If you have taken overnight call, you quickly develop a sense for the emergency department and the inpatient floors. In my institution, radiologists develop hypotheses on how inpatient orders are placed.
For instance, sometimes it might seem as if inpatient radiology exams follow some sort of circadian rhythm. The data look to confirm it: we see the infamous “x-ray bump” in the early morning, with the increase in CT start more gradually but last later into the day.
Also, are weekdays and weekends any different? If so, how?
Going on a Quest
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. Read the full story in my new post on Radiology Data Quest.
From the Open Data Network I stumbled upon the CMS outpatient imaging data organized by state and decided to peek into the dataset and stick the data onto a US map for fun. Geek out with Joe and me in this new blog Radiology Data Quest.
They are taking medical data science rather seriously. The folks at Stanford Medicine are onto something.
Source: Biomedical Data Science Initiative @ Stanford Medicine
Don’t Start with the Data
Do Start with a Good Question
Don’t think one person can do it all
Do build a well-rounded team
Don’t only use one tool
Do use the best tool for the job
Don’t brag about the size of your data
Do collect relevant data
Did you have a chance to read the July 2016 issue of Harvard Business Review (HBR)?
Why would anyone care to subscribe to HBR, you ask? Well, fine, you can read these for free on their website.
July’s HBR has a healthcare focus. First is Michael Porter and Robert Kaplan’s How to Pay for Health Care, followed by Brent James and Greg Poulson’s The Case for Capitation.
Still no? Here’s the quick run down for the closet MBA in you. Continue reading
We often think of innovation as creating new technology, but innovation also comes in the form of new business models, new regulations, and new ways to perform a craft. Radiologists are in crossroads among a myriad of such “news,” the more comprehensively discussed among which are the reimbursement changes and regulation changes which have been discussed ad infinitum (e.g. here, here, here, and more).
Rather than discussing the impact of the new healthcare regulations on radiology and the emerging high-tech, social-media-connecting, cloud-based, deep learning, big-data-supporting, iPad-friendly, segmentation-compatible solutions , maybe it is worthwhile to take a few minute to think about innovating at a much lower level. On the level of our job description.
Photo Credit: debspoons; freedigitalphotos.net
Dr. Keith Dreyer opens with a keynote during the Intersociety Summer Conference (ISC) with description of data science and overview of how machine learning have evolved over time.
He describes that machines and humans inherently see things differently. Humans are excellent at object classification, recognition of faces, understanding language, driving, and imaging diagnostics. Continue reading