Lots of folks are now in the game of slicing and dicing Electronic Health Records (EHR) or Electronic Medical Records (EMR).  There is much to be learned about diagnoses, outcomes, and populations and more when we take the fielded or structured portions of the EHR and apply Big Data techniques and Machine Learning models.   But what of the hand written notes doctors, PAs, and nurses make in the record? What can that tell us?  How does it influence our thinking about the diagnoses, outcomes and populations?

In short, those notes provide the "color" that fielded and rigid forms cannot.  The EHR wasn't designed to capture everything so sometimes something the medical professional sees it goes into the notes.  If the medical professional expresses "worry" or some other emotion in the notes they are in effect taking a first step towards predicting an outcome before it happens.  This is the analytical side of the medical professional brain at work - tallying all the indicators from the exam (or multiple exams and tests) into a written expression. 

Analyzing those written notes turns out to be a real goldmine for medicine.  Utilizing good Natural Language Processing (NLP) to know what those notes say, what they mean and then mixing those results with the fielded information on the rest of the EHR opens new avenues of understanding and prediction.  Heart disease is difficult to predict.  But recent work in mining both the notes and EHR suggest a prediction rate of > 80% for heart failure. 

What boxes are ticked and what choices from menus isn't the whole story.  What medical professionals write down does complete the story however.  NLP is the way to bring these two sets of information together.  Making medicine both preventative and curative is the result. 

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