Commentary

Data analytics and informatics are key to improving health outcomes for CMS

It is essential to understand the current state of health disparities, and how policymakers can leverage data-driven insights to create more targeted programs.

Darryl W. Roberts

September 5, 2024 2:47 pm

6 min read

Health disparities remain a prime area of interest for federal health agencies, with a focus on initiatives that help identify gaps in care and find ways to bridge those divides. While there are a lot of moving parts that need to come together to improve health outcomes, data analytics will continue to play an important part in orchestrating change.

Before we dive in any deeper, it’s instructive to define health disparities and those most affected. The Centers for Disease Control and Prevention define health disparities as “preventable differences in the burden of disease, injury, violence or opportunities to achieve optimal health that are experienced by socially disadvantaged populations.”

This includes higher incidence and earlier onset of disease, self-reported health-related quality of life, and premature or excessive mortality from diseases where population rates differ. Within the United States, racial, ethnic and economic disparities in health and health care are a persistent challenge across the country.

To affect change, it is essential to understand the current state of health disparities as well as the way policymakers can leverage data-driven insights to create more targeted and effective programs.

Understanding health disparities

A recent Kaiser Family Foundation report found that more than twice as many nonelderly Hispanic adults (36%) report not having a usual doctor or healthcare provider compared to Black and White nonelderly adults (both at 17%). Similarly, nonelderly Hispanic adults are more likely to go without access to health care (21%) compared to Blacks (11%) and whites (14%).

Also, in 2020, a scientific statement from the American Health Association identified poverty as the leading health disparity affecting heart-related outcomes, noting that people living in higher-income neighborhoods report lower levels of stress, anxiety and obesity, as well as fewer health-related comorbidities. Low income correlated with higher numbers of hospitalizations, outpatient medical visits and other clinical events.

Disparities impact a wide range of people in various stages of life, including expecting mothers. Although maternal mortality in the U.S. is high for an industrialized country, it remains an exceedingly rare event. In 2022, 817 American women died during pregnancy or during the postpartum period. As an attempt to reduce this occurrence, CMS proposed policies in July to improve health outcomes, access to care and support underserved communities.

For the first time, CMS is proposing baseline health and safety requirements for hospitals and critical access hospitals for obstetrical services. The agency will engage in maternal quality improvement efforts, such as setting standards for staffing, and care delivery.

CMS will also work to ensure readiness of emergency services, develop or promote best practices for transfer protocols, and monitor annual staff training on evidence-based maternal health practices and cultural competencies.

This is a step in the right direction for a process that impacts the future of the U.S. To manage these varied and highly critical challenges and topics, federal health agencies, including CMS, must harness the power of data and data-based strategies.

Excellent data analyses can help the federal government and health community make sense of where underserved communities reside, develop priorities from among the innumerable needs, and recommend the most efficient and effective ways to address them.

Technology helps teams better serve citizens

Health informatics and analytics can help healthcare providers deliver better care for underserved patients by providing access to data and then turning that data into useful, actionable information and knowledge.

Both domains look at how to use medical data, information and knowledge effectively for scientific inquiry, problem-solving and decision making. All data secured and used for research is motivated by efforts to improve human health. Informatics and analytics also inform public policy by providing decision makers with data-driven insights, access to public use files and technical documents, support for clearance processes, identification of helpful publications and more.

Data analytics and health informatics strengthen the government’s focus on reducing avoidable inequalities and eliminating health disparities. This approach also enables organizations to better understand where disparities exist and what’s needed to close essential gaps.

Most importantly, analytics and informatics can greatly support efforts once data-based information is translated throughout the bureaucratic process to inform and help guide public health policy.

The future process

When it comes to the future of healthcare informatics, the federal health community must lean on data management and analysis to drive its efforts to improve access to care. This includes details on where and how the federal government can best support specific communities across the nation, indicating potential food deserts too.

Within healthcare informatics, the federal government can improve access to care by focusing on two core areas — artificial intelligence (including large language models [LLMs]) and proper data management.

AI is a growing topic and form of technology that has made a large impact on various industries for many years. This has been particularly true over the past few years since industries have achieved the computing capacity necessary to use it effectively. Government needs to remain aware of what AI can do and its future implications.

Within health informatics, AI can help health informaticians by discovering new information and organizing that information to support the analysis of huge data stores. Together with AI, they can translate the findings into actionable information. Computer-human collaboration is essential because it gives a human-based perspective on what the AI outcomes deliver, helping leaders and decision makers better digest critical information.

The main translation occurs between government leaders, informaticians and the healthcare providers/end users. AI can do a lot, but it can only get you so far without the involvement of a solid health informatician who can support the translation process.

Today, there is so much data throughout the federal heath space that decision makers can pull from to inform an answer. This can lead to a lot of “yes” answers to healthcare questions because the current volume of data can allow for inaccurate curation of those answers.

A health informatician must truly understand all relevant data and serve as a sherpa, reviewing and guiding the data-based results to suggest approaches based on an accurate understanding of the information at hand. This accuracy, combined with efficient data management and informatics, can guide organizations to avoid an incorrect “yes.” Instead, they can use the data to make informed, defensible decisions.

Healthcare disparities are one of the great challenges of our time — using technology and data-based approaches can help close gaps and help teams better serve citizens. All the innovation in medicine, healthcare and technology doesn’t mean much if clinicians cannot use it to deliver the best possible care to people in underserved communities. Data analytics and health informatics provide the catalyst to drive change that will make healthcare more effective, efficient and accessible for all.

Darryl W. Roberts is vice president of health informatics solutions at RELI Group.