Data Science for Healthcare Providers
Ended Dec 7, 2022
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Full course description
Registration fee is non-refundable after the first session starts.
If your organization wants to enroll multiple registrants or you would like to speak to someone about this program please contact Ms. Irene Hurst at ihurst@usf.edu
Volume discount is available when three or more participants from the same organization register for the course.
Full Schedule May 4th, 2022 through December 7th, 2022 – Once or twice a month, Wednesdays between 5:00 PM – 7:30 PM EST.
Modality Synchronous virtual via MS Teams.
Prerequisites
Physicians/residents/healthcare providers who have had some statistics in their training and who have had any programming experience (e.g. SAS, or any computer programming language) will find the contents easier to follow and will likely benefit more from this program. That said, the program is open to those with little to no programming background as well.
Description
In 2021, USF’s Center for Analytics & Creativity designed and ran a one year “physician data science” pilot program, attended by ~30 physicians in areas as diverse as internal medicine, cardiology, OBGYN and oncology. The program taught topics such as machine learning and AI, and provided extensive exposure to building predictive models in medicine using Python and R. At the end, the group was in significantly better position to think about how data-driven machine learning and AI can transform medicine; having experience with understanding the methods and playing around with actual code was a significant part of this experience. About half of the participants had little prior programming experience. Building on this, we are proposing to launch a formal ‘data science for healthcare providers’ certificate program.
Background
Working with large secondary data requires embracing a broader computational and data mindset as follows:
- Methods to query large structured and unstructured databases and data warehouses to retrieve information
- Methods to deal with textual data and text analytics
- Machine learning algorithms such as deep learning
- Exploring causal frameworks vs predictive frameworks
The main objective of this program is to introduce physicians and other healthcare providers to the four topics above and allow participants to explore big data to help in decision making in medicine.
Highlights
- Nine sessions of 2.5 hours per session
- Extensive discussion of both big picture as well as algorithmic issues, along with exposure to current work in the medical journals.
- Exposure to Python/R and related machine learning libraries
- Participants present a final project at the end of the program. The final project will include (1) a detailed design of how machine learning models can be used to solve a problem in medical practice (2) results from building and applying machine learning models on real data. The paper may also be written in a format suitable for a medical journal (e.g. JAMIA or JMIR for instance), so participants can submit the paper for publication at the end of the program.
Integration of Ph.D. in Big Data Analytics Students
USF’s new interdisciplinary Ph.D. in Big Data Analytics program will be integrated into this program. The doctoral students in that program will present parts of this and/or work closely with participants on their projects and likely co-author papers.
Mode of Offering
The program will be offered synchronous online – meaning participants will have to commit to being in the “live” (synchronous) sessions once to twice a month. This will also give them an opportunity to interact live with the presenter and other participants in the program. In the rare occasion they miss a live class, there will be a recording that is accessible, but this should be used only as a last resort since live attendance and participation is expected.
Investment $5000 for the whole program including the certificate.