Using trustworthy AI and data analytics to increase health equity and decrease health inequalities.
The biggest problem currently facing US public health professionals is achieving health equity. Despite allocating a much higher percentage of its GDP to healthcare, the country was placed last among high-income countries in access to healthcare and equity in a 2021 study from the Commonwealth Fund.
Healthcare disparities are strongly related to issues of access, cost, and quality of care that are related to race, ethnicity, gender, and other demographic and socioeconomic factors. The COVID-19 pandemic brought health inequities in the US into sharp focus. Analyses of federal, state, and municipal healthcare statistics reveal that people of color bore a disproportionate burden of cases and deaths.
However, there is good news. The current crises showed how important it is to pay more attention to health equity and how using data-driven technology may help ensure equity for disadvantaged populations.
IBM and the Rhode Island Department of Health worked together in 2020 to identify new and existing data patterns to support the agency’s overall response to the health problem. Real-time, data-driven decisions resulting from this effort were able to identify inequities caused by pandemics, such as a lack of vaccine access. The project ultimately resulted in more equitable emergency response services in the areas of Rhode Island that most needed them.
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The data-leveraging lessons discovered during the pandemic are already being applied by state health departments around the nation to a variety of public health problems affecting underserved groups. Food hardship, unplanned pregnancies, increasing suicide rates, and opioid addiction are among the challenges that health authorities are concentrating on. Leaders may now make decisions that are smarter, quicker, and more effective to advance health equity and improve public health outcomes.
Creating a citizen 360 view using data and AI
To ensure that quality data can be accessible by the appropriate parties at the appropriate times, regardless of where it lives, the journey begins with the development of a data fabric architecture. The important thing is to ensure that all of this data is openly managed and protected in terms of privacy and security.
By utilizing intelligent and automated processes, a data fabric makes it easier to integrate different data pipelines and cloud environments from beginning to finish. Additionally, it offers a solid framework for 360-degree views of customers, or in this case, citizens rather than customers.
A 360-degree perspective of consumers or citizens provides a complete, comprehensive picture of a person based on data gathered from all interaction points, whether in B2B or B2C circles. By resulting in more successful outcomes and more individualized customer experiences, this generates corporate value. For instance, this data architecture helps state health personnel to comprehend the situation of healthcare generally, thereby enhancing patient care and addressing inequities.
Data and AI literacy through visualization and storytelling
How to make sense of all that data is a problem that both private and public companies face when collecting huge amounts of data.
Data visualization, the process of analyzing huge amounts of data and communicating the results in a visual context, is a component of data storytelling. However, effective storytelling must go beyond simply displaying facts in the form of graphs, tables, and charts.
For instance, to effectively convey their facts to their whole workforce, state health departments, which are made up of some stakeholders and players, must develop a compelling storyline and consistent messaging.
Prioritizing the needs of the citizen
Predictive analytics for insights that can address some of the most pressing health issues, such as hunger and food insecurity, are also made possible by data and reliable AI.
For instance, despite a substantial rise in poverty over the past ten years, fewer people have been enrolling in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), a federal food aid program run by state health departments and local organizations. The slow pace of modernization—all WIC payments were still issued as paper vouchers until recently—and the ongoing stigma associated with receiving federal help are thought to be contributing reasons. Finding and treating these variables, among others, is necessary for aiding.
The WIC Enrollment Collaboration Act of 2020 mandates that state health departments count families who are eligible for WIC but have not yet enrolled. That count can be helped by a data fabric with a 360-degree perspective. Additionally, it can assist states in developing and implementing referral procedures and running extensive outreach programs (also detailed in the Act). Data can be used by states cooperatively to evaluate and enhance WIC access and more effectively reduce food hardship.
State health, education, and mental health departments around the US are utilizing data to fight other health concerns, such as the opioid and suicide epidemics. A robust public health crisis response system that enables cross-branch and interstate cooperation is provided via a centralized data hub. Such multifaceted initiatives are bridging the essential information gap, explaining the how and why of inequities, and opening the way to greater health equity for all.
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