Workforce shortage and the increasing burden of rheumatic and musculoskeletal diseases lead to extreme time constraints in rheumatology outpatient care. Digital services promise to facilitate care by ...relieving employees and unleash new capacities. This study aims to explore the perspectives of early adopter health care professionals (HCP) on digital transformation in outpatient rheumatology. In-depth qualitative interviews were conducted with rheumatology nurses and physicians in 3 German rheumatology outpatient clinics, each characterized by an advanced level of digital adaption. Qualitative data were subsequently analyzed using deductive-inductive qualitative content analysis. Interviews with 11 rheumatology nurses and 5 rheumatologists were completed. Three key themes emerged from the qualitative analysis: (i) Digital transformation of care; (ii) impact of digital transformation on health care delivery; and (iii) perceived drivers of successful digitalization. The interviews revealed that digital technologies are widely used throughout the complete patient pathway. Digitalization enables more continuity and flexibility in rheumatology care. Patient information can be electronically obtained in a standardized manner prior to planned visits, enabling an informed consultation and more time for in-depth patient discussion. Although digitalization restructures work, it can also increase the current workload. Improved accessibility for patient calls leads to more work for HCP. Important drivers of successful digital technology implementation are low-threshold and interoperable services, a medical team that is interested and educated in eHealth, and comprehensive patient information and onboarding. Digital transformation is increasingly redefining rheumatology care. While accelerating communication and workflows, improved service accessibility leads to more work for HCP.
Background
Based on given legislation the German approach to digital health applications (DiGA) allows reimbursed prescription of approved therapeutic software products since October 2020. For the ...first time, we evaluated DiGA-related acceptance, usage, and level of knowledge among members of the German Society for Rheumatology (DGRh) 1 year after its legal implementation.
Materials and methods
An anonymous cross-sectional online survey, initially designed by the health innovation hub (think tank and sparring partner of the German Federal Ministry of Health) and the German Pain Society was adapted to the field of rheumatology. The survey was promoted by DGRh newsletters and Twitter-posts. Ethical approval was obtained.
Results
In total, 75 valid response-sets. 80% reported to care ≥ 70% of their working time for patients with rheumatic diseases. Most were working in outpatient clinics/offices (54%) and older than 40 years (84%). Gender distribution was balanced (50%). 70% knew the possibility to prescribe DiGA. Most were informed of this for the first time via trade press (63%), and only 8% via the scientific/professional society. 46% expect information on DiGA from the scientific societies/medical chambers (35%) but rarely from the manufacturer (10%) and the responsible ministry (4%). Respondents would like to be informed about DiGA via continuing education events (face-to-face 76%, online 84%), trade press (86%), and manufacturers′ test-accounts (64%). Only 7% have already prescribed a DiGA, 46% planned to do so, and 47% did not intend DiGA prescriptions. Relevant aspects for prescription are provided. 86% believe that using DiGA/medical apps would at least partially be feasible and understandable to their patients. 83% thought that data collected by the patients using DiGA or other digital solutions could at least partially influence health care positively. 51% appreciated to get DiGA data directly into their patient documentation system/electronic health record (EHR) and 29% into patient-owned EHR.
Conclusion
Digital health applications awareness was high whereas prescription rate was low. Mostly, physician-desired aspects for DiGA prescriptions were proven efficacy and efficiency for physicians and patients, risk of adverse effects and health care costs were less important. Evaluation of patients’ barriers and needs is warranted. Our results might contribute to the implementation and dissemination of DiGA.
Psoriatic arthritis (PsA) is a chronic inflammatory disease that develops in up to 30% of patients with psoriasis. In the vast majority of cases, cutaneous symptoms precede musculoskeletal ...complaints. Progression from psoriasis to PsA is characterized by subclinical synovio-entheseal inflammation and often non-specific musculoskeletal symptoms that are frequently unreported or overlooked. With the development of increasingly effective therapies and a broad drug armamentarium, prevention of arthritis development through careful clinical monitoring has become priority. Identifying high-risk psoriasis patients before PsA onset would ensure early diagnosis, increased treatment efficacy, and ultimately better outcomes; ideally, PsA development could even be averted. However, the current model of care for PsA offers only limited possibilities of early intervention. This is attributable to the large pool of patients to be monitored and the limited resources of the health care system in comparison. The use of digital technologies for health (eHealth) could help close this gap in care by enabling faster, more targeted and more streamlined access to rheumatological care for patients with psoriasis. eHealth solutions particularly include telemedicine, mobile technologies, and symptom checkers. Telemedicine enables rheumatological visits and consultations at a distance while mobile technologies can improve monitoring by allowing patients to self-report symptoms and disease-related parameters continuously. Symptom checkers have the potential to direct patients to medical attention at an earlier point of their disease and therefore minimizing diagnostic delay. Overall, these interventions could lead to earlier diagnoses of arthritis, improved monitoring, and better disease control while simultaneously increasing the capacity of referral centers.
ObjectiveTo perform a systematic literature review (SLR) on different outcomes of remote care compared with face-to-face (F2F) care, its implementation into clinical practice and to identify drivers ...and barriers in order to inform a task force formulating the EULAR Points to Consider for remote care in rheumatic and musculoskeletal diseases (RMDs).MethodsA search strategy was developed and run in Medline (PubMed), Embase and Cochrane Library. Two reviewers independently performed standardised data extraction, synthesis and risk of bias (RoB) assessment.ResultsA total of 2240 references were identified. Forty-seven of them fulfilled the inclusion criteria. Remote monitoring (n=35) was most frequently studied, with telephone/video calls being the most common mode of delivery (n=30). Of the 34 studies investigating outcomes of remote care, the majority addressed efficacy and user perception; 34% and 21% of them, respectively, reported a superiority of remote care as compared with F2F care. Time and cost savings were reported as major benefits, technical aspects as major drawback in the 13 studies that investigated drivers and barriers of remote care. No study addressed remote care implementation. The main limitation of the studies identified was the heterogeneity of outcomes and methods, as well as a substantial RoB (50% of studies with high RoB).ConclusionsRemote care leads to similar or better results compared with F2F treatment concerning efficacy, safety, adherence and user perception outcomes, with the limitation of heterogeneity and considerable RoB of the available studies.
Background Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease ...progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. Objective This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score. Methods Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest. Results Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve AUC of 0.9078 and logarithmic loss LogLoss of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores. Conclusions The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.
ObjectivesTo evaluate the feasibility, accuracy, usability and acceptability of two upper arm self-sampling devices for measurement of autoantibodies and C reactive protein (CRP) levels in patients ...with immune-mediated rheumatic diseases (IMRDs).Methods70 consecutive patients with IMRD with previously documented autoantibodies were assigned to supervised and unsupervised self-collection of capillary blood with the Tasso+ or TAP II device. Interchangeability of 17 biomarkers with standard venesection was assessed by: concordance, correlation, paired sample hypothesis testing and Bland-Altman plots. Patients completed an evaluation questionnaire, including the System Usability Scale (SUS) and Net Promoter Score (NPS).ResultsWhile 80.0% and 77.0% were able to safely and successfully collect capillary blood using the Tasso+ and TAP II within the first attempt, 69 of 70 (98.6%) patients were successful in collecting capillary blood within two attempts. Concordance between venous and capillary samples was high; 94.7% and 99.5% for positive and negative samples, respectively. For connective tissue disease screen, anti-Ro52 and anti-proteinase 3 autoantibody levels, no significant differences were observed. Self-sampling was less painful than standard venesection for the majority of patients (Tasso+: 71%; TAP II: 63%). Both devices were well accepted (NPS; both: +28%), usability was perceived as excellent (SUS; Tasso+: 88.6 of 100; TAP II: 86.0 of 100) and 48.6 %/62.9% of patients would prefer to use the Tasso+/TAP II, respectively, instead of a traditional venous blood collection.ConclusionsRemote self-collection of capillary blood using upper arm-based devices for autoantibody and CRP analysis in patients with autoimmune rheumatic diseases is feasible, accurate and well accepted among patients.Trial registration numberWHO International Clinical Trials Registry (DRKS00024925).
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