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.
Last October, my team started working on a project to bridge the communication gaps between inpatient general medicine and radiology. Despite having done a full year of internship before starting residency, we quickly realized that as radiologists we knew very little about healthcare is delivered on the wards. Understanding how well the imaging workflow runs from ordering to reporting, identifying possible delays by systematically analyzing patient data seemed straightforward.
Hypothesized imaging workflow for admitted medicine patients. Source: post author
A 2-hour meeting, eight weeks of delay, and several email exchanges later, we now rely mostly on manual data collection. This blog post is about what happened. Continue reading
Data is the results section of a scientific paper.
Data is a graph on the dashboard.
Data is a powerful motivator when it puts what we already know about ourselves in numbers.
Data is necessarily biased because it cannot exist in a vacuum.
Data is rarely perfect or complete.
Data is the Wizard of Oz in whom we only see that which we desire to see.
Data is not meaning.
Data is not opinion.
Data is not a mirror mirror on the wall to reveal the hidden truth in it all.
At the end of the day, data is data. It’s people who write the Discussion sections.
People draw conclusions from analytics.
It’s people who create meanings. People who form opinions.
Don’t confuse the two.