Background: Expenditures for physiotherapy and extended outpatient physiotherapy (EAP) are increasing within Germany’s statutory accident insurance system (Berufsgenossenschaften), placing growing pressure on rehabilitation capacity and timely access to care. Digital health applications (DiGAs) are reimbursable nationwide and represent a novel component of routine rehabilitation pathways. However, their real-world system-level and economic effects in occupational rehabilitation remain insufficiently understood. Objective: This study aimed to evaluate how the integration of DiGAs into occupational rehabilitation pathways may influence costs, service capacity, and waiting times within routine care delivered by 5 German statutory accident insurance funds that cover 25.9 million insured individuals. Methods: Aggregated administrative data from 5 Berufsgenossenschaften (fiscal years 2023‐2024) were analyzed using a multilevel simulation framework combining (1) probabilistic cost-consequence modeling with Monte Carlo simulation (10,000 iterations), (2) an adherence-based adoption funnel distinguishing long-term engaged users (15%) and short-term users (85%) based on German claims data, and (3) a calibrated M/M/1 queuing model validated through discrete event simulation to estimate the effects on waiting times and system capacity. Primary outcomes included net financial impact, break-even thresholds, and changes in access-related performance metrics. Results: Combined physiotherapy and EAP expenditures reached €404 million (€1=US $1.18) in 2024, increasing by 10.1% year-over-year. The primary simulation (N=10,000 iterations) indicated mean annual net savings of €18.4 million (median €17.9 million) with a 90.7% probability of cost savings (95% uncertainty range: net cost of €8 million to net savings of €47.7 million). After incorporating adherence dynamics, the projected mean net savings were €16.2 million (95% CI €5-€29.8 million), corresponding to a 100% probability of positive financial impact within the modeled parameter space. Cost neutrality was maintained for DiGA prices up to €617.8 per prescription, nearly 40% above the base-case assumption of €450, indicating substantial economic robustness. Queuing analyses demonstrated that modest reductions in therapeutic demand decreased mean waiting times from 17.3 to 12.8 days (−26%), equivalent to approximately 120,000 cumulative patient waiting days saved annually across 26,705 EAP patients. The validation of discrete event simulation confirmed the magnitude and direction of analytic estimates. Conclusions: Under conservative assumptions, integrating digital therapeutics into occupational rehabilitation pathways is likely to generate both economic benefits and substantial system-level capacity gains. The break-even threshold of €617.80 per prescription provides a wide margin for pricing policy. Beyond cost effects, DiGAs may function as scalable capacity tools that alleviate systemic bottlenecks and improve timely access to rehabilitation services in capacity-constrained systems.

Background: YouTube is increasingly used for healthcasting, the sharing of evidence-based dietary and lifestyle interventions by domain experts. In the metabolic health domain, channels focused on therapeutic carbohydrate restriction have accumulated audiences of millions. A distinctive feature is the comment section, where viewers share first-person accounts of health changes, constituting a unique source of real-world outcome data at scale. However, extracting structured health information from unstructured comments presents computational challenges. Objective: This observational, cross-sectional study aims to develop and validate a precision-optimized computational framework for extracting self-reported health outcomes from healthcasting YouTube comments and to characterize the prevalence, distribution across health aspects, and channel-level variation of reported outcomes across a large-scale metabolic health corpus. Methods: This study analyzed 43,111 unique YouTube comments from 110 videos across 11 therapeutic carbohydrate restriction-focused healthcasting channels (37,458 unique authors; data span November 2013 to January 2026; collected via YouTube data application programming interface version 3). The methodology comprised 3 construction phases and 5 validation studies. The construction phases were (1) exploratory corpus characterization, (2) iterative development of a 35-aspect hierarchical health outcome ontology, and (3) precision-optimized rule-based classification, validated through precision validation (stratified sample of n=500), recall estimation (n=510), external validation on 5 held-out channels (n=12,653 comments), large language model–assisted interrater reliability assessment, and transformer baseline comparison against Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (ROBERTa) classifiers. A supplementary aspect–based sentiment analysis contextualized the positive-only design. Results: The framework identified 1790 positive health outcome reports (1790/43,111, 4.15% prevalence), achieving 97.6% (488/500) precision (95% CI 95.7%-98.6%) and estimated 56.2% recall (95% CI 43.4%-67.9%). The reports described 6674 positive outcomes, distributed across 35 health aspects and 18 named disease conditions extending beyond weight loss: pain and inflammation reduction (1137/6674, 17%), type 2 diabetes improvement (977/6674, 14.6%), skin health (784/6674, 11.8%), and psychological well-being (731/6674, 11%). Over half (3355/6674, 50.3%) spanned multiple research objectives. Significant channel-level variation was observed (χ²₁₀=927.5; P<.001), with positive outcome rates ranging from 1.32% to 10.40% (odds ratio 8.68, 95% CI 7.10-10.61). Transformer baselines achieved higher recall but lower precision, confirming their advantage for high-confidence corpus generation. A supplementary aspect-based sentiment analysis indicated a positive-to-negative ratio of approximately 4.6:1 (n=1003), with negative experiences (59/495, 11.9%) predominantly involving gastrointestinal and cardiovascular concerns. Conclusions: This study presents, to our knowledge, the first validated, rule-based framework for extracting self-reported metabolic health outcomes from healthcasting YouTube comments at corpus scale. Unlike existing recall-oriented social media health classifiers, the precision-optimized design achieves the confidence threshold required for outcomes research without manual review. These findings demonstrate that expert-led health content comment sections constitute a scalable, complementary data source for monitoring real-world engagement with dietary interventions, with implications for public health surveillance, platform design, and health communication research.

Background: Self-sovereign identity (SSI) provides a decentralized approach to digital identity management, enabling individuals to control their personal data without reliance on centralized authorities. Blockchain technology offers a tamper-resistant and distributed infrastructure that can support secure and verifiable identity systems. In health care, where identity fragmentation, privacy risks, and interoperability challenges persist, blockchain-enabled SSI (BC-SSI) has been proposed as a potential solution. However, existing research remains heterogeneous, with varying levels of technical maturity and limited evidence of real-world deployment. Objective: This study conducts a scoping review to systematically map BC-SSI applications in health care and to analyze their application domains, development stages, study aims, targeted challenges, and technological infrastructures. In addition, this study aims to identify structural gaps in current research and assess the readiness of BC-SSI systems for clinical deployment. Methods: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methodology. A comprehensive literature search conducted between September 2024 and August 2025 identified 37 peer-reviewed studies that met predefined inclusion criteria. Data were extracted and synthesized using descriptive and thematic analyses across application areas, system maturity, technological components, and reported challenges. Results: The findings indicate that BC-SSI research in health care remains at an early stage of maturity, with most studies proposing conceptual models or prototype implementations and limited real-world validation. Applications predominantly focus on identity verification, credential management, and privacy-preserving data exchange across domains such as electronic health records, mobile health, and access control systems. Commonly used technologies include decentralized identifiers, verifiable credentials, smart contracts, and privacy-enhancing mechanisms such as zero-knowledge proofs and selective disclosure. Despite rapid technical development, persistent challenges include interoperability limitations, governance gaps, usability concerns, and insufficient integration with health care infrastructures. Notably, a structural gap was identified between technological capability and system-level readiness for clinical deployment. Conclusions: BC-SSI technologies demonstrate potential for enabling secure, interoperable, and patient-centric identity management in health care. However, current research is predominantly technology-driven and lacks sufficient system-level validation. This study highlights the need for integrated architectural approaches, governance frameworks, and real-world evaluation to bridge the gap between conceptual innovation and clinical implementation. Advancing BC-SSI toward health care adoption will require coordinated progress across technical, organizational, and regulatory dimensions.