5 Applications of NLP in Healthcare

Introduction

 

Healthcare industry demands fascinating technology advancement for sustaining value-based treatment for each patient. One of the appropriate technologies is NLP.

 

Healthcare data includes patients health data like report status, past and present medical records, symptoms, and diagnoses, etc. NLP, in the healthcare industry, could help in transforming raw data into valuable insights for enhancing outcomes. 

 

Artificial intelligence (AI) has been adopted across the healthcare industry rapidly, and most of the AI applications leverage NLP for smooth execution. Being a specialized branch of AI, NLP serves as a promising tool for healthcare payers and providers. 

 

Through this blog, we will explore the significance and applications of NLP and its use cases in healthcare and discuss the essence of the knowledge graph that accelerates benchmarking in the healthcare industry.

 

 

Significance of NLP in Healthcare

 

Natural Language Processing is the capability of computers in order to understand novel human speech terms and text. In terms of the latest technology, it can be used to detect spam emails, maintain personal voice assistants and language translation applications.

 

Put simply, NLP is the subsection of AI that interprets and controls the human-generated data which is either in spoken or written forms.

 

The enactment of NLP in healthcare is increasing due to its perceived potentiality for exploring, deciphering, and interpreting the massive amount of patients’ data. Implementing advanced ML algorithms and NLP technology embrace the ability to harness the data for extracting relevant insights. 

 

Though, NLP in healthcare can precisely deliver significance to the unstructured data, providing unbelievable insight into understanding aspects, enhancing analyzing methods, and yield exceptional outcomes for patients.

 

Healthcare NLP employs specific models that are capable of cleaning large unstructured health datasets for scrutinizing prior to missed or inadequately coded patient conditions. NLP medical records, that are implementing ML algorithms, could unfold diseases that are not coded previously.

 

Following are some fundamental tasks that NLP can perform; 

 

• Ciphering tedious unstructured clinic data/records.

• Mapping appropriate data from unstructured data into structured data in Electronic Health Records(EHR).

• Transforming data into the natural language from machine-readable formats, for describing and training.

• Responding to text questioning via natural language interface.

• Optimizing the medical images into important insights and reports.

 

Through this way, NLP could become a backbone for the healthcare industry in the context of making certain decisions and therefore advance the overall condition of healthcare providers to the patients. 

 

(Related reading: Healthcare data analytics)

 

Discussing the application of NLP in Healthcare

 

1. Managing the flood of clinical data

 

Right from the current and past health record, their exponential increment and utility, and the digital transformation of medicine has led to a huge jump in the volume of data with healthcare organizations. 

 

The necessity of making sense from this data and extracting valuable inferences have become crucial. Therefore, the advanced system is required for managing the flood of this clinical data where NLP would be a suitable tool. 

 

2. Encouraging value-based treatment and Population Health Management

 

As the saying for any sort of data, “the switching in distinct business models and respective outcomes/expectations is prompting the requirement for extensive use of unstructured data”, so in medical data. 

 

Talking about conventional health systems that could extract value from a minor amount of healthcare data, in a structured manner via clinical modes.

 

But, now NLP can address these obstacles in various ways, such as;

 

• Refining clinical documentation: EHR solutions have a complicated data structure and documenting data into them is often a nuisance. 

 

So, with the help of speech-to-text interpretation, data can be seized automatically that make physicians free from the lengthy task of delivering documentation carefully. 

 

• Forming CAC more active: CAC stands for computer-assisted coding that can be enhanced with NLP efficiently. CAC draws insights regarding methods in order to obtain codes and maximize applications and helps medical personalities to make the change into value-based models from fee-for-services systems that modify patient experience automatically. 

 

 

3. Advancing interaction amid patient-provider with EHR(Electronic Health Record)

 

At the present scenario, patients consistently seek special attention from their corresponding healthcare service providers. Therefore, various organizations including doctors offer personalized services while handling onerous documentation along with billing records and services. 

 

In the same line, most of the organizations have already deployed virtual assistants (Siri, Cortana, and Alexa) into administrative support, assisting in customer services duties and reducing desk burdens. 

 

However, on the same action, with the help of intelligent medical algorithms, NLP could be used for making smart models/ virtual assistants across the clinical level of the healthcare industry in the form of ordering assistants and medical transcribers. 

 

EHR practices and various responsibilities, performed by medical bodies such as documentation and billing, generally lead to overload as work in the traditional healthcare space. For this, implementing NLP will be the leading choice.

 

 

4. Identifying patients who seek specialized treatment

 

Without any doubt, ML techniques and NLP tools hold the potential for detecting patients with health complexity, i.e, the patients who have the memoir of mental illness, or body impairment, who need specialized care. 

 

Since the healthcare industry makes both structured and unstructured data, therefore it is significant, for healthcare companies, to perform data cleaning and implement NLP models for providing better care to patients.

 

 

5. Authorizing patients with health knowledge

 

Advancing healthcare with trending technology becomes a key feature in the industry. 

 

For example, AI has already occupied the arena in the healthcare ecosystem due to the capability to assist patients to grasp their symptoms and acquire extra information concerning their health situations. 

 

Also, after becoming conscious about their health conditions, patients themselves make informed decisions and resolve their health conflicts, maintain health on track through interacting with a smart chatbot. 

 

In this situation, NLP could improve the understanding of the patients over the EHR platform by unlocking possibilities in order to make them more aware of their health.

 

(Must read: Top healthcare technologies)

 

 

What is the Knowledge Graph in Healthcare?

 

It is crucial to discern the interrelationship amid several healthcare actualities, for example, doctors, patients, hospitals, medicines and medical data, government or corresponding authorities, in order to apprehend the indicated inferences and essential factors incorporated with it.

 

A health knowledge graph is the extensive interconnection of various datasets that are related to these actualities. It enables an individual to examine/probe defined or predefined hypotheses via NLP. 

 

Moreover, a knowledge graph encodes actualities, also known as concepts, and maps the corresponding relationship to one another. All these relationships build a data-web that can be deployed while computing applications and enabling NLP systems to think about medicine, similar to how a human might.

 

A knowledge graph contains most of the information regarding the general and specific medicines, critical illness and diseases with reference to patients conditions where it presents means for proficient physicians in order to communicate information and knowledge efficiently and accurately. For example, a highly expert doctor can transmit knowledge to less experienced personalities.

 

Another benefit of the graph includes the storing of clinical information, captured. It enables the array of graph algorithms to scrutinize buried patterns existing in the data. Also, the knowledge graph proffers vital advantages over conventional traits, for example, the speed at which the activities take place.

 

(Suggested blog: AI applications in healthcare)

 

 

Conclusion

 

While concluding the blog, we have seen how new and evolving applications of text analytics and NLP have been deployed in the healthcare industry. 

 

Each healthcare database is expanding exponentially where NLP systems turn this data into value. However, healthcare providers, pharmaceuticals enterprises and biotechnology firms all deploy NLP for boosting patient outcomes, streamline operations, and controlling regulatory conformity. 

 

NLP in healthcare is making unique and interesting opportunities for healthcare offering and patient experience.