COVID-19 exacerbated healthcare inequities throughout the world, making them more apparent than ever. This led to renewed calls for putting systems and technologies into place that provide the support that every population needs to achieve their maximum health potential, particularly those with historically poor outcomes. This is the basis of health equity: the idea that everyone should have access to the things necessary to maintain or achieve good health. It prioritizes healthcare based on need that will lead to similar health outcomes. Health equality isn’t always enough, as some people need more – or different – forms of care. Data equity, including the social determinants of health (SDoH) and access to care are two keys to health equity.[i][ii] Initiatives seeking to improve health outcomes should focus on weaving a bridge from new data sources and combining services to create pathways of care that diminish unequal health outcomes.
Achieving equity of health outcomes starts with data equity; data and data practices can close equity gaps or perpetuate them. It starts with gathering the right data and ensuring that the information is relevant to all populations served. Great strides have been made since the 2009 Institute of Medicine report that stated that the U.S.’s healthcare information infrastructure “does not provide the necessary level of detail to understand which groups are experiencing health care disparities or would benefit from targeted quality improvement efforts.” And while structured data fields are far more inclusive, there is an enormous amount of relevant data that is unstructured. Gathering it requires the trust of the patient and the right process. Intentionally designing data collection at the point of care and outside the clinic to be comfortable and thorough is essential. This requires that the care team understands the challenges of underserved populations and how to elicit the needed information (targeted education is an effective way to remove data collection hurdles). Loneliness, for instance, is a huge risk factor for mortality and is the kind of detail that might only come from the right conversation (and won’t be a structured data field outside of a depression screen).[iii]
Data transparency is another important feature and an example of where clinical staff empowered by the right analytic tools can make a huge difference. At a conference last year, a physician related a story about his health system’s efforts to ensure equitable health outcomes for an acute condition. Despite having a checklist for a life-saving procedure, a doctor noticed what appeared to be a racial disparity in the health outcomes across locations within the system. Further digging revealed that the checklist was being followed, but there was some variance in the time to follow the various steps. Ultimately, a real-time dashboard gave care teams the insights needed to eliminate the inadvertent lag.
Access to the full spectrum of care across the care continuum is another necessary feature for equitable health outcomes. There is an enormous body of work demonstrating that those who face obstacles to obtaining care have the highest rates of chronic disease, poorer health outcomes, and greater risk for mortality. Limited access means a decreased likelihood of receiving preventive care, something that impacts those at high risk for certain conditions. It also means delays in diagnosis and treatment, which can have a negative impact on the overall health trajectory. It also limits the opportunities for health education, a necessary feature of ensuring adherence to the care plan and engagement with health outside the clinical setting. Access is also vital for generating data sets that reflect the entirety of the population being cared for. This is hugely important as we begin to bring on tools like artificial intelligence, which are pattern driven and will reflect the biases inherent in the training data.
Access is also important on the technology side, and when designed properly, can lead to better engagement and outcomes. Supportive technologies need to be introduced in such a way as to be useful and available for all populations (see the recent episode of the Nordic’s podcast feature Designing for Health, where we spoke with Memora Health’s Chief Commercial Officer Omar Nagji about using technology to engage health consumers where they are).
The path to health equity requires a multifaceted approach that includes improving access to care, the structures and governance that support data equity and transparency, and care engagement tools that are inclusive. Improving data equity and access to care will ensure better outcomes for people today, through proper preventive care, earlier disease identification, and better management. It also gives us the best chance of developing models that accurately predict likely outcomes for everyone. Continuing efforts to improve equity is the best way to ensure that everyone has the chance to achieve an optimal health outcome.
[i] Walker, R. J., Williams, J. S., & Egede, L. E. (2016). Influence of race, ethnicity and social determinants of health on diabetes outcomes. The American journal of the medical sciences, 351(4), 366-373.
[ii] Purnell, T. S., Calhoun, E. A., Golden, S. H., Halladay, J. R., Krok-Schoen, J. L., Appelhans, B. M., & Cooper, L. A. (2016). Achieving health equity: closing the gaps in health care disparities, interventions, and research. Health Affairs, 35(8), 1410-1415.
[iii] Holt-Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and Social Isolation as Risk Factors for Mortality: A Meta-Analytic Review. Perspectives on Psychological Science, 10(2), 227–237. https://doi.org/10.1177/1745691614568352