Low rates of actively contributing to the Social Health Insurance Fund (SHIF) under the Social Health Authority (SHA) could be due to health insurance knowledge inadequacies, possibly because of poor electronic health (eHealth) literacy. This study assesses whether eHealth literacy is associated with SHA/SHIF knowledge among undergraduate healthcare students in Kenya. An analytical cross-sectional study was conducted using the eHealth Literacy Scale (eHEALS) and an adapted Kaiser Family Foundation quiz. A total of 207 students in mainly six health-related academic programs in 21 institutions of higher learning in Kenya responded to the online survey. Only 54% and 21.7% of the participants had high (median ≥ 4 out of 5) levels of eHealth literacy and knowledge of SHA/SHIF, respectively. About 9.2% of the students had never heard of SHA/SHIF. Only high eHealth literacy compared to low eHealth literacy (OR = 6.2, p < 0.0001) and pursuing nursing, public health, and other programs compared to pursuing Bachelor of Medicine and Bachelor of Surgery (MBChB) (OR = 4.9–5.1, p = 0.01–0.03) significantly predicted SHA/SHIF knowledge. Thus, eHealth literacy levels and SHA/SHIF knowledge require improvement among undergraduate healthcare students in Kenya to prepare them as SHA ambassadors in their communities.

Background/Objectives: Strategies to improve the Social Health Authority (SHA)’s equity can be identified by analyzing the Kenya Demographic and Health Survey (KDHS) 2022. This study reports evidence of determinants of health insurance coverage in Kenya. Methods: Household- and individual-level datasets from the Kenya Demographic and Health Survey conducted between February and July 2022 were combined to form the analyzed dataset. Proportions of individuals with and without health insurance were estimated. The associations between potential determinants and health insurance status were calculated using the Rao–Scott chi-square. Logistic regression was used to analyze the determinants of health insurance coverage. Results: Most of the 14,232 participants were literate (75%), relatively poor (56%), in good health (79%), connected to electricity (55%), and radio listeners (61%). About 34% had health insurance, with 93% of the insured covered by the NHIF. Twenty predictors (Adjusted F = 4.2–434.1, p < 0.0001) were included in the complex sample logistic regression model, but only nine were statistically significant predictors of health insurance coverage. The key predictors were education level; wealth index; ownership of a solar panel, television, smartphone, and computer; age; and recent outpatient care (11–80% differences in odds). Conclusions: Health insurance coverage remains low in Kenya due to low education levels, poor economic status, and disparities in access to media. The SHA can emphasize media campaigns in the informal sector to increase premium payments. Accelerating socioeconomic advancement and adopting tax-based funding could speed up Kenya’s progress towards UHC.

Green technologies in the health care sector offer the potential to improve the environmental sustainability and the quality of patient care. This study presents a bibliometric analysis of research on green technologies in healthcare. It aims to identify trends in the literature, key researchers and influential publications. The analysis included articles published between 2000 and 2023. The data was collected using the Web of Science and Scopus databases. The bibliometric analysis provides an important resource for understanding the current state of the literature and future research directions in the field of green technologies in health. It is of particular interest that green technologies have little relation to artificial intelligence, which is a popular research topic today. The spread of more sustainable and environmentally friendly practices in the health sector will benefit from increased interest and research activity in this area.

BACKGROUND Musculoskeletal disorders increased by 152% in the Middle East and North Africa region with Kuwait leading in prevalence between 1990 and 2019. Yet, artificial intelligence (AI)-enabled professional networking solutions to speedify inter-professional consultations toward reducing burnout are barely used in Kuwait. Concerns over poor adoption intention considering over-reliance on informal channels for professional communication in Kuwait may be hindering market entry into MENA countries. OBJECTIVE This study aimed to identify predictors of adoption intention of a hypothetical AI-enabled professional support solution among orthopedics in Kuwait. METHODS A cross-sectional offline survey was conducted among orthopedics in Kuwait to assess their agreement with statements on trust, social identify (SI), perceived usefulness (PU), and perceived ease of use (PEOU) regarding a hypothetical AI-enabled professional networking solution. Path coefficients were calculated to determine the relationships between the factors in multivariable regression and structural equation modelling. Mediation, moderation, importance performance, and dominance analyses were conducted to characterize the relationships between factors. RESULTS A total of 326 orthopedics mainly surgeons and physiotherapists with varying years of experience working in hospitals participated in the survey. Perceiving profession’s success as personal success and pride in the professional group, aspects of social identity, were the strongest positive and negative individual predictors of adoption intention (β = 0.55 to 0.58, p < 0.001 and β = -0.34 to -0.35, p < 0.001, respectively in both models. Trust was the strongest direct positive predictor (β = 0.6, p < 0.001), the mediator of the relationship between PU and SI, the factor requiring the most attention, and the factor with the highest dominance weight (average contribution = 0.28, 53.8% of R2) followed by SI (0.148, 28.1% of R2). CONCLUSIONS Trust significantly predicted adoption intention of AI-enabled professional networking solution either directly or by influencing PU and SI. SI, the second most significant predictor of adoption intention, is complex. Developers of and researchers on the AI-enabled professional networking solutions should align with the social identities and trust-building essentials of Kuwaiti orthopedics to improve adoption intention and prediction models.

Low rates of actively contributing to the Social Health Insurance Fund (SHIF) under Social Health Authority (SHA) could be due to health insurance knowledge inadequacies possibly due to poor eHealth literacy. This study assesses whether eHealth literacy predicts SHA/SHIF knowledge among undergraduate students in health-related courses in Kenya. An analytical cross-sectional online survey was conducted using the eHealth Literacy Scale (eHEALS) and adapted Kaiser Family Foundation quiz to collect data. A total of 207 tertiary education students mainly pursuing six health courses in 21 institutions in Kenya responded to the survey. The median eHealth literacy score was 3.9/5, with about 54% having high eHealth literacy levels (median ≥ 4). About 9.2% of the students had never heard of SHA/SHIF while only 21.7% had high knowledge of SHA/SHIF (median ≥ 4), with the median knowledge score being 3/5. Only high eHealth literacy compared to low eHealth literacy (OR = 6.2, p<0.0001) and pursuing nursing, public health, and other courses compared to pursuing MBChB (OR = 4.9-5.1, p = 0.01-0.03) significantly predicted SHA/SHIF knowledge. Thus, health students in Kenya had modest eHealth literacy levels and SHA/SHIF knowledge. eHealth literacy was a significant predictor of SHA/SHIF knowledge, thus interventions can target it.

Medical tourism has been proposed as a potential means of using available capacity in public hospitals, generating additional revenue, and supporting healt