December 14, 2022

Better Data Collection Essential to Understanding and Addressing Health Inequities

AUTHORS


Lindsay Petersen
Senior Manager, Care Redesign

TOPLINES


Addressing health inequities has become a top priority for many employers, purchasers and health care providers. The ability to effectively collect a range of data points about patients and the care they receive is essential.
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The COVID-19 pandemic exposed and exacerbated the weaknesses of the U.S. health care system and highlighted long-standing inequities for minority communities. Highlighted during this period was the profound impact of economic stability, education, social and community life, one’s neighborhood and access to high-quality health care—social determinants of health—on the overall health and well-being of communities.

As a result, addressing health inequities has become a top priority for many employers, purchasers and health care providers. The ability to effectively collect a range of data points about patients and the care they receive is an essential component to creating meaningful change and ensuring populations achieve their full health potential.

Looking at health quality data by race, ethnicity, language and other patient characteristics, is crucial for understanding how long-standing systems of privilege and oppression impact the health of minority populations and communities. However, patient self-reported race, ethnicity and language (REaL) data across health insurance markets is widely variable and overall limited. While race and ethnicity data in California’s Medicaid program (called Medi-Cal) is broadly available likely because of legislation requiring health plans to collect this information starting in 2009, corresponding data for the majority of patients who receive health benefits through the commercial market – via employers or on the private market –is low or absent.

These limitations of known race and ethnicity data hinder the ability to see where disparities exist and for the health system to react with meaningful interventions. For health plans and large employers and purchasers, who provide health benefits for more than half the U.S., it is crucial to uncover variation in the access to care and the quality and experience of care being provided.

With better self-reported patient demographic information, employers, purchasers, payers and providers can tie this data to health care access, quality, patient experience and outcomes to illuminate exactly where disparities exist. These insights can enable tailored interventions and support for improvement.

How to Improve Data Collection

Legislation, Policy and Regulation

Legislation and regulations can incentivize or require health plans, providers and other health system organizations to increase the collection and quality of self-reported demographic data. Legislation and statute also have the potential to enforce standardization for data fields and definitions, which enables largescale purchasers of health care to align with their health plan and provider industry partners and enhance their ability to share, aggregate or disaggregate data to identify trends and implement plans for improvement.

It is crucial to ensure that national and state standards do not contradict each other.

Contracting and Business Relationships

Contracting requirements and incentives as part of large-scale public and private purchaser and payer programs can increase the collection, reporting and use of REaL data and thereby bolster efforts to mitigate disparities. Large purchasers could add incentive payouts if plans are able to stratify measures across self-reported REaL data. Health plans, provider organizations and other payers that contract within the health system can use incentive payouts for better data collection and stratification and other efforts to reduce disparities. Another approach is to build tiered networks that point patients to providers who have proven to be stronger at collecting, reporting and using REaL data.

It is important for purchasers and payers to avoid siloed initiatives that conflict with each other.

Organizational Leadership, Systems Structure and Culture

Organizations that pay for services at the point of care (e.g., health plans and independent physician associations, or IPAs) have the potential to increase REaL data collection, reporting and use by assessing and enhancing data collection opportunities, sharing data internally and creating a culture that values the collection of this information. This starts with organizational leadership. It is important to normalize data collection into regular workflows to improve the quality and ensure the most accurate information possible.

Purchasers, health plans and provider organizations can increase patient self-reporting by increasing awareness of how the data will be used and educating enrollment counselors and other staff with direct patient interaction on why it is important to collect this data.

Certification Requirements

The National Committee for Quality Assurance (NCQA) has required plans to report their percentage of self-reported REaL data for certain key measures, with a goal of 80% self-reported data. Additional accrediting organizations, purchasers and others could adopt similar certification requirements to support reporting and stratification for the same measures and self-reported data goals as NCQA. This would increase the consequences for not aligning and support the overall goal of greater availability of self-reported REaL data.

 

Read more in our latest issue brief.

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