METHODOLOGY: Data were collected form 5310 patients in 249 private clinics. The patients evaluated their satisfaction on the quality of service on the basis of nine criteria that comprised 31 subcriteria. We used multicriteria satisfaction analysis (MUSA) to analyze the data.
FINDINGS AND CONCLUSION: The data analysis results showed low level of satisfaction on the health care quality services offered by the private clinics in Yemen. The majority of the criteria and subcriteria showed low level of satisfaction, high demand, and high mandate for improvement.
METHODS: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.
RESULTS: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.
CONCLUSIONS: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.
DISCUSSION: Although crucial guidance has been released on how to maintain TB and HIV services during the pandemic, it is acknowledged that what was considered normal service pre-pandemic needs to improve to ensure that we rebuild person-centred, inclusive and quality healthcare services. The threat that the pandemic may reverse gains in the response to TB and HIV may be turned into an opportunity by pivoting to using proven differentiated service delivery approaches and innovative technologies that can be used to maintain care during the pandemic and accelerate improved service delivery in the long term. Models of care should be convenient, supportive and sufficiently differentiated to avoid burdensome clinic visits for medication pick-ups or directly observed treatments. Additionally, the pandemic has highlighted the chronic and short-sighted lack of investment in health systems and the need to prioritize research and development to close the gaps in TB diagnosis, treatment and prevention, especially for children and people with HIV. Most importantly, TB-affected communities and civil society must be supported to lead the planning, implementation and monitoring of TB and HIV services, especially in the time of COVID-19 where services have been disrupted, and to report on legal, policy and gender-related barriers to access experienced by affected people. This will help to ensure that TB services are held accountable by affected communities for delivering equitable access to quality, affordable and non-discriminatory services during and beyond the pandemic.
CONCLUSIONS: Successfully reaching the related targets of ending TB and AIDS as public health threats by 2030 requires rebuilding of stronger, more inclusive health systems by advancing equitable access to quality TB services, including for people with HIV, both during and after the COVID-19 pandemic. Moreover, services must be rights-based, community-led and community-based, to ensure that no one is left behind.