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

Bergier, H.; Duron, Loïc; Sordet, Christelle; Kawka, Lou; Schlencker, Aurélien; Chasset, François; Arnaud, Laurent

doi:10.1016/j.autrev.2021.102864

Cited by 31 publications

(22 citation statements)

References 68 publications

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“…Furthermore, these assessments could be carried out in the patients' homes. This fits in perfectly with telemonitoring [82] and the use of real-world data [83]. This would therefore allow the development (by increasing the number of potential users, companies may be more inclined to invest in such solutions) and wider use of these solutions.…”

Section: Discussionsupporting

confidence: 58%

Is mHealth a Useful Tool for Self-Assessment and Rehabilitation of People with Multiple Sclerosis? A Systematic Review

et al. 2021

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The development of mobile technology and mobile Internet offers new possibilities in rehabilitation and clinical assessment in a longitudinal perspective for multiple sclerosis management. However, because the mobile health applications (mHealth) have only been developed recently, the level of evidence supporting the use of mHealth in people with multiple sclerosis (pwMS) is currently unclear. Therefore, this review aims to list and describe the different mHealth available for rehabilitation and self-assessment of pwMS and to define the level of evidence supporting these interventions for functioning problems categorized within the International Classification of Functioning, Disability and Health (ICF). In total, 36 studies, performed with 22 different mHealth, were included in this review, 30 about rehabilitation and six for self-assessment, representing 3091 patients. For rehabilitation, most of the studies were focusing on cognitive function and fatigue. Concerning the efficacy, we found a small but significant effect of the use of mHealth for cognitive training (Standardized Mean Difference (SMD) = 0.28 [0.12; 0.45]) and moderate effect for fatigue (SMD = 0.61 [0.47; 0.76]). mHealth is a promising tool in pwMS but more studies are needed to validate these solutions in the other ICF categories. More replications studies are also needed as most of the mHealth have only been assessed in one single study.

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Section: Discussionsupporting

confidence: 58%

Is mHealth a Useful Tool for Self-Assessment and Rehabilitation of People with Multiple Sclerosis? A Systematic Review

et al. 2021

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“…Therefore, the information management system has been established in most countries [ 12 , 13 ]. Recording individual medical information in the computer reduces the incidence of medical errors, thus improving the overall level of medical treatment and providing better medical services for patients [ 14 , 15 ]. The method of DS data extraction is parallelism between system instances and serial extraction of tables between system instances [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care

et al. 2022

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Objective. In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city. Methods. A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared. Results. After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant ( P < 0.05 ). The scores of anxiety and depression in the group were lower, with statistical significance ( P < 0.05 ); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant ( P < 0.05 ); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant ( P > 0.05 ). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant ( P < 0.05 ). Conclusion. The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women’s self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.

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“…A variety of other clinical uses for AI/ML are also in various stages of development, but space precludes extensive discussion, including digital health, smart technology, wearables, care algorithms, and monitoring of adherence 66 . Wearables are of particular interest given the potential for continuous monitoring 67 .…”

Section: Future Directions For Ai/ml In Rmdsmentioning

confidence: 99%

“…Wearables are of particular interest given the potential for continuous monitoring 67 . This type of data could allow for automated alerts to patients or their physicians, direct patient feedback, and/or algorithm-based automatic intervention 67 , while providing an opportunity to increase access and potentially improve monitoring and outcomes 66 . An obvious limitation, in addition to cost and use of the technology itself, is the need for enhanced health and digital literacy of both patients and their care providers to allow optimal use of such tools.…”

Section: Future Directions For Ai/ml In Rmdsmentioning

confidence: 99%

“…Unfortunately, the currently available methods (e.g., regression with understandable coefficients or heat maps for imaging applications) do not imply accurate performance and may give false assurances, and thus are better understood as tools for developers 47 . Any tool to be utilized in clinical care must undergo "robust assessments of the efficacy, affordability, and scalability of AI in the context of digital health for rare connective tissue diseases…to avoid the detrimental waste of scarce resources 66 ." AI/ML techniques can inform all stages of drug development and repurposing, including identification of potential targets, validation of those targets, identification of biomarkers and optimization of clinical trial endpoints.…”

Section: Future Directions For Ai/ml In Rmdsmentioning

confidence: 99%

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Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

Nelson

Arbeeva

2022

J Rheumatol

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There has been rapid growth in the use of artificial intelligence analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers given the "black box" nature of most algorithms and the unfamiliarity of the terms and lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

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

Cited by 31 publications

References 68 publications

Is mHealth a Useful Tool for Self-Assessment and Rehabilitation of People with Multiple Sclerosis? A Systematic Review

Is mHealth a Useful Tool for Self-Assessment and Rehabilitation of People with Multiple Sclerosis? A Systematic Review

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

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