How Does Natural Language Processing Help in Unlocking Healthcare Data?


The healthcare industry has always been at the forefront of technological advancements. As the industry deals with a large trove of data, it leverages digitally advanced technologies to deliver an effective patient care experience. Natural language processing (NLP) is one such technology focused on the analysis and administration of human-generated data. As a crucial branch of artificial intelligence (AI), NLP significantly helps in information extraction, document categorization, data conversion, and summarization. 

NLP systems learn and advance over time by reabsorbing the outcomes of previous interactions as feedback to interpret better decisions for the future. In the healthcare landscape, NLP has a significant impact and numerous applications. One such application is clinical documentation and healthcare data where this technology plays a crucial role. NLP has the potential to enhance the inclusiveness and precision of electronic health records (EHRs) by unlocking insights from unstructured data and translating them into meaningful data. 

As per recent studies, physicians spend more of their work hours in the EMR. Clinicians spend two hours on clinical documentation for each hour spent in-person meeting with patients. This is one of the cited key factors contributing to medical personnel burnout. Fortunately, today we have NLP-powered virtual assistants such as Alexa and Google’s Assistant that help physicians with automated clinical processes. 

NLP and Its Impact on Healthcare 

Natural language processing is one of the technologies gaining rapid traction in healthcare in terms of adoption. This is mainly because of its potential to search, assess and interpret voluminous amounts of patient data. By using advanced medical algorithms, NLP harnesses insights and concepts from data that was previously concealed in text form and not possible to extract. It accurately construes unstructured data and gives voice to that data, providing incredible insights into understanding quality, enhancing methods, and better results for patients. 

Since physicians spend a lot of time treating their patients and taking notes to their cases, those notes go into the EHR systems and get stored as free text. As these patient data generally are in unstructured form, it is hard for computers to aggregate that critical data. Reports show that over 80 percent of healthcare documentation is unstructured and largely unutilized. This is where NLP comes in, making all types of data into a usable form that can help physicians make strategic decisions. 

NLP and Its Use Cases in Healthcare 

As the branch of AI, natural language processing is critical for navigating through the large volume of data generated daily in the healthcare system. It also has numerous use cases that have an immediate impact. 

1. Clinical Documentation – NLP systems are expected to improve clinical documentation, EHR usage, and provider workflow. In a study, researcher David R. Kaufman, PhD, noted that “EHR documentation places ever-increasing demands on clinicians’ time, which contributes further to diminished quality of documents (e.g., replete with irrelevant, redundant, and erroneous information) and physician dissatisfaction.” As per the study, NLP has considerable potential in clinical documentation improvement. 

2. Computer-Assisted Coding – It refers to the use of computer software to produce a set of medical codes from the documentation. The outcomes derived from it are then presented to the coder that verifies, validates, and reviews it. By using natural language processing, computer-assisted coding promises to enhance the accuracy of the coder to excerpt information about procedures and therapies to capture every code and maximize claims.  

3. Speech Recognition – NLP combines linguistics and machine learning. It is considered the core AI technology. With advancements of NLP, it has expanded its use case to speech recognition in healthcare by allowing clinicians to transcribe notes for valuable EHR data entry. As the front-end speech recognition removes the task of physicians to command, back-end speech recognition works to spot and correct inaccuracies in the transcription before passing it to humans for proofing. 

4. Data Mining – By leveraging NLP in data mining, healthcare organizations can create insights to form precision medicine for the better treatment of patients with the rare or new disease based on their genotype-phenotype history. Medical professionals or clinicians can identify and deliver effective treatment to patients by understanding their EHR, environmental, and physical factors they live in. 

5. Imaging Workflow – Identifying, comparing, and reporting patient reports can be a time-intensive and tedious task as radiologists evaluate thousands of reports. Integrating NLP with image recognition algorithms can help detect the disease identification marks in various images on a speech annotation. Bialogics Analytics Inc., for example, last year introduced a new deep learning-based NLP engine that seamlessly integrates into radiology and cardiology PACS workflows. It gives providers the tools they require for advanced mining and analysis of structured and unstructured data contained in radiology and cardiovascular imaging and information systems to stimulate workflow optimization and clinical research for real-time identification of patient cohorts. 

6. Automated Registry Reporting – Today, most health IT systems are burdened with regulatory reporting when measures such as ejection fraction are not stored as distinct values. To capitalize on automated reporting, health systems need to identify when an ejection fraction is documented as part of a note. They also need to save each value in a form that can be used by organizations’ analytics platform for automated registry reporting. 

Natural language processing in healthcare has a critical impact on delivering effective care and an advanced solution. It could help healthcare organizations to translate human speech and make it understandable and logical by excerpting meaning out of the information transmitted. NLP tools also help them harness large chunks of data stored in the form of speech, text, images, and pictographs and derive valuable insights. 

Final Words 

The healthcare industry is a data-driven industry where data plays a critical role in delivering effective patient care. The data collected generally takes place in the form of both structured and unstructured data. However, there is a higher need to leverage that data for value-based care delivery. NLP can extract relevant information from any data type, helping in driving strategic decision making and administrative cost reduction with healthcare value creation. 

With the increasing adoption of NLP, it is predicted that the NLP applications in healthcare will intensify exponentially, with the market growing from $1.3 billion in 2016 to $2.65 billion by 2021 at a CAGR of 20.8%. 

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