Evaluating the Portability of Rheumatoid Arthritis Phenotyping Algorithms: A Case Study on French EHRs.

Previous work has successfully used machine learning and natural language processing for the phenotyping of Rheumatoid Arthritis (RA) patients in hospitals within the United States and France. Our goal is to evaluate the adaptability of RA phenotyping algorithms to a new hospital, both at the patient and encounter levels. Two algorithms are adapted and evaluated with a newly developed RA gold standard corpus, including annotations at the encounter level. The adapted algorithms offer comparably good performance for patient-level phenotyping on the new corpus (F1 0.68 to 0.82), but lower performance for encounter-level (F1 0.54). Regarding adaptation feasibility and cost, the first algorithm incurred a heavier adaptation burden because it required manual feature engineering. However, it is less computationally intensive than the second, semi-supervised, algorithm.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Cluster analysis reveals three main patterns of beliefs and intention with respect to SARS-CoV-2 vaccination in patients with autoimmune and inflammatory diseases.

INTRODUCTION: Given the COVID-19 pandemic, it is crucial to understand the underlying behavioural determinants of SARS-CoV-2 vaccine hesitancy in patients with autoimmune or inflammatory rheumatic diseases (AIIRDs). We aimed to analyse patterns of beliefs and intention regarding SARS-CoV-2 vaccination in AIIRD patients, as a mean of identifying pragmatic actions that could be taken to increase vaccine coverage in this population. METHODS: Data relating to 1258 AIIRD patients were analysed using univariate and multivariate logistic regression models, to identify variables associated independently with willingness to get vaccinated against SARS-CoV-2. Subsets of patients showing similar beliefs and intention about SARS-CoV-2 vaccination were characterized using cluster analysis. RESULTS: Hierarchical cluster analysis identified three distinct clusters of AIIRD patients. Three predominant patient attitudes to SARS-COV-2 vaccination were identified: voluntary, hesitant and suspicious. While vaccine willingness differed significantly across the three clusters (P < 0.0001), there was no significant difference regarding fear of getting COVID-19 (P = 0.11), the presence of comorbidities (P = 0.23), the use of glucocorticoids (P = 0.21), or immunocompromised status (P = 0.63). However, patients from cluster #2 (hesitant) and #3 (suspicious) were significantly more concerned about vaccination, the use of a new vaccine technology, lack of long-term data in relation to COVID-19 vaccination, and potential financial links with pharmaceutical companies (P < 0.0001 in all) than patients from cluster #1 (voluntary). DISCUSSION: Importantly, the differences between clusters in terms of patient beliefs and intention was not related to the fear of getting COVID-19 or to any state of frailty, but was related to specific concerns about vaccination. This study may serve as a basis for improved communication and thus help increase COVID-19 vaccine coverage among AIIRD patients.