Using R Bridge the Gap in Electronic Medical Record Reporting
summary
Hospital admissions operating at 80–85% ideal but hitting ≥90% nine of the last 12 months motivates a scheduling intervention that exposes scheduled surgical volume four months out. The team extracts daily SQL results via Apache Airflow, pulls them into R with the rocky package, transforms with dplyr/lubridate, renders a flexdashboard app (highcharter heatmap) hosted on R Studio Connect, and sends conditional HTML emails; one alert flags 22 patients and prevents late cancellations.
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chapters (11)
- 0:00Introduction
- 1:58Project Overview
- 7:44Data Warehouse
- 9:16Flex Dashboard
- 10:03Rocky
- 11:52Why R
- 13:28Impacts
- 15:25Dynamic Emails
- 17:17Big Takeaways
- 18:33Next Steps
- 19:32Questions
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