| | MARCH 20239UTLOOKHealthcare Tech Multiple models and methods for reporting and stratifying patient demographic information make it challenging, despite common best intentions, to leverage outcomes analysis done by different enterprise teamsreporting and stratifying patient demographic information. This makes it challenging, despite common best intentions, to leverage outcomes analysis done by different enterprise teams. For example, some analysts might treat race and ethnicity as separate independent dimensions (which is how they are captured during patient registration), while others combine them into a single category list. Likewise, some analysts create distinct multi-race/multi-ethnic categories, others use the first race or ethnicity selected by a family, while yet others assign a patient to every category they indicated during registration. Even though the various reporting approaches all have pros and cons, we felt that a single unified approach for REaL data stratification was an essential first step in our efforts to look at population-level outcomes and potential disparities.Another area we tackled was aggregating the many distinct, specific, race/ethnic categories into roll-up groups that would provide sufficient numbers for analysis. For example, various Hispanic ethnicities such as Puerto Rican, Mexican, etc. While we respect the need for families to have specific classifications that they identify with, the comparatively low N-size of pediatric clinical populations dictates that we start with a higher level of aggregation, particularly when exploring disease-specific or procedure-specific outcomes that impact even smaller populations. Our new model combines race and ethnicity into a single classification and establishes Hispanic identity as a primary characteristic when present as an ethnic group choice. It also handles multi-racial/multi-ethnic patients by creating a category of Two or More Races.Analysts engaging in different types of demographic stratification can use more granular or specific categories based on their specific needs, with the understanding that this new enterprise model is to serve as both the starting point and required standard for our system-wide equity and disparity measures.To implement our new analytics infrastructure, we started by inventorying the various data sources used to pull demographic data across the organization. These included: EHR-integrated reporting tools such as Workbench and Radar, visualization tool data marts (Qlik QVDs), and SQL raw data extracts (Clarity & Caboodle). To validate that our new mapping logic factored in all of the various unique combinations of multi-race/multi-ethnic/partial refusal/"other", etc. we wrote an elaborate error-checking formula in SQL and tested the results against several million patient records in our database. We believe that our end-product data asset is now ready for prime time and are rolling it out first for our Qlik data visualization tools and ad-hoc SQL queries. Within a few months, we will load it into our EHR for use in integrated reporting tools.Moving ForwardWe have our whole child health strategic imperative, we have our standard enterprise data assets for starting to look into equity and disparities. The next steps involve persuasive communication, education, and outreach. Fortunately, we can build upon very relatable studies that provide great examples of the types of analysis we plan to perform; such as the study on physician-patient gender concordance and heart attack mortality published in PNAS in 2018, and the analysis of racial gaps in stroke treatment published in the journal Stroke in 2019. Between our new analytics capabilities and readily explainable examples of disparities in outcomes, Nemours is well positioned with both the "why" and the "how" surrounding this important work. The only part left is the "when", and that starts now. HT
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