Our Take

Take a Data-Driven Approach to Identifying and Reducing Disparities

25 Minute Read

Leaders across the health care industry increasingly recognize the need to reduce disparities in care access, cost, and quality. To reduce these disparities, organizations must fully leverage their patient and community data to:

• Identify top opportunities for reducing disparities and inform initiatives to address them

• Monitor efforts and evaluate the success of their initiatives in reducing disparities in the short and long term

• Make the case for prioritizing health equity with dedicated investment

 

The conventional wisdom

Leaders across the health care industry increasingly recognize the need to reduce disparities in care access, cost, and quality that impact patients based on their race, ethnicity, age, gender identity, sexual orientation, language, and other demographic and socioeconomic factors. Data is the backbone of any equity strategy, however most organizations have yet to fully leverage their internal data—from both quantitative and qualitative sources—in their approach to reducing disparities.

Traditionally, hospitals and health systems rely on their Community Health Needs Assessment (CHNA) and patient experience scores to identify inequitable care delivery and outcomes within their patient populations. However, most organizations that have this data fail to fully leverage it. As a result, many provider institutions struggle to identify which disparities their patients and community members are experiencing.

While CHNAs and patient experience scores can be helpful data sources, they are only part of the data set needed to identify and address disparities. Health systems must also include comprehensive data on patient demographics and social determinants of health (SDOH). However, this data is often inconsistently collected. Only one-third of health plans report having complete or partially complete data on race, and fewer than half reported complete or partially complete data on ethnicity and language needs, a pattern that is likely reflected in electronic health records (EHRs). Furthermore, only 24% of hospitals collect data on the five key SDOH outlined by CMS.

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Piecemeal collection of data is just one part of a larger, long-standing lack of contextual data that we need to target resources.
- Marcella Nunez-Smith, M.D., M.H.S.
Co-chair, White House Covid-19 Health Equity Task Force

 

Our take

Insufficient data collection and analysis prevent provider organizations from having a holistic view of their patient population, which makes it hard to identify and eliminate the disparities they face. Without a robust data-driven approach, it is difficult—if not impossible—to set clear, measurable goals for reducing disparities and sustain long-term progress.

Health equity interventions that are not targeted to a specific patient population or identified need are unlikely to yield a real impact on outcomes. This can not only harm trust with patients or the community but also impact the viability of the entire organizational investment in health equity. To make a real impact, organizations must demonstrate they value health equity by bringing the same strategic rigor to these investments as they do with any other investment.

Organizations that have a true interest in improving health equity must take a data-driven approach to reducing disparities. Just as with any strategic priority, organizations should start by leveraging data to identify the scope of disparities their patients and community face. This data is key to informing an organization’s investment in initiatives to address disparities. Taking a data-driven approach allows organizations to:

  • Identify top opportunities for reducing disparities and inform initiatives to address them
  • Monitor efforts and evaluate the success of their initiatives in reducing disparities in the short and long term
  • Make the case for prioritizing health equity with dedicated investment

One outcome of collecting robust data is that it helps combat the dual problems of denial and inaction. Many stakeholders struggle to accept that structural and provider bias are not only present but detrimental at their own organizations. Organization- and community-specific data collection provides incontrovertible, measurable proof of disparities in care delivery and within the community. This data helps reveal to staff and leaders that bias in all its forms has consequences not just in the communities that health care organizations serve, but also within the walls of the organization itself.

 

Six strategies of a data-driven approach

 

  • Strategy

    Capture a snapshot of patient identity via comprehensive demographic data

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  • Strategy

    Collect qualitative feedback through internal and external sources to surface disparities

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  • Strategy

    Stratify performance data to reveal patterns in disparities

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  • Strategy

    Prioritize addressing disparities that align with broader organizational values, goals, and strategies

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  • Strategy

    Set measurable short- and long-term goals for reducing disparities

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  • Strategy

    Share performance data to promote transparency and accountability

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Parting thoughts

Provider organizations must address inequities among their patient population. But no matter how lofty an organizations’ health equity ambition, progress cannot be made without taking a data-driven approach. A truly data-driven approach requires the infrastructure to collect data and the technical resources to track and keep data transparent. It also requires leaders to be vulnerable and willing to own their gaps and shortcomings—internally and externally—and commit to short- and long-term goals to reduce them.

But simply identifying disparities and setting goals is not enough to create structural change. Leaders must address community-wide social needs and their root causes: intergenerational poverty and structural inequity. To advance health equity in the long term, leaders need to address these community-level factors that impact historically marginalized groups at disproportionate rates.

To start, leaders must understand the causes and effects of structural inequities and accept that players across the health care industry—from payers and health plans, to pharma, medical technology, and life sciences companies—all have a role to play. Then, leaders must tap into their data infrastructure to understand the full scope of the problem, strengthen partnerships with community and cross-industry health care leaders, and design transformational strategies for addressing structural inequities.

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