HealthPrism data comes from over 50 large commercially available consumer marketing data sets as well as publicly available data from the CDC and the US Census.

HealthPrism helps resolve data gaps that are common in publicly available data sources, such as underreporting of unhoused populations, by compiling data from over 50 data sources and using predictive modeling methods to accurately cover data gaps.

HealthPrism gives the ability to look at multiple data variables at once, enabling users to segment broad populations by multiple variables to find unique sub-populations.

Because HealthPrism has individual-level data, it can easily be integrated with your data at any level of aggregation.

HealthPrism leverages semi-supervised machine learning (ML) methods to train explainable predictive models. HealthPrism generates modeling datasets that represent the US population with respect to legally protected groups. HealthPrism extend bias checks further to include representation across income groups and the Rural-Urban Continuum Codes. Additionally, each variable input into a model is examined for its distribution with respect to age, income, the rural-urban continuum, and other demographic factors to help ensure that they do not act as a proxy for any of those variables. This bias awareness and examination can also influence the strategy for model selection where only explainable and interpretable models are used to train and predict risk.

HealthPrism is committed to maintaining the privacy of personal data, protecting user data and maintaining transparency with strict controls around data usage:

HealthPrism data reflects predictive estimates based on multiple demographic and consumer marketing data sources. HealthPrism updates underlying data and risk estimates on a regular basis (typically every one to two months, depending on the source). In other words, the data in this dashboard is updated on a consistently recurring basis as opposed to a real-time basis.

Populations that suffer disproportionately high rates of death and disease such as racially and ethnically diverse populations, people with disabilities, and rural populations are underrepresented in some health research, and their status as part of those groups is often not recorded in critical health care records. This lack of data is a serious barrier to advancing equity.

Race and ethnicity data are particularly essential to understanding and improving health equity. However, race and ethnicity data on much of the US population is missing from many health-related data sets. Race and ethnicity are often poorly documented in commercial health records like insurance claims. Additionally, gaps in data and outdated information in public data sources like the US Census can hinder meaningful insights into race and ethnicity. These gaps can prevent recognition of very real health disparities and delay action to improve equity.

To enable meaningful action on health equity, HealthPrism uses predictive models based on the Bayesian Improved Surname Geocoding Method (BISG) to fill the gaps in missing race and ethnicity data (this method is used by government agencies like the Virginia Department of Health). In addition, HealthPrism uses other data science methods to improve race and ethnicity predictive model accuracy. HealthPrism includes self-reported race where such data is available and displays predicted race where no self-reported data is available.