This dashboard provides a preview of how CareJourney is able to use the 100% Medicare FFS data set to help answer the most pressing questions for both the network performance managers and network builders by heat-mapping risk factors to identify vulnerable patient populations.
Using the dashboard below, drill down to the particular state or ACO level that you're most interested in.
Q: Why is this necessary?
A: As we just learned from the blog, we need to be collaborative and rely on our network to help us become to become a trusted, open data community. With that mindset, we will be able to do several things in order to help that community address the pandemic immediately, including heat-mapping risk factors. More specifically, we can “Heat-mapping” risk factors to identify populations vulnerable to COVID-19. This is currently being completed by CareJourney and early findings suggest that the common factors of individuals at the greatest vulnerability to COVID-19 include being aged 65+, residing in a long-term care or nursing home, and having multiple of the following chronic conditions: chronic lung disease, diabetes mellitus, chronic kidney disease, heart and vascular diseases and cancer.
Q: What data source is populating this dashboard?
A: This analysis is using the entire Medicare FFS data set from calendar year 2018. The data is broken down at both an ACO and state level.
Q: What are the risk predictors used in this analysis?
A: This analysis only counts patients who have had at least 1 of the 5 risk predictors. The risk predictors are:
Should you have any additional questions, please reach out to your main point of contact at CareJourney for more information and/or additional support regarding this analysis. If you do not have a main point of contact at CareJourney, please reach out to jumpstart@carejourney.com.
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CareJourney is a leading provider of clinically-relevant analytics for value-based networks supporting leading payer, provider, and life sciences organizations across the US. We help these organizations achieve their goals by bringing new, high value insights out of expansive population claims data.