NLP’s potential to be Sherlock Holmes in enhancing our Watson minds

Healthcare has an unfathomable amount of data — medical histories, chart notes, prescriptions, medical claims data, lab results and imageries, data from clinical trials, the list is never-ending.

Let this be on one hand. On the other hand, let’s take the enormous amounts of medical literature that is available — journals, articles, dissertations, conference proceedings documentation, textbooks, handy books and so on. When a patient comes into treatment for his ailment, would a physician be able to leverage all that there is in both his hands and provide an accurate diagnosis, treatment, and prescription? It’s easier when the ailment is commonplace, but what if it is an unknown disease.

It may be unknown to the physician but its clinical symptoms might have been recorded from some other corner of the world, in some other format. Its treatment method might either be hidden somewhere in the ocean of data or it might be a combination of drugs/treatment methods of two different but related diseases. NLP based cognitive computing powered by Artificial intelligence and Machine learning algorithms is here to bridge the gap between the incalculable amount of data and the limited cognitive capability of the human mind.

Listed below are some of the key areas where we could apply our expertise in NLP, AI, and ML for the wholesome betterment of the healthcare cycle.

Reduce the Physician Irritability Factor: Let’s admit it. Most physicians dread the usage of EHRs and are averse to the endless number of clicks and documentation required of them to do, for each patient. We ideate that NLP can be leveraged to search the physician’s clinical voice notes for keywords, interpret and map it to the respective fields in the EHR thereby enabling an intelligent and automated data entry process. Physicians can thus be relieved from redundant tasks and concentrate more on the complex issues at hand. Also, spend more time with patients.

Summarizing the Essence of lengthy articles, notes, journals, and dissertations we feel is another crucial task that NLP can manage proficiently. NLP algorithms can scan several hundred journals in minutes and provide a brief to the physician that will help them understand the core concepts and would lessen the time they need to invest drastically.

Play the role of an Oracle by capturing data from innumerable sources, collating them, comprehending the questions asked in a free text format, fetching the relevant information, synthesizing, and presenting it to the physician. Here NLP’s implementation is parallel to a chatbot but much more sophisticated, a virtual embodiment of an all-knowing medical oracle is what we envision.

Perform Visual Miracles by engaging complex optical character and image recognition algorithms, NLP can be made to perform analysis on lab imageries and scan reports, convert them into free text and present them in a ready state for analysis of the physician. Again, considering the amount of time it could save, we strongly feel this could be another important focus of NLP.

The seamless billing process is something that healthcare providers and patients yearn for. NLP can be used to distill relevant information from physician notes and assign them appropriate medical codes and facilitate an automated billing process.

Be Armed and Ready to handle a spike in clinical data and medical attention. With the adoption and widespread implementation of EHRs, the amount of medical data has grown astronomically. To make sense of these data and to draw sensible insights is fundamental to provide definitive healthcare solutions to a large populace. NLP can help lay a road in achieving that.

A personalized secretary who is not just adept and quick in typing clinical text but understands the medical jargon and produces a comprehensive clinical note such as a discharge summary. This is another avenue where NLP can alleviate the frustrations that physicians have come to put up with in terms of writing up details.

Streamline the mountains of unstructured and unusable data. Big Data analytics shows that almost 80% of the healthcare documentation is unstructured and hence goes predominantly unutilized or underutilized. But with NLP we can sieve out the clamor, extract and put to use pertinent data for the betterment of the quality of healthcare delivery

The Decision Maker is perhaps the role of a lifetime for NLP. It is a game-changer in the field of diagnostics and by involving predictive analytics it could do you a solid, like the precogs from Minority Report, although here the predictions are not premonitions, but gathered facts from various sources of data.

By assembling essential information from a plethora of literature and medical history it has shown the capability to aid physicians with accuracy in predicting at-risk patients and also provide valuable, lifesaving insights especially for the critical point of care decisions for the most complex patient problems.

Here are some striking real-time examples for NLP implementations in healthcare in the recent past and important market forecasts regarding the investment prospects in the NLP domain.

  1. The Department of Veteran Affairs has used NLP techniques to foresee the risk of PTSD in veteran patients. NLP algorithms were used to review about 2 billion EHR records and the pilot program deployed was 80% accurate in finding people who were at the risk of suicide.
  2. Researchers at the Massachusetts Institute of Technology were able to achieve a 75% accuracy in using NLP to comprehend the semantic meaning of specific clinical terms, from a text that was peppered with ambiguous terms and varied contexts, using a statistical probability model.
  3. The R&D department at the University of Arizona used NLP to review the medical data of several thousand patients and concluded that it was 22.6% more accurate in discerning patients who had to be included in the Cancer Registry Control Panel and those who need not be when compared to manual reviews of the same data.
  4. McKinsey used NLP to fasten the process of benchmarking clinical guidelines and achieved a 60% decrease in the time required for the synthesis.
  5. Allied Market Research predicts that the NLP market will be worth about $14 billion across multiple industries by 2021 representing a 38% growth rate based on current levels. The healthcare industry is forecasted to contribute 40% of the total market revenue.
  6. Also, the same firm states that text analytics in NLP will see an investment of $6.7 billion in 2021.
  7. A survey by Markets&Markets states similar statistics with the NLP market growing to $16.7 billion in 2021 and healthcare vertical being its backbone.

Gaining knowledge is the first step to wisdom, sharing it and putting it to the best use possible, is the first step to humanity. Mankind now has centuries worth of medical knowledge. With new ingenuity popping up from every part of the world, every other day, it is only appropriate that we use every bit of information we gain.

An intelligent step forward and investment in technology such as NLP is the way to do that. Make NLP your Man Friday. NLP’s prowess and potential is not just a postulate; it is a proven fact.

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