Industry News Details
Interview with Chris Franciskovich, Data Scientist, OSF HealthCare, Speaker at 4th Annual Global Big Data Conference Aug 30 - Posted on : Jul 26 - 2016
We feature speakers at 4th Annual Global Big Data Conference Aug 30 - Sep 1 2016 to catch up and find out what he or she is working on now and what's coming next. This week we're talking to Chris Franciskovich, Data Scientist, OSF HealthCare.
Interview with Chris Franciskovich
Tell us about yourself and your background.
As a Senior Data Scientist at OSF Healthcare System, my work focuses on generating value for our patients, staff and organization through the development and deployment of predictive models. I’ve spent the past nearly ten years at OSF with approximately the last four of those as a data scientist. I love digging in a exploring our raw data, transforming it into an actionable model and knowing my work is going towards helping the lives of our patients. I’ve presented my work in readmissions at EPIC’s UGM 2015 and Predictive Analytics World – Healthcare Boston 2015.
What have you been working on recently?
Recently, I’ve been working on a No-Show/Same Day Cancellation model. It’s really an exciting project that allows me to explore the incorporation of external data sources, such as weather data and Census information, through geocoding and spatial querying. I’ve also been working on a model that predicts provider office level daily demand, which can be used to inform staffing decisions moving forward.
Tell me about the right tool you used recently to solve customer problem?
I’m pretty flexible on tools. The majority of my work is done in R with data extractions coming from SQL Server or Teradata, but I’ll adjust the tool set based upon the project need. For my recent staffing simulation projects, I went with Python as it has well-developed discrete event simulation, parallel processing and GUI design packages. So, Python let me put everything in a single script I could share with the business leader, allowing her to run additional simulation scenarios.
What are some of the best takeaways that attendees can have from your “Predictive Modeling In Healthcare: A Brief Review of Case Studies” talk?
Healthcare is an industry in the middle of some considerable changes. The push towards population health, a shift toward pay-for-performance reimbursement, the proliferation of electronic medical record data and the ability to use data science to help improve the lives of others makes this space an exciting and incredible space to work as a data scientist. Attendees will see examples of how we’re using predictive modeling at OSF Healthcare System, and I hope they see why I believe this is a great industry in which to work.
What are the top use cases for Predictive Modeling in Healthcare?
Predictive modeling has incredible potential impact in Healthcare. It can help us identify and assist patients at risk for various negative outcomes, anticipate demand in order to proactively adjust staffing, increase workflow efficiencies by targeting interventions, assist in resource planning and drive marketing in various manners.
What trends you see in upcoming 6 months?
I anticipate an increased incorporation of predictive models directly into electronic medical record systems and other key business workflows. While many of these may come through the vendors themselves, I believe key healthcare systems will continue to expand their local talent for the generation of high performing localized models to improve patient care.
I anticipate an increased incorporation of predictive models directly into electronic medical record systems and other key business workflows. While many of these may come through the vendors themselves, I believe key healthcare systems will continue to expand their local talent for the generation of high performing localized models to improve patient care.
Any closing remarks?
I’m excited to meet and learn from practitioners from so many different organizations and industries.