Artificial Intelligence (AI) is rapidly becoming an integral part of various industries, and the field of public health is no exception. In the coming decade, AI is anticipated to bring about significant changes to the public health profession, education, and workforce, with far-reaching implications for the way health services are delivered and managed. The integration of AI into public health holds promise for improved efficiency, accuracy, and resource allocation, with the potential to revolutionize disease surveillance, diagnostics, health education, and policy development, among other areas.
However, the adoption of AI in public health also presents challenges that must be addressed to ensure equitable and ethical outcomes. Workforce displacement, potential biases in AI models, concerns about public trust, and overreliance on technology are some of the critical issues that public health professionals will need to navigate as AI becomes increasingly integrated into their field. Ensuring that AI-driven solutions are transparent, fair, and representative of diverse populations is crucial to minimize the risk of exacerbating existing health disparities.
This in-depth analysis will explore the potential implications of AI integration across the 10 essential public health services (EPHS), discussing the pros and cons for each while providing examples and evidence from current circumstances and trends.
The analysis will also consider a variety of topics, such as automation, speed of service, service accuracy/efficiency, replacement of workforce, simulation use, public trust, and budget efficiency. Finally, it will provide recommendations for public health professionals to prepare for these forthcoming changes, focusing on skill development, interdisciplinary collaboration, ethical AI practices, and community engagement. By examining both the opportunities and challenges presented by AI in public health, this analysis aims to provide a comprehensive understanding of the evolving landscape and the steps that public health professionals can take to adapt and thrive in a rapidly changing world.
Assess and Monitoring Population Health Status
The integration of AI into the monitoring of health status and identification of community health problems has the potential to revolutionize public health surveillance and response. With AI’s ability to analyze vast amounts of data quickly and accurately, it can improve the efficiency and effectiveness of these essential public health services.
Pros:
Enhanced disease surveillance: AI can automate data collection and analysis for disease surveillance, enabling public health professionals to identify patterns and trends more rapidly. This increased speed in data processing allows for improved outbreak prediction and real-time monitoring of health issues, leading to more timely interventions and potentially better health outcomes.
Precision public health initiatives: AI algorithms can analyze vast amounts of data to identify specific health needs within communities, which can pave the way for targeted public health interventions. By identifying the most pressing health problems within specific populations, AI-driven tools can help public health professionals develop and implement more effective and tailored programs, addressing the unique needs of different communities.
Comprehensive data analysis: AI-driven tools can process a wide range of data sources, including electronic health records, social media, and environmental data, to paint a more accurate and comprehensive picture of community health. This holistic approach enables public health professionals to better understand the complex factors that contribute to health outcomes and design more informed strategies to address them.
Early warning systems: AI can be utilized to develop early warning systems that can predict and detect emerging health threats, such as infectious disease outbreaks, environmental hazards, or potential pandemics. These systems can help public health professionals respond proactively, preventing or mitigating the impact of these threats on communities.
Cons:
Data quality and bias: Biased algorithms, resulting from incomplete or unrepresentative data sets, can negatively impact health monitoring and exacerbate disparities. Ensuring the quality, diversity, and representativeness of the data used to train AI models is crucial to avoid reinforcing or perpetuating existing health inequalities.
Overreliance on technology: The increased use of AI-driven tools for health monitoring may diminish the role of human expertise and intuition in identifying health problems. Striking a balance between leveraging AI’s capabilities and valuing human judgment is essential to ensure that public health professionals continue to play a critical role in identifying and addressing community health issues.
Privacy concerns: The extensive data collection required for AI-driven health monitoring raises concerns about individual privacy and data security. Ensuring that personal health information is collected, stored, and analyzed in a secure and ethical manner is essential to maintain public trust in public health initiatives.
Digital divide and resource access: As AI becomes more central to health monitoring, the digital divide may widen, leaving communities with limited access to technology or digital literacy skills at a disadvantage. Addressing this disparity is critical to ensure that all communities can benefit from the advances in AI-driven public health surveillance and response.
Investigate, diagnose, and address health problems and hazards affecting the population
The application of AI in diagnosing and investigating health problems and hazards can significantly enhance the ability of public health professionals to detect, understand, and respond to various health issues. AI-driven tools can aid in the early detection of diseases, help identify potential health hazards, and optimize the distribution of public health resources for disease investigation. However, there are also challenges associated with the use of AI in this essential public health service, including the potential for overreliance on technology and concerns about biased algorithms.
Pros:
Improved disease detection: AI-powered diagnostic tools can significantly enhance disease detection by analyzing medical images, laboratory results, and other data to identify subtle patterns that may be missed by human eyes. This can lead to better treatment outcomes and reduced health risks for individuals and communities.
Predicting health hazards: AI-driven epidemiological models can help public health professionals identify potential health hazards and predict their impact on communities. This can enable more targeted prevention efforts and better allocation of resources to mitigate the effects of these hazards.
Increased laboratory efficiency: Automation of repetitive laboratory tasks using AI can increase efficiency and reduce human error, allowing for faster processing of samples and more accurate results. This can enhance the ability of public health professionals to diagnose and investigate health problems in a timely manner.
Optimized resource distribution: AI can help optimize the distribution of public health resources for disease investigation by analyzing data on disease prevalence, population density, and other factors. This can ensure that resources are directed to where they are needed most, improving the overall effectiveness of public health interventions.
Cons:
Overreliance on AI-driven diagnostics: An increasing dependence on AI-driven diagnostic tools may lead to a decline in human expertise and clinical judgment, potentially negatively impacting patient care. Maintaining a balance between AI-driven diagnostics and human expertise is crucial for ensuring the best possible health outcomes.
Inadequately tested and validated algorithms: If AI algorithms used for diagnostics and health hazard assessments are not adequately tested and validated, they may result in misdiagnoses or missed diagnoses, leading to suboptimal treatment decisions and potential harm to patients.
Generalizability of AI models: AI models trained on specific populations may not generalize well to other populations, leading to inaccurate diagnoses and health hazard assessments for individuals from underrepresented groups. Ensuring that AI models are trained on diverse and representative data sets is crucial for mitigating this risk.
Ethical concerns: The use of AI in diagnosing and investigating health problems raises ethical concerns regarding consent, data privacy, and algorithmic fairness. Addressing these concerns and ensuring that AI-driven tools are used responsibly and ethically is vital for maintaining public trust in public health initiatives and ensuring equitable outcomes for all members of society.
Communicate effectively to inform and educate people about health, factors that influence it, and how to improve it
The integration of AI into health education and communication has the potential to transform how people are informed, educated, and empowered about health issues. AI-driven tools can enable personalized health education, improve access to reliable health information, and facilitate community engagement. However, there are also challenges and risks associated with the use of AI in health education, including concerns about biased algorithms, digital divide, and the spread of misinformation.
Pros:
Personalized health education: AI can be used to develop personalized health education programs tailored to an individual’s unique needs, preferences, and risk factors. This targeted approach can lead to more effective health education interventions and empower individuals to take control of their health.
Improved access to reliable health information: AI-driven chatbots and virtual assistants can provide people with instant access to accurate and reliable health information, helping them make informed decisions about their health. This can improve health literacy and empower individuals to be proactive in managing their health and well-being.
Enhanced community engagement: AI can help public health professionals identify and engage with key community stakeholders, facilitating the development of more targeted and effective health communication campaigns. This can lead to greater community involvement in public health initiatives and improved health outcomes.
Real-time monitoring of public health communication: AI-driven tools can be used to analyze social media and other digital platforms to assess the impact of health communication campaigns in real-time. This can enable public health professionals to adapt their messaging and strategies more rapidly, leading to more effective health communication efforts.
Cons:
Biased algorithms: If AI-driven health education tools are developed using biased or unrepresentative data, they may perpetuate existing health disparities and lead to less effective health education interventions for certain populations. Ensuring that AI models are trained on diverse and representative data is crucial for promoting equity in health education.
Digital divide: The increasing reliance on digital tools for health education may exacerbate the digital divide, leaving individuals with limited access to technology or digital literacy skills at a disadvantage. Addressing this disparity is essential to ensure that all individuals can benefit from AI-driven health education initiatives.
Spread of misinformation: AI-driven tools can inadvertently contribute to the spread of health misinformation if they are not properly designed, monitored, or regulated. Ensuring that AI-driven health education tools provide accurate and reliable information is critical for maintaining public trust and promoting informed decision-making.
Loss of human touch: Overreliance on AI-driven tools for health education may lead to a loss of human touch and personal connections, which are often essential for effective health communication. Balancing the use of AI-driven tools with interpersonal communication is key to ensuring that health education efforts are both efficient and engaging.
Strengthen, support, and mobilize communities and partnerships to improve health
AI has the potential to transform how community partnerships are mobilized to identify and address health problems. AI-driven tools can facilitate collaboration, enhance resource allocation, and enable more targeted interventions. However, there are also challenges associated with the use of AI in mobilizing community partnerships, including concerns about privacy, trust, and equitable resource distribution.
Pros:
Enhanced collaboration: AI can help public health professionals identify and engage with potential community partners, fostering collaboration and facilitating the sharing of resources, expertise, and knowledge. This can lead to more effective and coordinated efforts to address health problems at the community level.
Data-driven resource allocation: AI-driven tools can analyze community health data to identify areas with the greatest need for resources and support, enabling more targeted and equitable resource allocation. This can improve the overall efficiency and effectiveness of community-based interventions.
Identifying priority health issues: AI can process large amounts of data to identify the most pressing health problems within specific communities, enabling public health professionals and community partners to focus their efforts on the issues that are most relevant and impactful.
Strengthening community networks: AI-driven tools can help public health professionals and community partners identify gaps in community networks and develop strategies to strengthen these networks, leading to more resilient and empowered communities.
Cons:
Privacy concerns: The use of AI-driven tools for community mobilization and data analysis raises concerns about individual privacy and data security. Ensuring that personal and community data is collected, stored, and analyzed in a secure and ethical manner is essential to maintain public trust in public health initiatives.
Trust and relationship-building: Overreliance on AI-driven tools for community mobilization may undermine the importance of human interaction and relationship-building in fostering strong community partnerships. Balancing the use of AI with interpersonal communication and engagement is crucial for establishing trust and collaboration among community partners.
Equitable resource distribution: If AI-driven tools are not designed and implemented with equity in mind, they may exacerbate existing disparities in resource allocation and access to public health services. Ensuring that AI-driven approaches to community mobilization prioritize equity and inclusivity is critical for promoting health equity.
Ensuring cultural competence: AI-driven tools must be designed and implemented with cultural competence in mind to ensure that they are effective and appropriate for diverse communities. Failing to account for cultural differences in AI-driven community mobilization efforts can lead to unintended consequences and reduced effectiveness of public health interventions.
Create, champion, and implement policies, plans, and laws that impact health
AI has the potential to significantly influence the development of policies and plans that support individual and community health efforts. AI-driven tools can enable more evidence-based decision-making, facilitate stakeholder engagement, and optimize resource allocation. However, there are also challenges and risks associated with the use of AI in policy and planning, including concerns about biased algorithms, transparency, and accountability.
Pros:
Evidence-based decision-making: AI can analyze vast amounts of data to identify patterns and trends that inform policy and planning decisions, leading to more evidence-based and effective public health strategies.
Stakeholder engagement: AI-driven tools can help public health professionals identify and engage with key stakeholders, facilitating more inclusive and collaborative policy-making processes.
Optimized resource allocation: AI can be used to analyze data on resource utilization and the effectiveness of public health interventions, enabling more efficient and targeted allocation of resources for policy and planning efforts.
Adaptive policy-making: AI can help public health professionals monitor and evaluate the impact of policies and plans in real-time, allowing for more adaptive and responsive policy-making processes.
Cons:
Biased algorithms: If AI-driven tools used for policy and planning are developed using biased or unrepresentative data, they may lead to policies and plans that perpetuate existing health disparities or create new ones. Ensuring that AI models are trained on diverse and representative data is crucial for promoting equity in public health policy and planning.
Transparency and accountability: The use of AI-driven tools in policy and planning may raise concerns about transparency and accountability, as the decision-making processes underlying AI models can be complex and difficult to understand. Ensuring that AI-driven policy-making processes are transparent and accountable is essential for maintaining public trust in public health initiatives.
Ethical considerations: The use of AI in policy and planning raises ethical concerns, such as the potential for AI-driven tools to prioritize efficiency over the well-being of individuals and communities. Addressing these ethical considerations and ensuring that AI-driven policy-making processes prioritize the health and well-being of all members of society is critical.
Balancing human expertise with AI-driven insights: Overreliance on AI-driven tools for policy and planning may lead to a decline in human expertise and judgment, potentially negatively impacting the development and implementation of public health policies and plans. Striking a balance between leveraging AI-driven insights and valuing human judgment is essential for ensuring the best possible public health outcomes.
Utilize legal and regulatory actions designed to improve and protect the public’s health
AI has the potential to greatly enhance the enforcement of laws and regulations that protect health and ensure safety. AI-driven tools can improve compliance monitoring, streamline enforcement processes, and enable more targeted interventions. However, there are also challenges and risks associated with the use of AI in enforcement, including concerns about privacy, fairness, and the potential for overreliance on technology.
Pros:
Improved compliance monitoring: AI can be used to automate the monitoring of compliance with public health laws and regulations, enabling more efficient and effective enforcement efforts.
Streamlined enforcement processes: AI-driven tools can help public health professionals prioritize enforcement actions based on risk, ensuring that resources are directed to where they are needed most.
Targeted interventions: AI can be used to analyze data on compliance patterns and identify areas or populations at high risk for noncompliance, enabling more targeted and effective enforcement efforts.
Enhanced data analysis: AI-driven tools can analyze large amounts of data from various sources to identify trends and patterns in compliance, providing valuable insights to inform the development and implementation of public health laws and regulations.
Cons:
Privacy concerns: The use of AI-driven tools for enforcement raises concerns about individual privacy and data security. Ensuring that personal and community data is collected, stored, and analyzed in a secure and ethical manner is essential to maintain public trust in public health initiatives.
Fairness and bias: If AI-driven tools used for enforcement are developed using biased or unrepresentative data, they may result in unfair enforcement practices that disproportionately affect certain populations. Ensuring that AI models are trained on diverse and representative data is crucial for promoting fairness and equity in public health enforcement efforts.
Overreliance on technology: The increasing reliance on AI-driven tools for enforcement may lead to a decline in human expertise and judgment, potentially negatively impacting public health enforcement efforts. Balancing the use of AI-driven tools with human expertise is essential for ensuring effective enforcement of public health laws and regulations.
Legal and ethical considerations: The use of AI in enforcement raises legal and ethical concerns, such as the potential for AI-driven tools to infringe on individual rights and liberties. Addressing these concerns and ensuring that AI-driven enforcement efforts adhere to legal and ethical standards is critical for maintaining public trust in public health initiatives.
Assure an effective system that enables equitable access to the individual services and care needed to be healthy
AI has the potential to revolutionize how people are linked to needed personal health services and how health care is provided when otherwise unavailable. AI-driven tools can improve access to care, enhance care coordination, and optimize resource allocation. However, there are also challenges and risks associated with the use of AI in this essential public health service, including concerns about privacy, equity, and the potential for overreliance on technology.
Pros:
Improved access to care: AI-driven tools, such as telemedicine platforms and virtual health assistants, can help connect individuals to needed personal health services, particularly in remote or underserved areas. This can improve access to care and reduce health disparities.
Enhanced care coordination: AI can be used to streamline care coordination processes, ensuring that individuals receive timely and appropriate care from multiple providers. This can lead to better health outcomes and a more efficient use of health care resources.
Optimized resource allocation: AI can help public health professionals identify areas with the greatest need for health care services and allocate resources accordingly. This can improve the overall efficiency and effectiveness of health care delivery.
Personalized care: AI-driven tools can be used to develop personalized care plans based on an individual’s unique needs, preferences, and risk factors. This can lead to more effective and patient-centered care.
Cons:
Privacy concerns: The use of AI-driven tools for linking people to personal health services raises concerns about individual privacy and data security. Ensuring that personal health data is collected, stored, and analyzed in a secure and ethical manner is essential to maintain public trust in public health initiatives.
Equity concerns: The increasing reliance on digital tools for accessing health care services may exacerbate existing health disparities, particularly for individuals with limited access to technology or digital literacy skills. Addressing this issue is essential to ensure that all individuals can benefit from AI-driven health care initiatives.
Overreliance on technology: The increasing dependence on AI-driven tools for connecting individuals to personal health services may lead to a decline in human expertise and judgment, potentially negatively impacting patient care. Balancing the use of AI-driven tools with human expertise is essential for ensuring the best possible health outcomes.
Ensuring quality of care: As AI-driven tools become more integrated into the health care system, it is important to ensure that they are implemented and monitored in a manner that maintains or improves the quality of care provided to individuals.
Build and support a diverse and skilled public health workforce
AI has the potential to transform the public health and personal health care workforce by enhancing workforce development, automating routine tasks, and enabling more efficient workforce planning. However, there are also challenges and risks associated with the use of AI in this essential public health service, including concerns about job displacement, the potential for overreliance on technology, and the need for ongoing education and training.
Pros:
Enhanced workforce development: AI-driven tools can be used to identify skills gaps and develop targeted training and education programs for public health and personal health care professionals, leading to a more competent and effective workforce.
Automation of routine tasks: AI can automate repetitive and time-consuming tasks, such as data entry and reporting, allowing public health and personal health care professionals to focus on more complex and high-value tasks.
Efficient workforce planning: AI can be used to analyze workforce data and identify trends, enabling more effective workforce planning and resource allocation.
Continuous learning and improvement: AI-driven tools can facilitate ongoing education and training for public health and personal health care professionals, ensuring that they stay up-to-date with the latest knowledge and best practices.
Cons:
Job displacement: The increasing use of AI-driven tools in public health and personal health care may lead to job displacement, particularly for individuals working in roles that are heavily focused on routine tasks.
Overreliance on technology: The increasing reliance on AI-driven tools in the workforce may lead to a decline in human expertise and judgment, potentially negatively impacting patient care and public health outcomes. Balancing the use of AI-driven tools with human expertise is essential for ensuring the best possible health outcomes.
Ongoing education and training: As AI-driven tools become more integrated into the public health and personal health care workforce, ongoing education and training will be necessary to ensure that professionals have the skills and knowledge needed to effectively use these tools. This may require additional resources and investment in workforce development initiatives.
Ethical considerations: The use of AI in the public health and personal health care workforce raises ethical concerns, such as the potential for AI-driven tools to prioritize efficiency over the well-being of individuals and communities. Addressing these ethical considerations and ensuring that AI-driven workforce initiatives prioritize the health and well-being of all members of society is critical.
Improve and innovate public health functions through ongoing evaluation, research, and continuous quality improvement
AI has the potential to significantly improve the evaluation of the effectiveness, accessibility, and quality of personal and population-based health services. AI-driven tools can enable more robust data analysis, facilitate real-time monitoring, and support continuous quality improvement. However, there are also challenges and risks associated with the use of AI in this essential public health service, including concerns about biased algorithms, privacy, and the potential for overreliance on technology.
Pros:
Robust data analysis: AI can be used to analyze large amounts of data from various sources, providing valuable insights into the effectiveness, accessibility, and quality of health services. This can help public health professionals identify areas for improvement and inform the development and implementation of evidence-based interventions.
Real-time monitoring: AI-driven tools can enable real-time monitoring of health service delivery, allowing public health professionals to quickly identify and address issues related to effectiveness, accessibility, and quality.
Continuous quality improvement: AI can help facilitate continuous quality improvement processes, ensuring that health services are regularly evaluated and updated to reflect the latest evidence and best practices.
Enhanced patient experience: AI-driven tools can be used to collect and analyze patient feedback, enabling public health professionals to better understand and address patient needs and preferences.
Cons:
Biased algorithms: If AI-driven tools used for evaluating health services are developed using biased or unrepresentative data, they may lead to inaccurate or misleading evaluations. Ensuring that AI models are trained on diverse and representative data is crucial for promoting accurate and unbiased evaluations of health services.
Privacy concerns: The use of AI-driven tools for evaluating health services raises concerns about individual privacy and data security. Ensuring that personal and community data is collected, stored, and analyzed in a secure and ethical manner is essential to maintain public trust in public health initiatives.
Overreliance on technology: The increasing reliance on AI-driven tools for evaluating health services may lead to a decline in human expertise and judgment, potentially negatively impacting the quality and effectiveness of health service evaluations. Balancing the use of AI-driven tools with human expertise is essential for ensuring accurate and meaningful evaluations of health services.
Ethical considerations: The use of AI in evaluating health services raises ethical concerns, such as the potential for AI-driven tools to prioritize efficiency over the well-being of individuals and communities. Addressing these ethical considerations and ensuring that AI-driven evaluation initiatives prioritize the health and well-being of all members of society is critical.
Build and maintain a strong organizational infrastructure for public health
AI has the potential to significantly improve the evaluation of the effectiveness, accessibility, and quality of personal and population-based health services. AI-driven tools can enable more robust data analysis, facilitate real-time monitoring, and support continuous quality improvement. However, there are also challenges and risks associated with the use of AI in this essential public health service, including concerns about biased algorithms, privacy, and the potential for overreliance on technology.
Pros:
Robust data analysis: AI can be used to analyze large amounts of data from various sources, providing valuable insights into the effectiveness, accessibility, and quality of health services. This can help public health professionals identify areas for improvement and inform the development and implementation of evidence-based interventions.
Real-time monitoring: AI-driven tools can enable real-time monitoring of health service delivery, allowing public health professionals to quickly identify and address issues related to effectiveness, accessibility, and quality.
Continuous quality improvement: AI can help facilitate continuous quality improvement processes, ensuring that health services are regularly evaluated and updated to reflect the latest evidence and best practices.
Enhanced patient experience: AI-driven tools can be used to collect and analyze patient feedback, enabling public health professionals to better understand and address patient needs and preferences.
Cons:
Biased algorithms: If AI-driven tools used for evaluating health services are developed using biased or unrepresentative data, they may lead to inaccurate or misleading evaluations. Ensuring that AI models are trained on diverse and representative data is crucial for promoting accurate and unbiased evaluations of health services.
Privacy concerns: The use of AI-driven tools for evaluating health services raises concerns about individual privacy and data security. Ensuring that personal and community data is collected, stored, and analyzed in a secure and ethical manner is essential to maintain public trust in public health initiatives.
Overreliance on technology: The increasing reliance on AI-driven tools for evaluating health services may lead to a decline in human expertise and judgment, potentially negatively impacting the quality and effectiveness of health service evaluations. Balancing the use of AI-driven tools with human expertise is essential for ensuring accurate and meaningful evaluations of health services.
Ethical considerations: The use of AI in evaluating health services raises ethical concerns, such as the potential for AI-driven tools to prioritize efficiency over the well-being of individuals and communities. Addressing these ethical considerations and ensuring that AI-driven evaluation initiatives prioritize the health and well-being of all members of society is critical.
Preparing for the future
Public health professionals can take several steps to prepare for the forthcoming changes brought by AI. These include staying informed about AI advancements and their implications, developing digital literacy and data analysis skills, advocating for ethical AI practices, and fostering interdisciplinary collaborations between public health professionals and AI experts. By proactively embracing AI’s potential while addressing its challenges, public health professionals can ensure a future in which AI enhances public health outcomes and reduces disparities.
Looking forward, public health professionals must acknowledge both the transformative potential of AI and the challenges it presents. In addition to the steps mentioned earlier, public health professionals can engage in several activities to ensure that AI is effectively and ethically integrated into the public health sector.
Encourage collaboration between disciplines: By fostering interdisciplinary collaborations, public health professionals can leverage the expertise of AI specialists, data scientists, and technologists to address critical public health issues. This exchange of knowledge can lead to more robust AI-driven solutions and improved health outcomes.
Prioritize diversity and inclusivity in AI development: Ensuring that AI models and tools are developed using diverse and inclusive data sets is crucial for reducing the risk of biased outcomes. Public health professionals should advocate for the collection and use of representative data to create AI solutions that are more equitable and effective.
Emphasize lifelong learning and skill development: As AI continues to advance, public health professionals must prioritize the development of new skills, including digital literacy, data analysis, and AI-related competencies. By embracing lifelong learning, public health professionals can stay relevant in an evolving landscape and continue to contribute meaningfully to public health efforts.
Advocate for ethical AI use and regulations: Public health professionals should actively participate in discussions about AI ethics and regulation, advocating for responsible AI use that prioritizes privacy, fairness, and transparency. They should work with policymakers and stakeholders to ensure that AI is used in a manner that benefits all members of society.
Promote community engagement and education: To build public trust in AI-driven public health initiatives, professionals should engage communities in decision-making processes and promote education about AI’s role in public health. By demystifying AI and fostering open dialogue, public health professionals can help communities understand the benefits and risks associated with AI-driven solutions.
By addressing these key areas, public health professionals can play a pivotal role in ensuring that AI is harnessed effectively and ethically to improve public health outcomes, reduce disparities, and enhance the well-being of communities worldwide.