I promised myself I would write more (here and elsewhere) this year as part of my New Years Resolutions, and sadly, it's taken me more at least a week to write my first post. But what a week, so I think I can be excused.
In any case, one of the things we are going to be testing with SANER this Connectathon is support for Supplemental Data. Supplemental data in a measure allows the receiver of the measure to obtain additional information that can help them make sense of what is going on in an emergency response scenario.
As you start off in an emergency, especially a one related to some sort of infectious disease, there's a lot of information that you simply don't know at the start of the situation. What is the impact of other disease (comorbidity) on patient outcomes? What about patient age, gender, race or ethnicity? What are other complications of the disease. One only need to look at AIDS or COVID-19 to see all the things we don't know. Shortly thereafter, one might ask, how can we start to use this information about comorbidity to help assess the degree of strain imposed on an institution, and perhaps even create new measures.
To get there, we need more than just measures, we also need to bring supplemental data to the table, to the place where it can be analyzed in depth. While disease and rates of infection are the primary sources of strain on the health system, other factors such as risks (geography, age, gender, et cetera) and comorbidity can influence outcomes (complications, length of treatment and death).
Arguably, this level of detail is not needed for every situation, but it can be extremely valuable in cases where the disease outcomes are serious, or the number of infections is great. In the early stages, it can help emergency responders to develop models to assess the impacts, and in later stages apply those models.
There are several challenges right now in using supplementalData with Measures that I'll be discussing in subsequent posts.
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