Healthcare is rapidly growing and evolving to become a data science, using data to make decisions and guide clinical care at every opportunity.
Two aspects are driving that transformation: 1) new ways of processing data, especially AI and machine learning and 2) the incorporation of new types of data such as patient payment claims, social determinants of health, device data and genomics. Through data, we have the potential to fundamentally improve the healthcare system. Yet, we also know with respect to healthcare that racial minorities and those living in poverty tend to receive lower-quality healthcare than non-Hispanic Whites and people with higher levels of disposable income and accumulated wealth.
In the world of data science, there have already been concerning episodes where bias has crept into healthcare algorithms, even when the creators had tried hard to ensure data integrity.
To find out more, read our recent white paper, Removing Data Bias from AI and Machine Learning Tools in Healthcare, published in partner with HIMSS.
