Data modernization is crucial to making public health more equitable. A robust, state-of-the-art data system can provide deeper insights into health disparities, guide targeted interventions, and improve how effectively resources are allocated among patients and their communities.
But to advance health equity, it’s essential to collect accurate, consistent data that provides deeper insights into marginalized, and often overlooked, populations. Such data sets include race and ethnicity; sexual orientation and gender identity (SOGI); and the social determinants of health (SDOH), such as housing, food insecurity, or income levels. While more organizations and agencies are prioritizing these types of data, noteworthy gaps remain.
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Certain health equity data – such as race and ethnicity – have long been collected, albeit with gaps. An analysis of 20 years of US clinical trial data found that race was indicated for only 43% of patients. Even when race and ethnicity are indicated, the data collected often are too limited, clumping together different communities under single headings. Take the race option “Black.” This category captures several distinct communities in a single category (e.g., US-born African American, Haitian, African immigrant), overlooking potentially key health-related differences between the sub-populations.
Data on SOGI and SDOH, on the other hand, are rarely collected. Most federal health reports do not examine the sexual orientation or gender identity of those being surveyed. However, when such information has been collected, it has frequently demonstrated poorer health outcomes for sexual and gender minorities, something the White House acknowledged and urged federal agencies to address. It is also still unusual for these reports to include information on SDOH, even though the CDC indicated SDOH has the strongest impact on health in its HI-5 program, an initiative that highlights non-clinical approaches to improving public health.
While more attention is being paid to improving equity-related data collection, including initiatives at the Office of the National Coordinator for Health Information Technology, more needs to be done. The current data gaps have serious implications, exacerbating existing health disparities and creating misleading, or even inaccurate, health guidance. While the stakes are high, these gaps can be addressed by investing in people – who need proper training and incentives to complete data accurately – and by technology – which needs to provide easy data collection, sharing and analysis. By bridging this data divide, public health professionals can build towards a more equitable and effective system.
Investing in people
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Bridging data gaps and improving health equity means understanding how data are collected, analyzed, shared, and disseminated. This begins with the workforce that collects and inputs the data. These employees need proper training, reinforcement, and acknowledgement.
Knowing how to ask the right questions is vital to improving data equity. Staff should receive regular education and feedback on the importance and impact of data collection. Healthcare providers will be more likely to collect data if they are aware of how it helps their patients. Training should also explore how medical and public health professionals can create an environment where individuals feel more comfortable disclosing sensitive information, such as their sexual orientation.
Investing in technology
Once data are collected, utilizing the proper technology is essential for effectively sharing, analyzing, and leveraging insights to improve health outcomes. Sharing data between and across health systems can fill in some of the existing gaps mentioned above, and a strong cloud architecture is a crucial building block. For example, medical records, often collected in doctors’ offices or hospitals, contain key health equity data, like race or SOGI data. But this data often stays within the walls of a doctor’s office. Creating a robust cloud architecture that allows aggregate medical record data to be shared between healthcare providers and public health officials would provide critical insights into population health and disease trends and create more informed public health guidelines. Luckily, efforts to improve data sharing are already underway. California is one of several states exploring how best to identify and share equity-related data from multiple sources.
Increasingly, artificial intelligence (AI) is being leveraged to analyze health equity data. AI can help identify health patterns, pinpointing elevated risks within specific populations for potential illnesses and enhancing overall patient care. AI tools can also facilitate real-time monitoring of health trends, aiding decision-makers in implementing timely and evidence-based strategies to improve overall public health outcomes. For example, Washington state has already incorporated AI into its analysis of data in its Health Equity Atlas and other resources.
However, healthcare data are, of course, incredibly personal, and patient protections need to be a priority. Health systems must be equipped with proper cybersecurity measures in place to secure and protect sensitive data. Such action is needed to address the concerns of the public and policy makers that confidential information may be inappropriately used.
A future with equity data
There is a common misconception that a data problem can be fixed with a one-time investment in hardware or software development. But data modernization and the promotion of health equity require a continued investment. Leaders need to make sustained investments to keep their IT staff and technology updated and to support personnel on the ground – such as epidemiologists and clinical and laboratory staff – without whom accurate data cannot be collected and analyzed. While data modernization does require an investment in upfront planning, designing, and implementing the technology, the value of the investment will be short-lived without ongoing support.
Despite the remaining challenges, a public health system equipped with strong data, relevant training staffing support, and effective IT infrastructure is within reach. Proper data collection, analysis, and utilization has the potential to address long-standing health disparities and propel our healthcare system forward to serve all communities. While this transformation will not happen overnight, a continued commitment to equity data will undoubtedly improve public health outcomes for all.
Photo: eichinger julien, Getty Images
John Auerbach is ICF’s primary federal health expert and thought leader within the company’s public sector business. John’s thought leadership advances ICF’s combination of proven domain and scientific expertise with leading-edge analytics and technology solutions to drive improved health outcomes for clients.
John came to ICF from the Centers for Disease Control and Prevention (CDC), where he most recently served as the director of intergovernmental and strategic affairs. In this role, he was the lead strategic advisor on CDC engagement with government agencies at the federal, state, local, tribal, and territorial levels, as well as public health and other external partners. He also had oversight of CDC’s health equity workgroup and served as the chief equity officer for CDC’s Covid-19 response. John is also the former president and CEO of Trust for America’s Health and the former public health commissioner for the Massachusetts Department of Public Health.
