Health in All Policy Making Utilizing Big Data

The application of artificial intelligence in health policy: a scoping review

BACKGROUND

Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.

METHODS

We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data.

RESULTS

The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts.

CONCLUSION

Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.

Big data-driven public health policy making: Potential for the healthcare industry

The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

Analysing sentiment change detection of Covid-19 tweets

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.

How effective is the health promotion policy in Sichuan, China: based on the PMC-Index model and field evaluation

BACKGROUND

Many countries around the world highlight the health in all policies (HiAP). However, most of the related research focused on the influential factors and implementation strategies, with less concern on the evaluation of HiAP. In response to HiAP's call, the Chinese government has proposed health promotion policies (HPPs) in counties or districts, the evaluation of HPPs in sample counties or districts of Sichuan province in China is an essential basis for optimizing policy content, improving policy implementation, and ensuring health promotion's continuous and efficient operation.

METHODS

This paper established an evaluation system for HPPs based on the PMC-Index model and then quantitatively analyzed 37 representative HPPs from the pilot areas in Sichuan province. In addition, a team of experts conducted a field assessment.

RESULTS

The results showed that the average PMC index of 37 HPPs was 7.091, and correlation analysis showed that there was a significant correlation between the PMC index and expert score.

CONCLUSIONS

This study indicates that the overall consistency of HPPs was good and proves a connection between the formulation and implementation of HPPs.

The future of public health policymaking after COVID-19: a qualitative systematic review of lessons from Health in All Policies

Background: 'Health in All Policies' (HiAP) describes the pursuit of health equity. It has five main elements: treat health as a human right; identify evidence of the 'social determinants' of health inequalities, recognise that most powers to affect health are not held by health departments, promote intersectoral policymaking and collaboration inside and outside of government, and generate political will. Studies describe its potential but bemoan a major implementation gap. Some HiAP scholars learn from policymaking research how to understand this gap, but the use of policy theories is patchy. In that context, our guiding research question is: How does HiAP research use policy theory to understand policymaking? It allows us to zoom-out to survey the field and zoom-in to identify: the assumed and actual causes of policy change, and transferable lessons to HiAP scholars and advocates. Methods: Our qualitative systematic review (two phases, 2018 and 2020) identified 4972 HiAP articles. Of these, 113 journal articles (research and commentary) provide a non-trivial reference to policymaking (at least one reference to a policymaking concept). We use the 113 articles to produce a general HiAP narrative and explore how the relatively theory-informed articles enhance it. Results: Most articles focus on policy analysis (identifying policy problems and solutions) rather than policy theory (explaining policymaking dynamics). They report a disappointing gap between HiAP expectations and policy outcomes. Theory-informed articles contribute to a HiAP playbook to close that gap or a programme theory to design and evaluate HiAP in new ways.   Conclusions: Few HiAP articles use policy theories for their intended purpose. Policy theories provide lessons to aid critical reflection on power, political dilemmas, and policymaking context. HiAP scholars seek more instrumental lessons, potentially at the cost of effective advocacy and research.

The future of public health policymaking after COVID-19: a qualitative systematic review of lessons from Health in All Policies

Background: 'Health in All Policies' (HiAP) describes the pursuit of health equity. It has five main elements: treat health as a human right; identify evidence of the 'social determinants' of health inequalities, recognise that most powers to affect health are not held by health departments, promote intersectoral policymaking and collaboration inside and outside of government, and generate political will. Studies describe its potential but bemoan a major implementation gap. Some HiAP scholars learn from policymaking research how to understand this gap, but the use of policy theories is patchy. In that context, our guiding research question is: How does HiAP research use policy theory to understand policymaking? It allows us to zoom-out to survey the field and zoom-in to identify: the assumed and actual causes of policy change, and transferable lessons to HiAP scholars and advocates. Methods: Our qualitative systematic review (two phases, 2018 and 2020) identified 4972 HiAP articles. Of these, 113 journal articles (research and commentary) provide a non-trivial reference to policymaking (at least one reference to a policymaking concept). We use the 113 articles to produce a general HiAP narrative and explore how the relatively theory-informed articles enhance it. Results: Most articles focus on policy analysis (identifying policy problems and solutions) rather than policy theory (explaining policymaking dynamics). They report a disappointing gap between HiAP expectations and policy outcomes. Theory-informed articles contribute to a HiAP playbook to close that gap or a programme theory to design and evaluate HiAP in new ways.   Conclusions: Few HiAP articles use policy theories for their intended purpose. Policy theories provide lessons to aid critical reflection on power, political dilemmas, and policymaking context. HiAP scholars seek more instrumental lessons, potentially at the cost of effective advocacy and research.