6 Elements of a Data-Driven Informatics Solution (1/3)

Big Data has become a radiology buzzword  (the others: machine learning, AI, and disruptive innovation are also up there).

However, there is a real problem with using the term Big Data – it isn’t just one set of data problems.  Big Data is a conglomerate of different data challenges: volume of data, heterogeneity of data, or the velocity of data are all important dimensions.  Machine learning and internet of things are others layers superimposed on the big data problem.

Sometimes it is helpful to step back and approach data problems with a common framework, a way to think about how and which facets of data science fit in a real-life workflow in the face of an actual problem.

Below is a 6-element framework that helps me think about data-driven informatics problems. They are generally in chronological order, but they are not “steps” because you frequently will find yourself going back and redefining many things.  However, the framework helps you maintain a big-picture outlook.  The reason any sufficiently complex data problem requires a team approach. Continue reading

8 Ways to Gauge The Quality of Your Data Team

At the turn of the century, Joel Spolsky came up with the idea of a “Joel Test” – a highly irresponsible, sloppy test to rate the quality of a software team.

Then, this group thought to come up with their own criteria to rate the quality of a data science team.  How do your analysts in the radiology department fare?

The “Joel Test” for Data Science

  1. Can new hires get set up in the environment to run analyses on their first day?
  2. Can data scientists utilize the latest tools/packages without help from IT?
  3. Can data scientists use on-demand and scalable compute resources without help from IT/dev ops?
  4. Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions?
  5. Does collaboration happen through a system other than email?
  6. Can predictive models be deployed to production without custom engineering or infrastructure work?
  7. Is there a single place to search for past research and reusable data sets, code, etc?
  8. Do your data scientists use the best tools money can buy?

New Seizure Prediction Competition, and Why It Matters for Radiology

Kaggle is a website to host coding competitions related to machine learning, big data, or otherwise all things data science.

Newly launched on Kaggle is a healthcare-related competition!  A group of health institutions provided a large data set consisting of three patients’ interictal and preictal (up to 1 hour before) EEG tracings in raw data.  The goal? Predict which “unknown” EEGs are preictal so healthcare providers can intervene.

Also, with the timely arrival of Internet of Things (IoT), wearable, and big data, can you imagine the impact of giving patients an accurate 5-minute warning every time a seizure is about to start? Continue reading

Inpatient Radiology Ordering Patterns from Scratch

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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.

Geeking out with CMS Outpatient Imaging Data (Lumbar MRI and Mammo)


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.

Biomedical Data Science Initiative at Stanford

They are taking medical data science rather seriously. The folks at Stanford Medicine are onto something.


Source: Biomedical Data Science Initiative @ Stanford Medicine

Do’s and Don’ts of Data Science

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

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Bundled vs Capitated and What They Mean for Radiology (1 of 2)

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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

The Radiologist’s Real Job Description

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, herehere, 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

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The [machine learning] race is on – Don Dennison

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