developers. ResultsIn total, 128 and 63 apps were identified in the German Google Play and App Store, respectively. After removing duplicates and only including apps that were available in both stores, 28 apps remained. Sixteen apps met the inclusion criteria, which were (1) German language, (2) availability in both app stores, (3) targeting patients or physicians as users, and (4) clearly including rheumatology or rheumatic diseases as subject matter. Exclusion criteria were (1) congress apps and (2) company apps with advertisements. Nine apps addressed patients and 7 apps addressed physicians. No clinical studies to support the effectiveness and safety of apps could be found. Pharmaceutical companies were the main developers of two apps. Rheuma Auszeit was the only app mainly developed by a patient organization. This app had the highest overall MARS score (4.19/5). Three out of 9 patient apps featured validated questionnaires. The median overall MARS score was 3.85/5, ranging from 2.81/5 to 4.19/5. One patient-targeted and one physician-targeted app had MARS scores >4/5. No significant rater gender or platform (iOS/Android) differences could be observed. The overall correlation between app store ratings and MARS scores was low and inconsistent between platforms. ConclusionsTo our knowledge, this is the first study that systematically identified and evaluated mobile apps in rheumatology for patients and physicians available in German app stores. We found a lack of supporting clinical studies, use of validated questionnaires, and involvement of academic developers. Overall app quality was heterogeneous. To create high-quality apps, closer cooperation led by patients and physicians is vital.