The prevailing issue when working with Operating Room (OR) scheduling within a hospital setting is that it is difficult to schedule and predict available OR block times. This leads to empty and unused operating rooms leading to longer waiting times for patients for their procedures. In this three-part session, Ayad Shammout and Denny will show:
1) How we tried to solve this problem using traditional DW techniques
2) How we took advantage of the DW capabilities in Apache Spark AND easily transition to Spark MLlib so we could more easily predict available OR block times resulting in better OR utilization and shorter wait times for patients.
3) Some of the key learnings we had when migrating from DW to Spark.