Can Moneyball Become Moneycare?: How Predictive Informatics can Make mHealth a Success

     I knew the story of Billy Beane and the Oakland A’s long before I saw the movie.  As an avid baseball fan (Yankee fan since age 5, without apology), I followed the story of how informatics were used to bring a lowly $39M team to the brink of making the World Series, putting together the ultimate fantasy statistically successful baseball team.  The methodology is now the business model of all of baseball.  As I sat and watched the movie, I couldn’t help but wonder why the same methodology couldn’t be used to improve health care with mHealth technologies.

Informatics is defined in the Webster dictionary as “the collection, classification, storage, retrieval, and dissemination of recorded knowledge treated both as a pure and an applied science.”  Informatics has certainly been used in medicine. The LACE index was developed in Canada to predict death and readmission rates after discharge from the hospital (Published in the Canadian Medical Assn Journal, 2010.  However, yet another study in the Journal of General Internal Medicine (2011,Volume 26, Number 7, 771-6), neither providers nor a published algorithm were able to accurately predict which patients were at highest risk of readmission.  This type of informatics is known as Predictive informatics (PI), the combination of predictive modeling and informatics which is applied to healthcare, pharmaceutical, life sciences and business industries.  Good evidence that PI works is found in hospitals which utilize the Web-based eDischarge™ software-as-a-service (SaaS). The readmission rate fell for the second straight year to 14%, significantly below the national average of 20% for hospitals of comparable size, according to the Curaspan Health Group. (

The utilization of PI in the treatment or clinical pathways that would be part of an mHealth platform could significantly impact healthcare, by incorporating informatics as well as best practice guidelines.  PI itself can determine which mHealth tools or platforms would be best for a specific patient at the time of diagnosis, discharge from the hospital, or post-procedure.  The patient’s cell phone or other device can be programmed automatically, activating one or multiple apps or meddis see .   The possibilities are endless.  PI concerning behavioral patterns can be used to prevent or reverse obesity, smoking, non-adherence with medications or follow-up care, and more. This is not to say that computers will rule the world.  But we don’t have to reinvent the wheel when it comes to gathering or utilizing data.  The use of PI has been utilized in the business sector for years and is the backbone of companies like FedEx, Google, and Amazon.  There is more data out there than we will even know what to do with (a subject for another blog), and we need to harness it to digestible, beneficial forms. PI is certainly one of them.

Billy Beane was ridiculed.  His ideas changed baseball.  Mobile health is not toys or gadgets.  It is serious technology that is easily usable.  But to get to that point, we must step out of the box that providers are given in medical school.  It no longer holds.  There are over 2500 practice guidelines.  Do we expect docs to know all of them? Do we expect them to know all of them in their respective specialties (cardiology has already almost too many to count)? No. So let’s make medicine and mHealth a bit like the 2002 Oakland Athletics, built on predictive informatics.


About davidleescher

David Lee Scher, MD is Founder and Director at DLS HEALTHCARE CONSULTING, LLC, which specializes in advising digital health technology companies, their partners, investors, and clients. As a cardiac electrophysiologist and pioneer adopter of remote patient monitoring, he understood early on the challenges that the culture and landscape of healthcare present to the development and adoption of digital technologies. He is a well-respected thought leader in mobile and other digital health technologies. Scher lectures worldwide on relevant industry topics including the role of tech in Pharma, patient advocacy, standards for development and adoption, and impact on patients and healthcare systems from clinical, risk management, operational and marketing standpoints. He is a Clinical Associate Professor of Medicine at Penn State College of Medicine.
This entry was posted in healthcare economics, Healthcare IT, informatics, mHealth, mobile health, smartphone apps, statistics, technology and tagged , , , , , , , , , , , . Bookmark the permalink.

1 Response to Can Moneyball Become Moneycare?: How Predictive Informatics can Make mHealth a Success

  1. Pingback: The Medical App is a Patient Advocacy Tool | The Digital Health Corner

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