Information Special Issue : Natural Language Processing in Healthcare and Medical Informatics

As healthcare providers strive to deliver patient-centric, efficient, and evidence-based care, NLP emerges as a powerful tool to unlock the potential of vast amounts of data. By leveraging NLP, healthcare organizations can enhance clinical decision-making, improve population health management, and deliver personalized care to patients. NLP is not just a technological advancement; it is a transformative force in revolutionizing patient care and shaping the future of healthcare. The John Snow Labs Healthcare NLP Library offers a comprehensive collection of pre-built Natural language processing models and tools designed specifically for healthcare applications. The free version grants access to foundational Natural language processing models, supporting tasks like text classification, named entity recognition, sentence segmentation, and sentiment analysis. Users can explore these capabilities without any cost, and a 30-day free trial on the AWS Marketplace allows individuals and organizations to evaluate the software’s impact on their healthcare workflows, risk-free.

Risk adjustment models such as hierarchical condition category coding will be increasingly used. The objective of NLP is to make the complex, varied and ambiguous human language easy to understand for machines. The technology works by applying linguistics to analyze grammar and meanings of each word before leveraging algorithms to create systems capable of carrying out the various tasks.

The Rise of NLP in Healthcare

For example, if it takes physicians the longest to find and review radiology reports, start structuring data in that area first. Integrated with 3M’s Coding and Reimbursement system, Encompass provides multiple reporting capabilities with KPIs (potentially preventable readmissions/ complications, patient safety indicators, etc.) and analytics out of the box. The tool’s NLP decisions are also delivered using the principles of explainable AI, meaning that the coders can see the evidence behind the system and make adjustments.

When the doctor sits down with you, and documents your visit in clinical notes, those narratives go into the electronic health record systems (EHRs) and get stored as free text data. Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables machines to understand and communicate in natural language, like humans do. It involves using algorithms and techniques such as machine learning, deep learning and text analytics to interpret and analyze natural language content from audio recordings, documents, images or other sources.

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In the healthcare industry, electronic health records and physicians don’t always get along well. Researchers analyzing health systems often conclude, physicians suffer from EHR burnout and threaten to retire from service early rather than suffer through the many clicks and screens required to navigate their EHR. Medical NLP is steadily proving to be a solution to this challenge since NLP healthcare tools can easily access and accurately interpret clinical documentation like clinical notes.

• Because of its versatility, NLP can structure virtually any proprietary or public data to spark insights in healthcare, leading to a wide variety of downstream applications that directly impact patient care or augment and accelerate drug discovery.
• Learn how these insights helped them increase productivity, customer loyalty, and sales revenue.
• While AI-based CAC is showing excellent results, it’s currently used in a hybrid model, assisting human coders in routine tasks, freeing up more time for them to focus on some gray areas, which often come up in the field of medical coding.
• Since the machine model that Repustate developed for them read and analyzed the data natively, it did not dilute the nuances of the Arabic text.
• Healthcare natural language processing uses specialized engines capable of scrubbing large sets of unstructured data to discover previously missed or improperly coded patient conditions.
• The real value comes from combining text data with other health data to create a comprehensive view of the patient.

Also, natural language processing is being leveraged to help healthcare professionals perform diagnosis and check for symptoms. In the medical field, vast volumes of textual data are generated, such as clinical notes, health records, research articles, and patient reports. Manually sorting and categorizing this unstructured data is time-consuming and error-prone. NLP medical classification addresses this challenge by automatically organizing data and assigning relevant labels, enabling efficient retrieval and utilization of medical information.

Published Papers (2 papers)

The research of NLP methods still heavily relies on the advancement of machine learning models. Ultimately, NLP in healthcare will be a crucial component of our health systems moving forward. The accuracy of medical natural language processing goes up along with the volume of clinical documentation available for learning. The more a medical NLP platform is used, the more accurate using Artificial Intelligence in healthcare gets, since it’s always learning, and in some cases, can be customizable.

NLP-powered systems can derive meaning from what’s said or written, with all the complexities and nuances of natural narrative text. ForeSee Medical’s unique combination of machine learning technology and risk adjustment rules delivers industry leading NLP accuracy scores. See how using natural language processing technology natural language processing examples can help you capture all appropriate HCC categories and get the Medicare reimbursements you deserve. Most natural language processing healthcare engines are built to accommodate a wide variation of medical notation terminology. However, using uncommon acronyms can confuse NLP coding algorithms and other medical note readers.

Natural language processing in healthcare

It’s also prime for adoption with many clinics already reaping the benefits of data analytics and showing outstanding results. Many of those results are possible thanks to data derived using natural language processing. Such admin tasks as prior authorization of a patient’s health plan and claims processing contribute to the aforementioned burnout and high billing and insurance-related (BIR) costs.

Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation. From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches.

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Technology and computation have changed the backdrop of various aspects of our fast-paced lives. Healthcare is one such aspect that has been affected by this change and faces new challenges every day including the challenge of extracting relevant and valuable information from the enormous amount of data that is generated endlessly in this sector. Smart data analytics provides a solution to this problem through the use of Artificial Intelligence and Natural Language Processing (NLP).

Sun et al [13] built an automatic method to identify the clinically suspected claims for fraud, waste, and abuse detection by using a medical knowledge graph. A deep learning–based method was applied to extract the entities and relationships from knowledge sources, and a multilevel similarity matching method was developed for entity linking. From 185,796 drug labels from the China Food and Drug Administration, a medical knowledge graph containing 1,616,549 nodes and 5,963,444 edges was constructed for identifying fraud, waste, and abuse suspects. The research of health knowledge graphs still has the challenges of complex text representation, low extract performance, and limited knowledge graph size.

Choosing between the Healthcare Natural Language API and AutoML Entity Extraction for Healthcare

Additionally, natural language processing in healthcare has been used to automate the summarization of clinical notes for improved decision-making support and data mining for population health insights. As a result of its many applications in healthcare settings, the NLP system has become an essential part of clinical workflow optimization and clinical trial matching efforts across the sector. As the healthcare industry landscape evolves with digital transformation, providers are constantly seeking innovative solutions to navigate the challenges of regulatory compliance, financial constraints, and the increasing burden on clinicians. One technology that has emerged as a game-changer in this domain is natural language processing (NLP).