Before we talk about Natural Language processing in Healthcare, lets first define the term. Natural-language processing also knows as (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human or natural languages, in particular how to program computers to fruitfully process large amounts of natural language data. The history of natural language processing can be dated back to 1950. Natural Language processing is used in various industries like the transportation industry, educational industry, government agencies, the food industry, and also in the medical/healthcare industry. Use of Natural Language Processing in Healthcare is far from surface level. It is important and is used in every level of the industry from the simple linear activities like taking down patient records or communications between staff, to the deep multiplayer and sensitive medical activities like surgeries, organ transplants, the treatment of chronic diseases and sensitive research.
Natural language processing has existed for years in the healthcare industry but that doesn’t necessarily mean that it’s impact has been felt. Incremental progress is being made but there still exists fundamental gaps in the industry’s data ecosystem—missing pieces of the data puzzle—that inherently limit what can be achieved with natural language processing. One of the major ways natural language processing has been revolutionized is the way it’s been used by Google. Google revolutionized the world of NLP, but Google leveraged a metadata ecosystem that is layered on top of traditional NLP strategies to achieve the revolution. In healthcare, there is no access to the same or a similar metadata ecosystem within the current generation of electronic medical records, much less across EMRs. In today’s EMRs, the technology used is pretty simplistic. Hopefully, eventually the company's like Google, Facebook, and Amazon of the world will quietly build a new generation EMR, but with $29B in federal money now squandered on our existing generation of EMRs, there’s very little motivation in the market for innovation.
As with many advanced technical aspects of the medical and healthcare industry, there is a relationship between natural language processing and other technologies. One of those is the merger and blend of natural language processing and comparative data. Comparative data has existed for a long time in the healthcare system and industry in the United States but it hasn’t been developed on a lot. We’ve had comparative data for years in the U.S. healthcare system and it hasn’t moved the needle towards better, at all. In fact, the latest OECD data ranks the U.S. even worse than we’ve ever been on healthcare quality and cost. Comparative data, like the OECD, is interesting and certainly worth looking at, but it’s far from enough to drive improvements in an organization down to the individual patient. To drive that sort of change, you have to get your head and hands dirty in your own data ecosystem, not somebody else’s that is at best a rough facsimile of your organization. There are too many variables and variations in healthcare delivery right now that add too much noise to the data to make comparative analytics as valuable as some pundits advocate. There is also no industry standard on what the precise clinical definitions should be and which should be used.
In conclusion, one question that should be asked is “is new technology really useful if it can’t be used?” Take for example, To sequence the entire DNA of a patient may prove to be an incredible advancement in genomic medicine. Yet when changes in their DNA can’t be communicated in a clear and concise and timely manner to the health care provider who needs that information, the chain of utility for that technology is broken. Beyond that, even if the full information gets passed along efficiently, but lacks the context of metadata or interpretation of what it means and what to do about it, again the remarkable technology has fallen short of its full potential.
The sometimes not-so-obvious irony is that without having the proper technology framework in place new technology is really not very useful. In fact, it is often a waste of time and money.