Digging into Inpatient Radiology Patterns from Scratch
There is an ever increasing push in radiology towards value-based imaging. In the US, many inpatient stays are reimbursed as diagnostic related groups (DRG), which means inpatient radiology can become a costly medical service. Unfortunately, institutional-wide ordering patterns can be very difficult to evaluate.
In this post, we’ll take a look at different ways to take low-level input data and create compelling visualizations on inpatient radiology ordering patterns.
To follow along, set up your computer using the following Python tutorials:
Alternatively, start a free Jupyter notebook from Azure Notebooks.
(Howard) Po-Hao Chen, MD MBA is the Associate Informatics Officer at the Cleveland Clinic Imaging Institute and a musculoskeletal radiology subspecialist. He has an interest in data-driven radiology, quality improvement, and innovation. Howard has an MD and MBA from Harvard University, and he finished training with fellowships in musculoskeletal radiology, nuclear medicine, and clinical imaging informatics in June 2018 from University of Pennsylvania.
Previous Post
Next Post
[…] With a little coding in Python or R, one can gain a lot of insight into how our referring providers’ lives intertwine with our own. The full story in my new post on Radiology Data Quest. […]