Assignment 8 Final

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1 Assignment 8: Natural Language Processing (NLP) Janel Handy University of Maryland Global Campus HIMS 661: The Application of Information Technology in Healthcare Administration Dr. Craig Drayden July 14, 2023

2 Natural Language Processing (NLP) Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written which is referred to as natural language, a component of artificial intelligence (Burns, 2023). Therefore, NLP models are able to be utilized by finding relationships between letters, words, and sentences that are found in text datasets. NLP helps to streamline business operation and improve employee productivity. In order for applications to perform successfully, programmers have to develop tools that teach natural language driven applications to recognize and understand accurately from implementation. Natural Language Processing Tasks, Tools, & Approaches NLP tasks are helpful to assist with breaking down human text and voice data to help computers process the language. An example of tasks are speech recognition, name entity recognition, and natural language generation. Speech recognition which is referred to as speech txt has the ability of converting voice into text which is required for any application that follows voice commands or answers spoken questions (IBM, n.d.). Named entity recognition (NEM) uses words or phrases as useful entities. For example, they have the ability to recognize Maryland as a location. Another useful task is natural language generation which does speech to text and takes information into the human language. There are two useful tools that are utilized for NLP which are spaCy and Natural Language Toolkit (NLTK). NLTK programming language utilizes a vast number of tools for certain NLP tasks. Many of the tasks are found in the NLTK which is a useful resource of libraries, programs, and education resources for building NLP programs (IBM). SpaCy is considered one of the most versatile NLP approaches. This tool supports approximately 66

3 languages which uses pre-trained word vectors for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization (Deep Learning AI). SpaCy is considered one of the more and efficient and faster tools which has become widely used. There are three type of NLP approaches which are rule-based approach, machine learning approach, and neural network approach. Rule-based approach is known as one of the oldest methods which helps to process textual data. The benefit of rule-based approach applies a particular set of rules or patterns to capture specific structures, extract information, or perform tasks such as text classifications. There are four steps to complete rule-based approach which are rule creation, rule application, rule processing, and rule refinement. In this approach, the rules are manually created and relies on linguistic or domain-specific knowledge but this approach can be challenging to handle complex language. A disadvantage of this approach is that it does not handle complex language applications. How to extract useful information using NLP The use of NLP plays a vital role of extracting diagnostic codes such as ICD-10-CM and codes CPT 4.0 from summary notes to reimburse claims. Staff follow a process when extracting information and the first step is data pre-processing. Data preprocessing is used to clean and standardize the text which ensures accuracy during the extraction process. Named entity recognition (NER) which involves identifying key information in the text and classification of a set of predefined categories. For progress notes, NER is useful for extracting medical terminology and information that is useful for coding (Deep Learning AI). Implementing rule based approaches are useful to help associate patters within progress notes that are corresponding with diagnostic codes. When the codes are successfully extracted techniques can be developed to

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4 assist with linking them to the appropriate ICD-10-CM codes. The advantage of the extraction process is that it can assist with improving document quality and completion of progress notes. Providers and staff should be made aware that use of this tool should be utilized as more of support and not replacement of staff. Accuracy of NLP Information Information accuracy is based on information that is extracted for natural language processing which is based on various factors such as complexity of the test, quality of training data, and certain NLP techniques. An example of a system that is useful to improve accuracy is Generative Pre-trained Transformer. Generative Pre-trained Transformer are language models that use deep learning to produce natural language texts based on given input (Kivindyo, 2023). Transformers are useful when sequence data needs to be generated. This useful tool generates the next word in the sequence by analyzing probability of potential next words. Choosing this model or a similar model will improve their accuracy by adapting the language to terms that are utilized in the healthcare industry. Research has shown that certain techniques does improve accuracy there is no way to have total accuracy only to help enhance the process. Analyzing need for medical coding professionals NLP will not eliminate the medical coding profession because there will still be a need for human medical coders. Medical coders are working with a plethora of codes that can correspond to various situations depending on the patient. Due to the various variables of medical coding NLP is not sophisticated enough to handle complex cases. For example, there are different codes that are utilized depending on whether the diagnosis is initial visit or follow-up visit which only a medical coder would be able to correctly decipher. Consequently, NLP does

5 not have the capacity to correctly decipher procedures that are not as straight forward. Also, medical coding is continuously being updated with policies regarding standards and regulations. Medical coders are required to stay abreast of the most up to date polices and regulations whereas it can be time consuming to try to ensure the NLP tool is updated with all changes. Proper communication is essential amongst providers, clinical staff, and medical coders. Therefore, medical coders may need to communicate with staff concerning additional information needed or ensuring that all information is interpreted correctly. NLP systems does not have the capability to request additional information which would delay the workflow. The use of artificial intelligence is very useful but there are more advantages to collaborating medical coders with artificial intelligence NLP systems. Health Information Management (HIM) Mangers utilizing NLP NLP system would provide significant support to Health Information Management (HIM) managers by helping to improve and streamline their work flow. HIM managers could utilize the NP tool to improve quality assurance audits. The system could analyze documentation and data by discovering discrepancies and identifying potential medical or coding errors. The use of this tool will help managers to enhance their policies and regulations and improve their processes. NLP tools are useful to help managers analyze documentation and recommend appropriate diagnostic codes based off of the information that is provided. Utilizing an NLP tool will help to improve process which allows HIM manager to focus on managing their team and handling serious issues. Therefore, NLP can be advantageous for HIM managers which also helps improve accuracy for medical coding staff.

6 Future of NLP in the HIM profession The future of NLP in regards to the HIM profession will be beneficial. The healthcare industry is constantly evolving and there are always areas to improve and the use of NLP will provide significant benefits. NLP will continue to improve accuracy with coding and review of documentation. Collaborating NLP and electronic health records and other technologies are continually to emerge to enhance interoperability and data integration. Organizations will receive optimal results if they utilize NLP to complement their current processes to improve tools and technologies that create a more efficient system.

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7 Reference Bresnick, J. (2012, October 31). Natural language processing presents opportunities for clinical coding improvement. Tech Target. https://ehrintelligence.com/news/natural-language- processing-presents-opportunities-for-clinical-coding-impr Burns, E. (2023, January). Natural language processing . Tech Target. https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing- NLP Deep Learning.AI (n.d). Natural language processing . Deep Learning.AI. https://www.deeplearning.ai/resources/natural-language-processing/ Kivindyo, A. (2023, February). What are generative pre-trained transformers (GPTs). Medium. https://medium.com/@anitakivindyo/what-are-generative-pre-trained-transformers-gpts- b37a8ad94400 IBM (n.d.). What is natural language processing (NLP). IBM. https://www.ibm.com/topics/natural-language-processing Payne, T., Aidan, G.H., Kirkegaard, S., Sweeney, J., Ash, M., Kailasam, K.K., Hall, C.L., Sinanan, M.N. (2011). Natural language processing improves coding accuracy . MGMA Connexion, (11) ,9, 15-17.

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