eHealth Africa

Digitalization in geriatric rehabilitation presents unique challenges, making it essential to align eHealth solutions with patients' digital skills. The Quickscan Digital Skills (QDS) is a tool designed to help health care professionals match eHealth interventions to individual skill levels. This study aimed to explore the applicability of QDS by comparing it to self-reported digital skills and to gain insight into the digital skills of patients in geriatric rehabilitation. In this multicenter cross-sectional study, participants from 13 geriatric rehabilitation centers in the Netherlands completed a survey, including demographic questions, QDS, and a numeric rating scale (NRS) for self-reported digital skills. Participants were categorized into 3 skill levels (beginner, intermediate, and experienced) based on the cutoff points in QDS scores. Cutoff points were predetermined, guided by the information provided on QDS. Descriptive statistics for median age and frequencies for skill levels were calculated. Comparative analysis using a Kruskal-Wallis test assessed differences between QDS and NRS within these groups, and Spearman rank-order correlation examined the relationship between the two measures. To gain more insight into the different skill levels between groups, data were visualized and associations among age, gender, and digital skill levels were examined using ordinal logistic regression analysis. A total of 463 patients (median age 78, IQR 12 years; 282/463, 60.9% female) participated in this study. Based on QDS scores, 42.1% (195/463) were classified as beginners, 19.4% (90/463) as intermediates, and 38.4% (178/463) as experienced users. A moderate positive correlation was found between QDS and NRS scores. Digital skills generally declined with age: 69.8% (37/53) of participants younger than 65 years were experienced users compared to only 13.2% (5/38) of those older than 91 years. A logistic regression analysis showed that increasing age was significantly associated with lower digital skill levels (odds ratio 0.93, 95% CI 0.92-0.95; P<.001). The association between age and digital skills does not differ between males and females. This study suggests that QDS is a promising and practical screening tool for assessing digital skills in patients in geriatric rehabilitation. Self-reported digital skills with an NRS do not capture the differentiation in the assessed abilities by QDS. QDS could be a practical tool for identifying digital skill levels in patients in geriatric rehabilitation and can support more personalized eHealth implementation. Further research should explore the parametric properties of QDS and how the scores relate to actual eHealth use.

ABSTRACTBackground and AimsWall shear stress (WSS) may govern the initiation and progression of atherosclerosis. We aimed to depict WSS distribution in symptomatic, atherosclerotic M1 middle cerebral artery (MCA‐M1) stenosis, and its associations with adjacent vessel and plaque geometry.MethodsPatients with symptomatic, atherosclerotic, 50%–99% MCA‐M1 stenosis were analyzed. MCA‐M1 vessel curve orientation and tortuosity, luminal stenosis, plaque length and longitudinal asymmetry were assessed on CT angiography (CTA). Relative WSS (rWSS) was calculated by the absolute WSS divided by mean WSS at the proximal, normal vessel segment, in a CTA‐based computational fluid dynamics model. rWSS < 1.0, 1.0–3.0, and > 3.0 were respectively defined as low, normal, and high WSS; low‐ and high‐WSS areas were measured. The vessel and plaque geometry was associated with the rWSS measures, across a plaque as a whole, and separately in upstream and downstream plaque segments divided at the stenotic throat.ResultsIn 176 patients, rWSS increased progressively along the upstream plaque segment but highly varied downstream. rWSS was lower on the inner than on the outer wall of the MCA‐M1 vessel curve. Patients with ventrally (than dorsally), inferiorly (than superiorly) oriented MCA‐M1 vessel curves and higher tortuosity of the affected vessel segment exhibited lower rWSS and larger low‐WSS areas at the downstream plaque segment. More severe luminal stenosis and upstream dominance in the plaque were associated with higher rWSS and larger high‐WSS areas in the upstream and downstream plaque segments.ConclusionsWall shear stress (WSS) distribution across symptomatic MCA‐M1 stenosis was variable and strongly associated with adjacent vessel and plaque geometry, independent of systemic factors.

Abstract We deployed a large language model (LLM) decision support using GPT4 for health workers at two outpatient clinics in Nigeria. For each patient, health workers drafted care plans that were optionally revised after LLM feedback. We compared unassisted and assisted plans using (i) blinded randomized assessments by on-site physicians who assessed and treated the same patients and (ii) results from laboratory tests for common conditions. Academic physicians performed blinded retrospective reviews of a subset of notes. Providers reported high satisfaction with LLM feedback, and retrospective academic reviewers rated LLM-assisted plans more favorably. However, on-site physicians observed little to no improvement in diagnostic alignment or treatment decisions. Objective testing showed mixed effects of LLM-assistance, with reduced overtesting for malaria but increased overtesting for urinary tract infection and anemia. This highlights a gap between chart-based reviews and real-world clinical relevance that may be especially important in evaluating the effectiveness of LLM-based interventions.

Abstract Introduction In 2023, Nigeria was still faced with the challenge of achieving COVID-19 vaccination targets, especially at the subnational levels. The Technical Assistance for COVID-19 Vaccine Deployment Project (TA CVDP), was implemented in 18 states to strengthen coordination, data use and institutional capacity. Methodology A Most Significant Change Evaluation study was conducted to identify outcomes attributed to the TA intervention. Six states were purposively selected, 1 per geo-political zone. Fifty-five stakeholders participated and 32 significant change stories were randomly selected and analyzed across the domains of assessment, planning and coordination, data use and institutional strengthening, using a thematic content analysis approach. Results The study found that there were significant changes across the domains. The stories showed improvement in COVID-19 Technical Working Group planning, coordination and functionality, and there was a noticeable increase in the utilization of data for planning, resource allocation, and targeting. Additionally, institutional strengthening was evident in increased government ownership, enhanced supervision, and improvement in state actors’ capacity to lead data management and logistics. Respondents consistently attributed these positive changes to the substantial support from the TA interventions such as the capacity building on data use, deployment of action plan tracker, and the establishment of the data control room. Conclusion The evaluation emphasizes the targeted role Technical Assistant Intervention can play in improving health systems in resource-constrained settings. Importantly, these changes were aligned with what respondents considered crucial, and there was a strong consensus that the project significantly addressed gaps in response to the anticipated results.

BackgroundStroke is a feared complication of transcatheter aortic valve replacement (TAVR), and effective cerebral embolic protection devices are an important innovation target. The EnCompass F2 system is a novel cerebral embolic protection device consisting of a cylindrical, nitinol frame and an electrospun polyurethane deflection filter with 30-μm average pore size. It is deployed across the aortic arch from femoral access to provide complete cerebral embolic protection during TAVR.MethodsThis first-in-human study investigated the feasibility and safety of F2 filter use during TAVR. Subjects had symptomatic severe aortic stenosis and met established clinical indications for TAVR. The primary safety end point was 30-day major adverse cardiac and cerebrovascular events, defined as all-cause death, stroke, major vascular complication, type 2 to 4 bleeding, or acute kidney injury stage 3 to 4. F2 filter technical and procedure success and new ischemic brain lesion counts and volumes on diffusion-weighted magnetic resonance imaging (DW-MRI) were evaluated.ResultsTwelve patients underwent transfemoral TAVR with the F2 device. Subjects were 58% female with mean age 73.4 ± 5.1 years and mean Society of Thoracic Surgeons score 3.2 ± 2.0%. A balloon-expandable valve was used in 75% (n = 9). A single F2 device was used in all cases and was delivered ipsilateral to the TAVR sheath in 41.7% (n = 5). Technical and procedure success was achieved in 100% of cases. No major adverse cardiac and cerebrovascular events occurred within 30 days of TAVR, including no strokes. DW-MRI revealed median total new lesion volume 23.4 mm3 (IQR: 13.1-159.8 mm3).ConclusionsIn this first-in-human series, cerebral embolic protection with the EnCompass F2 during TAVR was feasible and safe with very low new brain DW-MRI lesion volumes and no strokes.

This paper studies the contribution of the visual road scene to estimate the driver-reported stress levels. Our research leverages on previous work showing that environmental factors, such as traffic congestion, weather conditions, and driving context, impact driver’s stress. Each of the models we evaluated is trained and tested with the publicly available AffectiveROAD dataset to estimate three categories of driver-reported stress level. We test three types of modelling approaches: (i) single-frame baselines (Random Forest, SVM, and Convolutional Neural Networks); (ii) Temporal Segment Networks (TSN) and two variants of it, which use learned weights (TSN-w) and LSTM (TSN-LSTM) as consensus functions; and (iii) video classification Transformers. Our experiments reveal that the TSN-w, TSN-LSTM, and Transformer models achieve statistically equivalent performances, all significantly outperforming the other models. Particularly noteworthy is TSN-w, which attains the highest performance observed with an average accuracy of 0.77. We further provide an explainability analysis using Class Activation Mapping and image semantic segmentation to identify the elements of the road scene that contribute the most to high levels of stress. Our results demonstrate that the visible road scene offers significant contextual information for estimating driver-reported stress levels, with potential implications for the design of safer urban road environments.

In 2021, Nigeria developed a novel Electronic Management of Immunization Data (EMID) system to address COVID-19 data management challenges and ensure the successful implementation of its COVID-19 vaccine deployment plan. The EMID system was envisioned to be interoperable with the DHIS2 national data management system and serve as a gateway into the integration of other primary health care (PHC) service data management. However, the EMID system faced challenges, including inability to filter reports, missing or loss of data, and difficulties with data synchronization, which curtailed its potential to meet the country's needs for COVID-19 data management and negatively impacted system scalability to enable integration with other PHC data systems. Multilayered stakeholder interviews were conducted to determine the optimal functionality requirements for the EMID system. Based on these findings, an optimization plan was designed and implemented to address identified gaps and create a more stable and scalable system to enable further system integrations. Following optimization, a routine immunization module was developed and integrated with the EMID system as a first step to achieving an integrated data management system for PHC services in Nigeria. The integrated system currently provides an opportunity to address data fragmentation and strengthen PHC service delivery in Nigeria. By allowing 1 health care worker to deliver both vaccinations, there is also potential for reduction in cost and redundancies, informing redistribution of the health workforce and overall system strengthening. The journey from the initial challenges faced by the EMID system to the development of an integrated system for PHC services in Nigeria has been a transformative one. Through a thorough optimization process, training and capacity-building, stakeholder-driven improvements, and an elicitation exercise, the EMID system has evolved into a powerful tool for addressing data fragmentation and enhancing public health service delivery in the country.

La prise de médicaments selon un plan déterminé revêt des aspects juridiques