You think you know what predictive analytics means, huh?

By  |  July 20, 2014 | 


This month’s Health Affairs dedicates itself to the subject of big data, a term in the news quite a bit these days.  If you think you know what big data implies, mainly dredging data sets to build the clinical decision support in your EMRs, you would be incorrect.

We are talking much, much bigger.

One article grabbed my attention, whose lead author practices both law and bioethics.  The piece delves into how information requires handling–legally, medically, entrepreneurially, and ethically.  I got way more out of reading the citation than I thought.

To whet your appetite, think about the quote below:

[…] In the hypothetical example above, a potential predictive analytics model might ascertain the instantaneous risk for cardiopulmonary arrest of every one of a thousand patients in a given hospital at every second and determine which patients would most benefit from ICU admission. Use of the model might reduce the aggregate rate of cardiopulmonary arrest if those high-risk patients were admitted to the ICU—provided they or their surrogates agreed to that admission. Conversely, a predictive analytics model might also suggest which low-risk patients could be discharged safely from the ICU or could not be admitted to the ICU in the first place, thereby allocating scarce resources more equitably. […]

[…] Predictive analytics models are already being deployed to help identify in real time high-risk patients like those in the cardiopulmonary arrest example. In the near future, models based on machine learning (that is, ones using a computer that can learn from data instead of requiring additional programming) will be able to instantaneously consider the risk of all patients in a hospital, their individual therapeutic goals and preferences, hospital staffing (including staff members’ experience and performance), resource constraints, and external conditions such as whether other hospitals are diverting patients in the emergency department in the case of a disaster. The model could then advise hospital administrators on whom to admit to the ICU and how to staff it. […]

You can envision all the scenarios and snags employing such systems could spawn.

I also found the issue of using big data for quality improvement (versus research) vexing, and the paper goes into some detail on the matter.

The paper has application to hospital practice and will be our future.  Even if you are not a data geek, much in the commentary has relevance to your bedside rounds and patients. Take a look.

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About the Author:

Brad Flansbaum
Bradley Flansbaum, DO, MPH, MHM works for Geisinger Health System in Danville, PA in both the divisions of hospital medicine and population health. He began working as a hospitalist in 1996, at the inception of the hospital medicine movement. He is a founding member of the Society of Hospital Medicine and served as a board member and officer. He speaks nationally in promoting hospital medicine and has presented at many statewide meetings and conferences. He is also actively involved in house staff education. Currently, he serves on the SHM Public Policy Committee and has an interest in payment policy, healthcare market competition, health disparities, cost-effectiveness analysis, and pain and palliative care. He is SHM’s delegate for the AMA House of Delegates. Dr. Flansbaum received his undergraduate degree from Union College in Schenectady, NY and attended medical school at the New York College of Osteopathic Medicine. He completed his residency and chief residency in Internal Medicine at Long Island Jewish Medical Center in New York. He received his M.P.H. in Health Policy and Management at Columbia University. He is a political junky, and loves to cook, stay fit, read non-fiction, listen to many genres of music, and is a resident of Danville, PA.


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