Swathi Katta S

Menstrual cups are gaining global recognition as a sustainable and cost-effective alternative to conventional menstrual products. However, their adoption in India remains limited due to various factors. Understanding of key determinants influencing their acceptance, such as awareness, affordability, safety concerns and cultural perceptions, is crucial for promoting informed choice and menstrual health equity. Hence, this study aims to assess the factors influencing intentions to adopt menstrual cups among Indian women. A cross-sectional online survey was conducted among 282 women of reproductive age groups across diverse demographic backgrounds in India. Partial least squares structural equation modelling was employed to examine the relationships among key factors and behavioural intention to adopt menstrual cups. The findings suggest that awareness, perceived ease of use, and price value significantly shape attitudes towards menstrual cups ( p < 0.001). Furthermore, attitudes towards menstrual cups, safety perceptions and subjective norms play a significant role in influencing adoption intentions. Additionally, safety perceptions mediated the relationship between attitude and behavioral intentions with a VAF of 0.5097. The study highlights the need for targeted education campaigns that address the benefits and safety concerns to enhance the acceptance of menstrual cups. The findings may be useful for public health experts, policymakers and marketers in devising strategies to enhance menstrual cup acceptance rates in India.

This study measured the quality of discharge teaching and discharge readiness levels in ischemic stroke patients to understand the factors affecting the discharge. A total of 120 patients with mild-to-moderate ischemic stroke were recruited using simple random sampling. Three instruments, namely, a demographic data questionnaire, the readiness for hospital discharge scale, and the quality of discharge teaching scale, were used for data collection. Descriptive statistical analysis and parametric tests with a correlation matrix, one-way analysis of variance, and independent t-tests were used for data analysis, providing robust evidence that the quality of discharge teaching was strongly correlated with patient readiness (r = 0.625, p<0.001) and underscoring that effective education was a key, modifiable factor for improving outcomes. The results advocated for preemptive screening and individualized discharge plans to enhance care quality and reduce readmission risks.

Background Technology is revolutionizing healthcare, making it more connected, efficient, and patient-centric. Smart healthcare tools like wearables and mobile health applications empower individuals to manage their health proactively. By leveraging these technologies, individuals can monitor chronic conditions like hypertension, diabetes, obesity, and cardiac issues, potentially preventing their detrimental consequences. This proactive approach not only enhances personal health management but also contributes to the overall well-being of society. Objective This study aims to understand individuals’ perceptions of smart technology usage and identify the antecedents influencing the adoption of smart healthcare technological applications. Methods This cross-sectional study used a structured questionnaire to collect the responses from 390 respondents in the Indian context. The data were analyzed using Partial Least Square Structural Equation Modeling. Findings and Conclusion The findings of this research show that antecedents such as self-efficacy, preventive awareness, technology promptness and innovativeness, and social influence play a significant role in the adoption of technology among individuals. Further, the study’s results will help to develop and promote technological applications to improve population health and have implications for healthcare providers, technology developers, marketers, and researchers.

Background Technology is revolutionizing healthcare, making it more connected, efficient, and patient-centric. Smart healthcare tools like wearables and mobile health applications empower individuals to manage their health proactively. By leveraging these technologies, individuals can monitor chronic conditions like hypertension, diabetes, obesity, and cardiac issues, potentially preventing their detrimental consequences. This proactive approach not only enhances personal health management but also contributes to the overall well-being of society. Objective This study aims to understand individuals’ perceptions of smart technology usage and identify the antecedents influencing the adoption of smart healthcare technological applications. Methods This cross-sectional study used a structured questionnaire to collect the responses from 390 respondents in the Indian context. The data were analyzed using Partial Least Square Structural Equation Modeling. Findings and Conclusion The findings of this research show that antecedents such as self-efficacy, preventive awareness, technology promptness and innovativeness, and social influence play a significant role in the adoption of technology among individuals. Further, the study’s results will help to develop and promote technological applications to improve population health and have implications for healthcare providers, technology developers, marketers, and researchers.

Artificial intelligence (AI) technologies are used in healthcare to detect diseases faster, provide personalized treatment, automate some processes, and enhance hospital safety. Yet, one of the critical challenges is users’ hesitation and unwillingness to adopt technological applications in medical practice. Hence, this research aims to identify the important factors affecting professionals’ behavioural intentions to use AI in healthcare. This cross-sectional survey was conducted in Kerala, India. We proposed a framework grounded on the literature on technology acceptance theories, and the conceptual model was tested using a 20-item questionnaire with a 5-point Likert scale. The structured questionnaire was converted into a Google Form and shared with the respondents. A total of 347 respondents, including doctors, nurses, and allied healthcare professionals from 5 different private hospitals in India, participated in this study. The partial least square structural equation modelling approach was used to evaluate the proposed framework upon confirming the reliability and validity of the measurement items. The study findings reveal that perceived ease of use (PEU), perceived usefulness (PU), and facilitating conditions (FC) are the critical factors that influence the behavioural intentions (BI) of healthcare professionals to use AI. Though healthcare professionals perceived the risk of using AI, it did not affect their choice to use AI. This study gives an understanding of healthcare professionals’ behavioural intentions to use AI so that the administrators can plan and improve their strategies to implement AI in hospitals.

Background Technology is revolutionizing healthcare, making it more connected, efficient, and patient-centric. Smart healthcare tools like wearables and mobile health applications empower individuals to manage their health proactively. By leveraging these technologies, individuals can monitor chronic conditions like hypertension, diabetes, obesity, and cardiac issues, potentially preventing their detrimental consequences. This proactive approach not only enhances personal health management but also contributes to the overall well-being of society. Objective This study aims to understand individuals’ perceptions of smart technology usage and identify the antecedents influencing the adoption of smart healthcare technological applications. Methods This cross-sectional study used a structured questionnaire to collect the responses from 390 respondents in the Indian context. The data were analyzed using Partial Least Square Structural Equation Modeling. Findings and Conclusion The findings of this research show that antecedents such as self-efficacy, preventive awareness, technology promptness and innovativeness, and social influence play a significant role in the adoption of technology among individuals. Further, the study’s results will help to develop and promote technological applications to improve population health and have implications for healthcare providers, technology developers, marketers, and researchers.

This study aims to identify the nature and prevalence of patient safety incidents and the design, development, and implementation of a patient-reported incident management system. The system's effectiveness in capturing patient-reported incidents and promoting patient empowerment was also evaluated. Findings reveal that nearly 33.8% of patients reported experiencing a patient safety incident, with 81.6% of these incidents identified by the patients themselves. After the implementation of the system, 47% of reported incidents were addressed by staff, indicating a significant improvement in patient empowerment. Furthermore, there was a 71% increase in overall incident reporting. Following the introduction of the patient-reported incident management system, 67% of patients reported feeling more empowered, and 53% provided positive feedback regarding the system's user-friendliness.

Presents corrections to the paper, (Corrections to “Advancing Ovarian Cancer Diagnosis Through Deep Learning and eXplainable AI: A Multiclassification Approach”).

Ovarian cancer is a dangerous gynaecological malignancy, and the presence of many subtypes causes significant diagnostic difficulties. In general, the high accuracy of classification results in adequate prognosis and effectiveness of treatment. This work aims at the development of a DL approach for subtypes of ovarian cancer multiclassification, which tries to solve the problem of the creation of precise and reliable diagnostic methods. In the work, we have used and explored various DL models such as MobileNetV2, VGG19, ResNet18, ResNeXt, Xception, EfficientNet, and InceptionV3 to perform the classification task. Further, we used the state-of-the-art eXplainable Artificial Intelligence methods, including integrated gradient, saliency map, Grad-CAM, and DeepLift, to improve model interpretability. From our experiments, we inferred that the highest accuracy was achieved by InceptionV3, with a value of 97.96%. XAI techniques incorporated provide transparent insights into the model’s operations during the decision-making process, thus increasing the level of trust and clinical usability. The proposed DL approach, by leveraging InceptionV3 as its top performer, has convincingly demonstrated the potential of AI to revolutionize the diagnosis of ovarian cancer through a high level of accuracy in subtype classification. XAI techniques integrated allow transparency support for the model and further enable its clinical adoption. All of these developments have significant potential for improved patient outcomes within the scope of personalized medicine in ovarian cancer treatment.