Reshaping the Healthcare Sector with Economic Policy Measures Based on COVID-19 Epidemic Severity: A Global Study

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.

The causal configurations of provincial health policy innovation in China: an analysis of the food safety standard filing policy

Introduction

According to China's Food Safety Law of 2015, the filing of food safety enterprise standards is a policy innovation led by p9rovincial governments in China. However, there are significant differences in the development of the "Food Safety Enterprise Standard Filing Policy" between provincial governments across the country. This study aims to explore the internal mechanisms driving autonomous innovation by provincial governments in the absence of administrative pressure from the central government, to better understand the policy innovation mechanism in the Chinese context.

Methods

Crispy Set Qualitative Comparative Analysis (csQCA) method is used to identify the innovation mechanism.

Results

This study found that provinces with good provincial economic resources and strong government capabilities are prone to policy innovation, and the influence of internal factors of provincial governments is stronger than that of external factors.

Discussion

When provincial economic resources and capacity are weak, endogenous factors in the province also help achieve proactive policy innovation by provincial governments. The research results reveal how provincial governments construct local policies in the absence of administrative pressure from the central government.

The Spatiality of COVID-19 in Kermanshah Metropolis, Iran

The COVID-19 pandemic is a severe ongoing health crisisworldwide. Studying the socio-economic impacts of COVID-19 can help policymakers develop successful pandemic management plans. This paper focuses on the spatial epidemiology of COVID-19 among different social classes in the Kermanshah metropolis, Iran. This cross-sectional study uses the data of people infected with COVID-19 in the Kermanshah metropolis in 2020, acquired from the official COVID-19 Registry of Kermanshah. The results show that 2013 people were infected with COVID-19 (male = 1164 and female = 849). The mean age of the patients was 45 ± 18.69. The Moran’s I show that COVID-19 in different social classes was clustered across the neighbourhoods in the Kermanshah metropolis. The mean ages of men and women were 44.51 ± 18.62 and 45.69 ± 18.76, respectively. Importantly, COVID-19 was highly prevalent in the middle-class groups. Age group comparisons indicate that older people were the most infected in poorer neighbourhoods. In the middle-classtheage group of 0–14 years and in the rich neighbourhoods the age group of 15–64 years were the most exposed to the disease. The findings of this study suggest that older people and lower socioeconomic classes should be prioritised while developing and implementing preventative programs for COVID-19 and similar pandemics.