An enterprise data warehouse (EDW) is the beating heart of a healthcare organization’s analytic capabilities. Successful EDWs can provide hugely valuable insights. Unsuccessful EDWs become black holes of effort and morale.
Author: Andy Hackbarth
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Patient attribution 101: tips for setting up a dynamic model
Many healthcare analyses involve the concept of “patient attribution”—in other words, linking a patient to the provider responsible for their care. For example, you might want to know which primary care physician or clinic has been managing a patient’s health to properly assign the rewards when that patient has a good outcome (e.g., good medication adherence, or lower-than-expected spending over some period) or the responsibility for a bad outcome (e.g., a readmission, or a preventable emergency room visit).
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Report: 2021 Database Benchmark for Healthcare Analytics
New cloud-based database management systems (DBMSs) are powerful and scalable, but it’s hard to know which technology will best serve your organization’s needs under realistic data processing scenarios. This uncertainty is compounded in healthcare, where the characteristics of a typical data analytic exercise don’t usually conform to the “big data,” aggregation-oriented use cases around which the latest generation of columnar DBMSs were developed.
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How to track patient features over time for pop health analytics
In improvement work, tracking performance over time is a core idea. However, it’s often quite hard, technically, to work with “dynamic” data — data that reflects the changing state of the world over time.