There’s an origin story for every superhero; even those without superpowers (like Batman – that’s right) got started somewhere. What we sometimes forget is that there is also an origin story for every regular person, every profession, every hobby.
Source: Wikimedia Commons
If you’re a radiologist looking to learn a few things in radiology data science, a simple web search will reveal a seemingly overwhelming amount of material you might have to know.
Fortunately, only a very small subset is necessary to start being productive. Here are a few resources I used to get started.
A well-written framework on Stanford Social Innovation Review describes three distinct forces of transforming a practice.
An agitator brings the grievances of specific individuals or groups to the forefront of public awareness. An innovator creates an actionable solution to address these grievances. And an orchestrator coordinates action across groups, organizations, and sectors to scale the proposed solution.
The key observation is that transformation requires all three in harmony. In medicine, the voices of agitators frequently meet top-down repression or with the silence of the leadership. “This is just the way we’ve always done it,” they might say.
The Stanford article focuses on building a team consisting of people in all three domains in order to bring about social innovation. In medicine, practices tend to be resistant to change partly due to the higher stakes but also due to the highly regulated climate of modern health care. (This is not necessarily good or bad – it just is.)
Although medicine often places more weight on orchestration – coordination of interdisciplinary care to benefit patient health – it stands to reason that a healthy dose of the other two is also necessary. If you see yourself as an agitator, know that a thorough understanding of stakeholder analysis can help you better differentiate between a simple inconvenience and an opportunity to create value. If you are an innovator, your strength may lie in an intuitive visualization of connections between disparate organizational units. Know that what seems obvious to you is probably opaque to others. In the end:
Agitation without innovation means complaints without ways forward, and innovation without orchestration means ideas without impact.
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.”
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
They are taking medical data science rather seriously. The folks at Stanford Medicine are onto something.
Source: Biomedical Data Science Initiative @ Stanford Medicine
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