Digital health technologies (DHTs) represent various products used in the healthcare system, including software, applications (apps) and online platforms benefiting individuals and the wider health and social care system. It is a field characterised by complexity and dynamism.
The English digital health ecosystem centres around patients as the ultimate beneficiaries but comprises a vibrant network of stakeholders from all the private, public and third sectors (e.g., non-profits), engaging with each other at various steps along the technology lifecycle.
The Thai government introduced its first Global Health Strategic Framework for 2016 to 2020 in 2016, under the cooperation between the Ministry of Public Health, Ministry of Foreign Affairs, and other sectors involved in global health initiatives. This was to promote policy coherence across sectors and enhance the country's health security, which can contribute to sustainable economic and social development. It also aimed at enabling Thailand to play a leading role in global health policy issues. In order to accomplish the goals of the global health strategic framework and effectively implement the strategy, the development of a monitoring & evaluation (M&E) mechanism is required as a tool to monitor progress and evaluate outputs of the plan.
This study aimed to review monitoring and evaluation mechanisms and frameworks implemented in other countries. Information obtained from this review will be used to support the development of an M&E mechanism and framework for the Thai global health strategic framework. Researchers employed descriptive literature search and review to obtain the required data. The review results were grouped into two categories: 1) M&E mechanisms implemented in seven countries, namely Australia, Canada, Japan, Norway, Switzerland, the UK, and the USA; and 2) case studies of M&E frameworks used to evaluate three global health issues, i.e. communicable disease surveillance, prevention and control of HIV/TB, and health systems strengthening, which were developed and promoted by the World Health Organization, Global Fund, and their partner organizations.
As COVID-19 spreads worldwide, national (and sub-national) governments and development partners are making use of a rapidly growing body of evidence to develop policies mitigating against this devastating pandemic. Mathematical models and computational simulation models play a unique role to inform resource planning and policy development (among other uses) through scenario analysis and short-term forecasting. Already in the first six months of this outbreak, we have seen many models at the sub-national, national, regional and global level being developed at an impressive speed.
This report also includes an in-depth discussion of factors and variables that affect unit costs, including labor (the most prominent), fixed costs, and potential contribution to epidemic control (e.g. achievement of targets). Key factors impacting input costs include staffing number and type, and capital costs, output costs vary based on factors including number and type of activities, innovations, and capacity to achieve targets. However, assessment of technical efficiency by model and site posed challenges. Assessed sites provide
different activities along the services cascade depending on KP group, geographical location, and other contextual factors. This variation also makes it difficult to determine specific drivers of unit costs. In addition, differenced in number and type of program inputs and outputs for activities result in variations in unit costs.
This report also includes an in-depth discussion of factors and variables that affect unit costs, including labor (the most prominent), fixed costs, and potential contribution to epidemic control (e.g. achievement of targets). Key factors impacting input costs include staffing number and type, and capital costs, output costs vary based on factors including number and type of activities, innovations, and capacity to achieve targets. However, assessment of technical efficiency by model and site posed challenges. Assessed sites provide
different activities along the services cascade depending on KP group, geographical location, and other contextual factors. This variation also makes it difficult to determine specific drivers of unit costs. In addition, differenced in number and type of program inputs and outputs for activities result in variations in unit costs.
As COVID-19 spreads worldwide, national (and sub-national) governments and development partners are making use of a rapidly growing body of evidence to develop policies mitigating against this devastating pandemic. Mathematical models and computational simulation models play a unique role to inform resource planning and policy development (among other uses) through scenario analysis and short-term forecasting. Already in the first six months of this outbreak, we have seen many models at the sub-national, national, regional and global level being developed at an impressive speed.
This report summarises the study which aimed to: assess the extent to which Gavi HSS support provided to DPR Korea during this period achieved, or is on track to achieve, its objectives; determine to what extent it has contributed to strengthening the health system of the country; identify issues encountered during implementation that have affected the overall results; and share the lessons learnt for informed decision-making with regard to future support from Gavi and other international donors to the DPR Korea. This study was supported by the World Health Organization (WHO) DPR Korea.