Keys to Disrupting Health Equity
Data tells the story. Accurate data can lead us to solutions. Acting on the accurate data will help close the health equity gap.
There is increased awareness and action around using data and data science to address the expanding health equity gap. During this process, many organizations have discovered the inaccuracies and incomplete picture their current datasets hold. As such, health systems and other health organizations are forming partnerships to better capture data connected to social determinants of health and experience patterns for underrepresented communities.
Health Evolve Technologies has facilitated several data-related dialogues with industry leaders to better understand their current state. Several themes in those conversations arose and we identified corresponding key tactics to aiding health systems and health plans in disrupting the patterns of health inequities.
Stakeholder Alignment
EY, a global data and technology firm, believes that innovation can unlock the power of data to produce value-based care.
“The major hurdle we face is not technological limitations, but human limitations. While technology is moving at an exponential rate, there is little movement on aligning the different needs of industry stakeholders. The ongoing misalignment in what stakeholders value, prioritize and invest in prevents us from moving towards the more connected ecosystem that can transform care.”
Source: EY
Accurate Data
Our challenges in capturing health disparities data became very apparent during the COVID19 pandemic. In 2020, the Harvard Business Review published an article entitled A Data-Driven Approach to Addressing Racial Disparities in Health Care Outcomes.
It is now well-known that the Covid-19 pandemic is disproportionately impacting Black, Indigenous, and other disadvantaged communities in the United States. Yet in the mist of the crisis, our understanding of this inequity was delayed and remains limited because many health care institutions, as well as state and federal governments, were slow to capture demographic information on Covid-19 patients. This omission is a striking example of how colorblindness and structural racism are manifested in our approaches to data science in health care and beyond.
Source: Harvard Business Review
Organizations like Brigham Health, a member of the not-for-profit Mass General Brigham health system, had a proactive approach by developing a robust data-driven infrastructure.
This powerful and underused approach revealed inequities that would have otherwise remained hidden. For example, we found that Hispanic non-English speaking patients were dying at higher rates than Hispanic English-speaking patients. Further risk-adjustment analyses then confirmed the finding and led to quality-improvement efforts to improve patient access to language interpreters.
Source: Harvard Business Review
The American Medical Association’s Center for Health Equity conducted a webinar entitled Research and Data for Health Equity in May of 2022. During the discussion, Nancy Krieger, PhD – a professor of social epidemiology at the Harvard T.H. Chan School of Public Health, shared her insights to the importance of accurate data and accurate usage of disparities data.
“Most recently, this past year at APHA, our data session focused on algorithmic bias. How is that playing out in public health data, in medical institution data? What do researchers and people that are using these data need to be aware of? How does it not only affect treatment people get but also allocation of resources and funding and decisions that are made on a cost basis.” Dr. Nancy Krieger
Stakeholder alignment and the collection and use of accurate data are two keys to disrupting health equity.
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