Category Archives: Radiology Innovation and Quality

Disruptive innovation! Six-sigma! DMAIC! Sustainable growth rate!  These are words people throw around, but it’s the idea that counts, not just the acronyms.

Innovating in a large health system

One would think that resource-rich organizations are able to foster new ideas better than poor, cash-constrained startups.

However, it is remarkably difficult to innovate within a large health system on an ad hoc basis, for the same reason that it is difficult to innovate in a large corporation.  For one, it’s all too easy to feel like a cog in a large machine.  Fear of failure, perceived lack of reward, and a paucity of institutional support are other reasons why innovation stagnates in otherwise resource-rich organizations.

But little-fish-big-pond problems are not the only ones that plague innovation.  This phenomenon is well-recognized as part of the key reasons why disruptive innovations are notoriously difficult to launch from within a corporation.

If you feel this way, you may be an “intrapreneur.”

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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

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

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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The Paradox of Standardizing Broad Data

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