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.

Presenters

Photo of Ayad Shammout

Ayad Shammout

Director of Data Platform & Business Intelligence

Ayad Shammout is a Database & BI Specialist and Microsoft MVP. He has more than 20 years deep experience in Database technologies and specializing in SQL Server, SharePoint, and Windows OS. Shammout is working in OLTP design and development, Data Warehousing, Business Intelligence and Big Data, with extensive experience in data management and analysis. Ayad has been involved in many SQL Server Enterprise implementations for High-Availability, Disaster Recovery, Infrastructure Design, Virtualization, Business Intelligence, Data Mining and Big Data.
Photo of Denny Lee

Denny Lee

Technology Evangelist

Denny Lee is a hands-on data architect and developer / hacker with more than 15 years of experience developing internet-scale infrastructure, data platforms, and distributed systems for both On-Premises and Cloud. His key focuses surround solving complex large scale data problems – providing not only architectural direction but hands-on implementation of these systems. Experience in building greenfield teams as well as turn around / change catalyst. His current technical interests include Apache Spark, Big Data, Machine Learning, Graph databases, Cloud Infrastructure, and Distributed Systems Robustness.