Creating a holistic 360-degree “citizen” view with data and AI

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Achieving health equity is perhaps the greatest challenge facing US public health officials today. In a 2021 report released by the Commonwealth Fund, the nation ranked last among high-income countries in access to healthcare and equity, despite spending a far greater share of its GDP on healthcare.

Healthcare disparities are closely linked to race, ethnicity, gender and other demographic and socioeconomic issues surrounding access, cost and quality of care. Health inequities in the US came into sharp relief during the COVID-19 pandemic: Analyses of federal, state and local healthcare data show that people of color experienced a disproportionate burden of cases and deaths.

But there is promising news. The recent crisis not only highlighted the critical need to focus more on health equity but also revealed how tapping into data-driven technologies can better ensure equity for marginalized groups.

In 2020, IBM collaborated with the Rhode Island Department of Health, uncovering existing and emerging data patterns to aid the agency’s overall response to the health crisis. This work resulted in real-time, data-driven decisions that identified pandemic-fueled disparities such as lack of access to vaccines. Ultimately, the project led to more equitable emergency response services in the Rhode Island regions that needed it most.

Today state health departments around the country are taking the data-leveraging lessons learned during the pandemic and applying them to an array of public health crises affecting underserved groups. Health departments are focusing on issues such as food insecurity, unwanted pregnancies, increased suicide rates and opioid addiction. Thanks to innovations in data analytics and AI, leaders can make smarter, faster and more efficient decisions to improve public health outcomes and advance health equity.

Learn how you can take advantage of your data so users make faster, better decisions using the right architecture.

The journey begins with building a data fabric architecture to ensure quality data can be accessed by the right people at the right time, no matter where it resides. The key is making sure all this data is transparent and responsibly governed for privacy and security.

A data fabric facilitates the end-to-end integration of various data pipelines and cloud environments by using intelligent and automated systems. It also provides a strong foundation for 360-degree views of customers, or in this case citizens rather than customers.

In B2B or B2C circles, a 360-degree view of customers or citizens offers a holistic, comprehensive picture of a person based on data collected from all touch points. This drives business value by creating more effective outcomes as well as more personalized customer experiences. For instance, this data infrastructure enables a state health workforce to better understand the overall healthcare landscape and subsequently improve individual care and address inequities.

Collecting massive amounts of data presents a common issue for both private and public enterprises: how to make sense of all that data.

Part of data storytelling involves data visualization, the process of analyzing large amounts of data and communicating the results in a visual context. But strong storytelling must go beyond presenting data in the form of charts, graphs and tables.

For instance, state health departments comprising many stakeholders and players need to create a compelling storyline and consistent messaging around their data, so they can communicate it effectively to their entire workforce.

Data and trustworthy AI also provide predictive analytics for insights that can solve some of the most pressing health issues, including hunger and food insecurity.

For example, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), a federal food assistance program that operates through state health departments and local agencies, has seen decreasing enrollment over the past decade despite a sharp rebound in poverty levels. Suspected factors include slow modernization — until recently, all WIC benefits were still delivered as paper vouchers — and persistent stigma against federal assistance. Providing assistance depends on identifying and addressing these and other factors.

The WIC Enrollment Collaboration Act of 2020 calls for state health departments to count unenrolled WIC-eligible families. A data fabric with a 360-degreee view can help that count. It can also help states build and deploy referral mechanisms and conduct a comprehensive outreach campaign (also detailed in the Act). Working together, states can use data to assess and improve access to WIC and better limit food hardship.

Throughout the US, state departments of health, education and behavioral health are using data to overcome other health crises, including the opioid and suicide epidemics. A centralized data hub provides a powerful public health crisis response system that allows for collaboration across government branches and state lines. Such multi-pronged efforts are closing the gap in critical information, shedding light on how and why disparities occur and paving the way to better health equity for all.

Today IBM is working with state health departments to accelerate their digital transformations in the areas of overall governance, operations, automation, data insights and more.