As the world battles a global pandemic, COVID-19 has strongly demonstrated the fundamental need for true holistic data platforms. We have seen the life-impacting consequences of not bringing data together for a whole person view of the community—identifying the needs and the impact of interventions. Health inequities are exacerbated as a result of this failure, especially in vulnerable populations.

To address this, employing flexible data platforms that enable whole person risk stratification can empower more nimble responses to improve human outcomes, including equity. In this model, risk stratification allows data-driven identification of people at higher risk and need. Central to this though, is a data platform that respects both privacy and ethics, especially when attempting to improve health equity. We all know the serious consequences of unethical AI and breaching privacy. This is particularly egregious when trying to serve more vulnerable populations.

When analyzing the health impacts – and health inequities – of COVID-19, population-level insights are key. While we need PHI and PII to perform the analytics properly, they can be anonymized and not surfaced to end users individually. This approach provides needed insights while also protecting individuals’ privacy. Even when identifying individuals in need to drive prevention work, basic information like names and contact information could be provided to care management teams to facilitate interventions. In this way, PHI could be protected while still helping drive the risk stratification algorithm and identifying those in need.

https://www.healthitoutcomes.com/doc/can-whole-person-analytics-improve-health-equity-0001