The greatest human advancement that has been witnessed in the 21st century is the advent of technology. Technology has literally penetrated into every sphere of our lives and become part and parcel of everything that we do. From how we communicate, to our shopping practices, our reliance on technology is astounding, to say the least. Our workplaces have not been left behind. We have seamlessly integrated our professional duties with technology with the aim of reducing labor and improving efficiency and effectiveness. However, this integration has been met with many challenges because computers do not understand human language. Therefore, there is only little that computers can do. However, all this is about to change with the invention of natural language processing (NLP). NLP is focused on the interactions between computers and human languages. How then can NLP be integrated into electronic medical record (EMR)-a critical area in the delivery of healthcare services? This essay presents an analysis of NLP, highlighting the advantages and possible disadvantages of integrating it with EMRs.
Overview of Natural Language Program (NLP)
As stated earlier, NLP is concerned with the interaction between computers and human languages. It is an agglomeration of artificial intelligence, computational linguistics, and human science. The development of NLP can be traced back to the 1960’s when computer scientists were researching on machine language translation. However, no single team of researchers can be credited with the development of NLP. NLP explores how computers can be used to manipulate human languages, both text, and speech, to conduct useful tasks. Currently, researchers are concerned with understanding how people understand languages so that appropriate tools can be developed for computers to learn languages in the same way. The applications of NLP are numerous. They include; machine translation, speech recognition, multilingual and cross-language information retrieval (CLIR) and natural language text processing and summarization
Possible Benefits of NLP to Medical Professionals Na Organizations
According to Roop (2012), several studies have proven that NLP tools and EMR information accrue numerous benefits regarding quality improvement. Roop (2012) goes ahead to state that NLP tools and EMR data are a ‘powerful combination.' NLP in healthcare is concerned with three areas i.e. converting dictated notes into well-planned data within an EMR database, clinical documents to come up with the most appropriate diagnosis and to improve the clinical decision support by examining clinical reference in relation to clinical queries. Apart from these, some healthcare facilities and practitioners have fully embraced NLP and are using it to their advantage, at the bedside; to identify illnesses, narrow care gaps and improve the overall clinical practice.
Medical professionals and organizations alike will find NLP useful in EMR content fulfillment. NLP will extract detached components from any data source e.g. amorphous sources and provide this information to the EMR database (Eramo, 2011). For example, if an organization has acquired an EMR, NLP can be useful in identifying different lists and move them to the EMR database. In the absence of NLP, this would have had to be done through the manual capture of the point-and-click template which is very tedious and not ideal for the clinical practice (Eramo, 2011).
Similarly, NLP can be used to abstract and report data. This comes in handy when dealing with large quality-related initiatives e.g. Physician Quality Reporting Initiative (Eramo, 2011). The NLP tools can automate to a higher degree the abstracting capabilities of an EMR network. The tools can comb through a library of documents and pinpoint the intended material. Doctors and organization will find that this simplifies their work and can do it with great ease. Through abstraction and reporting, physicians and organizations will have real-time patient information, perform complicated search queries and improve on clinical documentation efforts (Eramo, 2011).
Possible Disadvantages of using NLP Technology.
One of the primary problems of NLP technology has been its failure and the consequent failure of EMR systems. Over-reliance on NLP tools may lead to overall failure of the EMR system when the tools fail (Schaeffer, 2014). Sometimes, when these tools fail or get corrupted, they can distort the entire EMR database. The data stored in the database can be deleted and remain irretrievable. This loss of information can be a huge setback to both medical professionals and health organizations (Schaeffer, 2014). Also, NLP is very technical and would require the hospital staff to be trained. Training will add on operating expenses of the organizations.
Barriers to using NLP Technology
In spite of its numerous benefits, there are certain factors that act as obstacles to the acquisition and use of NLP technology. First, some of the tools are very technical. Therefore, many medical professionals and organizations shy away from using them (Schaeffer, 2014). Most of the tools are also open-source software. Their integration into EMR databases may pose a safety concern to data being stored (Schaeffer, 2014). Open-source softwares are prone to malicious attacks by hackers thus organizations are hesitant on using them.
Conclusion
In conclusion, NLP shows a great promise in automating EMR databases. Even with the numerous challenges associated with the technology, it continues to be a critical which when integrated with EMR will have infinite capabilities. Medical professionals and organizations should keep an open mind and acquire NLP and enjoys its capabilities.
References
Eramo, L. (2011). Natural Language Processing. Fortherecordmag.com. Retrieved from http://www.fortherecordmag.com/archives/042511p20.shtml
Roop, E. (2012). A Powerful Combination. Fortherecordmag.com. Retrieved from http://www.fortherecordmag.com/archives/100812p18.shtml
Schaeffer, J. (2014). NLP Shows Off Its Versatility. Fortherecordmag.com. Retrieved from http://www.fortherecordmag.com/archives/0414p24.shtml
