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Healthcare organizations are on the move, working feverishly to implement Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems as part of a federal “requirement” enacted by the American Recovery and Reinvestment Act of 2009.  This requirement forces healthcare organizations to implement and make effective use of electronic medical record systems by 2015, or risk having Medicare reimbursements reduced.  In the rush to implement such systems, little attention has been focused on what may be the greatest contribution to the healthcare field of our time – analysis and data mining of such medical records to detect, better treat, and ultimately prevent illness.  We believe that R, an open-source data analysis language, is best positioned to make such analysis possible.

In fact, we predict that electronic medical record vendors will soon be embedding or otherwise implementing R into their solutions.

While the benefits of electronic medical records versus a paper-based alternative have long been documented, fewer than 50% of US health organizations had adopted such technology by 2009.  Cost and a lack of standards have been two of the major reasons for the delay in adoption.  With the federal government creating financial incentives to ensure that such technology is adopted, we believe that costs and standards will no longer represent significant barriers to entry moving forward.  Electronic medical records will be adopted.  But then what…

Data mining and analysis of electronic medical record data is the next frontier.

While the frenzy to implement EMR has led to so much attention being paid to the practice of storing medical records, little time has been spent determining how such data can be fully leveraged to improve patient care and health systems as a whole.  In a previous article, we discussed some of the implications of electronic medical records in the insurance space.  We concluded that it would initially increase malpractice costs for physicians.  Medical mistakes are easier to catch when more information is being recorded about a patient’s treatment.  However, we ultimately find that adoption of such systems will have an enormous impact on improving patient care beyond the current paper vs. digital benefits boasted by EMR vendors.

We see the major benefits originating from data mining of EMR records.  For example, what if medical records were analyzed in real-time to create more personalized medicine?  What if we could quickly measure how patients of a similar background responded to various treatment options, then use that information to help treat a current patient?  What about predicting length of hospital stay using medical record information, enabling hospitals to staff and allocate resources more effectively?

We view R, an open-source data analysis language as being positioned to make this vision a reality.  We believe R is best positioned to analyze electronic medical records for the following eight reasons:

  1. Given that standards for EMR systems are still in flux, any solution to data mining of such records should be flexible and capable of adapting to shifting EMR standards. R is positioned well for this environment, as it already integrates and connects into a plethora of database management systems.
  2. The technology will need to be capable of analyzing very large amounts of data – millions to billions of records.  R enables parallel processing and can be used in conjunction with Hadoop and other technologies to spread analysis out to distributed hardware.
  3. As the EMR space is a rapidly growing field, the analytical technology that it’s paired with should also be on a growth trajectory.  Given R is open-source, new methods and techniques are implemented into R faster than proprietary alternatives.
  4. The analytical technology should work on many different operating systems in order to service the variety of hardware/software solutions used by healthcare organizations.  R fulfills this requirement and is cross-platform.  It works on Windows, Mac, and Unix.
  5. The analytical technology should have a large user base to support the needs of the healthcare space.  R has a large, international community that includes some of the brightest minds.  R is also taught in most of the top academic statistical programs across the US.
  6. The technology must be transparent.  Once again, R is open-source, enabling anyone to go in and understand what it is doing.  Also, R is very well-documented in the literature.
  7. The technology must have very strong support for unstructured data analysis, as much of EMR data is unstructured text.  R has a list of very powerful text mining and unstructured data analysis packages / libraries.
  8. The technology needs to be affordable. R satisfies this requirement;  R is free.

Similar to the inevitability of EMR adoption by mainstream US healthcare, we view data mining of such records as the next surge.  The question is, who will be the leader in this space?  We believe it will be R.

For those interested in discussing this topic further, contact Timothy D’Auria at tdauria@bostondecision.com.

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