Natural Language Processing (NLP) is a hot topic in healthcare.

At this year's AMIA Annual Symposium, in Washington DC,  we brought the discussion on clinical NLP to a roundtable held on Monday lunchtime and were also invited to the AMIA NLP workgroup to present some real-life use cases in clinical NLP.

However, as much as we like sharing what we're doing, we were keen to know what other people think when it comes to how NLP can transform patient care, today and in the future.

So that’s what we did – we asked peers at the AMIA conference that question (How can NLP transform patient care?) as part of a contest with an incentive of an iPad Mini and $50 Starbucks voucher for 1st and 2nd place respectively.

More than a third of entries identified mining the unstructured, free text narrative of a medical record to be crucial to the transformation of patient care. Unsurprising really, if you consider that around 80% of data in an electronic health record is unstructured and the only real way to get this information into a useable format is using NLP.

But what was interesting was the difference in how to use this data. Ideas included; for better patient information, for using the extracted coded concepts in clinical decision support and to retrieve full patient cohorts.

It was a tough contest to judge but the winning entry came from Edgar Chou at Drexel University College of Medicine. He had a few ideas but the one we thought was most interesting, with the potential to have the greatest impact on patient care was to around the payer care mix.

Rehospitalization is a serious problem in medicine.

Medical aspects are complicated by end of life care issues as well as a regulatory environment in which hospitals can experience financial penalties for "excess" rehospitalization rates. Existing rehospitalization predictive models, most of which are based on administrative data, have poor statistical performance, as do models that employ limited physiologic data.

At Linguamatics' upcoming seminar in San Francisco, Dr. Escobar will present work on a new rehospitalization model that employs data from a comprehensive electronic medical record and which could be instantiated in real time.

He will also present a "road map" to explain how data from natural language processing can be incorporated into this model as well as on future strategies for instantiation of NLP engines into routine clinical operations.

Dr. Escobar is a research scientist at the Kaiser Permanente Division of Research in Oakland as well as being the Regional Director for Hospital Operations Research for Kaiser Permanente Northern California.

An expert on risk adjustment and predictive modeling, Dr. Escobar has published over 130 peer-reviewed articles and is currently in the middle of deploying a real-time early warning system for deterioration outside the intensive care unit at two Kaiser Permanente hospitals.

IBM Watson gets a lot of attention in the medical field for trying to take capabilities that were demonstrated on the Jeopardy TV show and apply that cognitive reasoning to clinical care.

The complexities of disease combined with the mass of medical literature and clinical guidelines make this high dimensional problem an appropriate challenge for an industrial power house.

However, it should not be underestimated what can be achieved using sophisticated Natural Language Processing (NLP) for information retrieval in clinical decision support.

One of my favourite customer stories in recent years concerns our work with medical librarian Jonathan Hartmann from Dahlgren Memorial Library, the health sciences library at Georgetown University.

Jonathan’s role is to support the teams on the hospital’s paediatrics and internal medicine units on rounds at the Georgetown University Medical Center with access to the latest medical insights and publications relating to the current patient.

For example, should a patient with metastatic renal cell carcinoma be given warfin (an anticoagulant) for stroke prevention? Using his iPad at the bedside, Jonathan was able to quickly find journal articles that indicated cancer treatments and potentially cancer spread can indeed increase the risk of stroke.

You can read more about the story here.

From a technical perspective the use of NLP in this scenario is well hidden, as it should be, and simply ensures that the right information is provided to assist in clinical decision making.

Last year Georgetown University Medical Center launched the Center for Innovation in Leadership and Education (CENTILE).

In June I presented a poster at the first CENTILE  Colloquium for GUMC Educators in the Health Professions.

My poster Using iPads to Enhance Teaching and Learning on Patient Rounds explained how I have used iPads over the last four years on patient rounds to improve the education of medical students and residents at GUMC. I plan to continue to be involved with CENTILE in the future as I explore further innovative uses of technology in education.

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It’s always good to see NLP being used in a clinical care, a recent story about Microsoft and Washington University in Seattle using NLP in pneumonia detection in the ICU is a good example of this.

The project, called deCIPHER, uses a combination of Microsoft linguistics and machine learning to assess clinical information from electronic medical records and derive a diagnosis.

The system was trained against a cohort of 100 patients who had already been diagnosed with pneumonia and used a machine learning framework to build a predictive model based on extracted clinical factors. The system accurately predicted 84% of positive patients and the team are assessing incorporating the model into an ICU dashboard.

Last year Kaiser Permanente also published a paper on pneumonia diagnosis in relation to the ICU and using chest radiograph reports, using Linguamatics I2E for information extraction and also applying machine learning to the resulting clinical factors.

From a total of 194,615 ICU reports, Kaiser Permanente empirically developed a lexicon to categorize pneumonia-relevant terms and uncertainty profiles.