Search

Background and Aims: Coronavirus has resulted in unique challenges to the healthcare professionals. The pandemic has affected the mental health of healthcare professionals, which may impact their ability to provide care to patients. We aimed to assess anxiety among healthcare professionals, and its relation to selected parameters. Methods: This online questionnaire-based study recruited Indian healthcare workers through convenience sampling. Anxiety was measured using the Generalized Anxiety Disorder 7 scale. The reasons of emotional distress were also ascertained. Results: The sample comprised of 677 healthcare professionals including resident doctors, consultants, interns, nursing personnel, and others. Moderate/severe anxiety was present in 24.4% of the respondents. Moderate/severe anxiety was more likely to occur among those who had a friend or family member affected by COVID-19 (odds ratio [OR] 2.74, 95% confidence intervals [CI] 1.61–4.69), or who themselves had a psychiatric disorder in the past (OR 2.44, 95% CI 1.36–4.38). Those who reported long working hours and inadequate sleep, fear of being infected by the virus, concerns about transmitting the virus to a family member, mob violence against doctors, and shortage of personal protective equipment had significantly higher odds of moderate/severe anxiety. Conclusion: A considerable proportion of healthcare professionals suffering from anxiety suggests the need for addressing their mental health concerns.

ABSTRACT: Objectives: Antimicrobial resistance (AMR) is the next big pandemic that threatens humanity. The One Health approach to AMR requires quantification of interactions between health, demographic, socioeconomic, environmental, and geopolitical factors to design interventions. This study is focused on learning health system factors on global AMR. Methods: This study analysed longitudinal data (2004–2017) of AMR having 6 33 820 isolates from 70 middle and high-income countries. We integrated AMR data with the Global Burden of Disease (GBD), Governance (WGI), and Finance data sets to find AMR's unbiased and actionable determinants. We chose a Bayesian decision network (BDN) approach within the causal modelling framework to quantify determinants of AMR. Further, we integrated Bayesian networks’ global knowledge discovery approach with discriminative machine learning to predict individual-level antibiotic susceptibility in patients. Results: From MAR (multiple antibiotic resistance) scores, we found a non-uniform spread pattern of AMR. Components-level analysis revealed that governance, finance, and disease burden variables strongly correlate with AMR. From the Bayesian network analysis, we found that access to immunization, obstetric care, and government effectiveness are strong, actionable factors in reducing AMR, confirmed by what-if analysis. Finally, our discriminative machine learning models achieved an individual-level AUROC (Area under receiver operating characteristic curve) of 0.94 (SE = 0.01) and 0.89 (SE = 0.002) to predict Staphylococcus aureus resistance to ceftaroline and oxacillin, respectively. Conclusion: Causal machine learning revealed that immunisation strategies and quality of governance are vital, actionable interventions to reduce AMR.

Abstract The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

Background: Teleneurology consultations can be highly advantageous since neurological diseases and disabilities often limit patient's access to health care, particularly in a setting where they need to travel long distances for specialty consults. Patient satisfaction is an important outcome assessing success of a telemedicine program. Materials and Methods: A cross-sectional study was conducted to determine satisfaction and perception of patients toward an audio call based teleneurology follow-up initiated during the coronavirus disease 2019 pandemic. Primary outcomes were satisfaction to tele-consult, and proportion of patients preferring telemedicine for future follow-up. Results: A total of 261 patients who received tele-consult were enrolled. Satisfaction was highest for domain technological quality, followed by patient?physician dialogue (PPD) and least to quality of care (QoC). Median (interquartile range) patient satisfaction on a 5-point Likert scale was 4 (3?5). Eighty-five (32.6%; 95% confidence interval 26.9?38.6%) patients preferred telemedicine for future follow-up. Higher overall satisfaction was associated with health condition being stable/better, change in treatment advised on tele-consult, diagnosis not requiring follow-up examination, higher scores on domains QoC and PPD (p?0.05). Future preference for telemedicine was associated with patient him-/herself consulting with doctor, less duration of follow-up, higher overall satisfaction, and higher scores on domain QoC (p?