“AI is technology’s most important priority, and health care is its most urgent application,”– Satya Nadella, Microsoft CEO, during the acquisition of their new company. In recent years, AI has been expanding rapidly throughout the globe and catering for almost every sector.
Managing patient data in the modern healthcare industry is complex and time-consuming, particularly for Medicare Advantage (MA) organizations. Here, technological advancements like Natural language processing (NLP) came out as a boon for the healthcare industry.
In this blog, we will examine the NLP technology and how it streamlines patient chart review & audit tasks.
What Is Natural Language Processing?
NLP is one of the subfields of AI that uses statistics, linguistics, and machine learning to provide the computer’s capability to recognize human speech. NLP-powered systems have the potential to derive meaning from what’s written or said, with all the nuances and complexities of the natural narrative text. It enables the machines to extract or derive the values from unstructured data.
The healthcare industry is increasingly adopting Natural Language Processing (NLP) due to its immense potential in searching, analyzing, and interpreting massive amounts of patient data sets. By leveraging advanced medical algorithms and machine learning, NLP can extract valuable insights and concepts from clinical notes previously hidden in unstructured text data.
This technology has the potential to uncover critical information that was previously overlooked, providing deeper insights into quality, improved methods, and better outcomes for patients. NLP can accurately process unstructured healthcare data, giving healthcare systems an incredible tool to unlock valuable insights and optimize patient care.
Who Is Using NLP in Healthcare?
A diverse range of healthcare professionals, including physicians, nurses, pharmacists, and administrators, are utilizing medical NLP to streamline workflows, enhance predictive analytics, and alleviate administrative burdens.
NLP in healthcare can automatically extract necessary data within medical records, saving practitioners valuable time and energy that would otherwise be spent searching through lengthy documents. Furthermore, NLP has been utilized in healthcare to automate clinical note summarization, which can provide decision-making support and facilitate data mining for population health insights.
Due to its broad applications in healthcare, the NLP system has emerged as an indispensable tool for optimizing clinical workflows and clinical trial matching efforts throughout the sector.
Leveraging NLP for Medical Chart Review and Auditing
NLP can significantly streamline patient chart review and audit tasks for MA organizations by automating coding, prioritizing charts, extracting key information, risk stratification, and fraud detection. By leveraging these capabilities, MA organizations can improve the quality of care, reduce costs, and enhance overall efficiency. It can streamline patient chart review and audit tasks for MA organizations in several ways:
Automated Chart Review: NLP algorithms can be trained to automatically extract relevant information from electronic health records (EHRs), such as patient demographics, medical history, diagnoses, procedures, and medications. This can save healthcare providers time and effort in manually reviewing charts and reduce errors.
Prioritizing Charts: NLP can prioritize patient charts based on the urgency and importance of the information within them, allowing MA organizations to focus their review efforts on the most critical cases first.
Extraction of Key Information: NLP can extract meaningful information from clinical notes, such as patient history, symptoms, and medications, and provide MA organizations with relevant and timely insights for care management and quality improvement efforts.
Risk Stratification: NLP can identify patients at higher risk for adverse events, such as hospitalization or readmission, by analyzing their clinical notes and identifying patterns that may indicate increased risk. This can help MA organizations proactively intervene to prevent adverse events and improve patient outcomes.
Fraud and Abuse Detection: NLP algorithms can detect fraudulent activity, such as billing for services not provided, duplicate claims, or upcoding. This can help healthcare providers identify and prevent fraudulent activity and ensure compliance with regulations.
RAAPID is a healthcare risk adjustment solutions provider using artificial intelligence and machine learning to help MA organizations optimize their Risk adjustment (RA) coding workflow. Their technology can help identify and prioritize patients at higher risk for certain health conditions, improve documentation accuracy and completeness, and streamline the overall risk adjustment process. In addition, RAAPID.AI helps MA organizations improve the level of ICD-10-CM coding and HCC code review and audit accuracy along with efficiency.
Their personalized HCC compass is an AI -Powered Risk Adjustment Coding and Audit Solution, enabling MA organizations to leverage NLP and deep learning to understand clinical charts and claims. Furthermore, it’s retrospective chart review solution allows you to identify unclaimed, over-claimed, and properly coded conditions.
Concluding Remarks
NLP in healthcare presents an opportunity for computers to perform various tasks such as HCC risk adjustment coding, back-office functions, analytics, and transforming unstructured data into structured data without interfering with physician communication.
In addition, medical NLP tools are expected to play an increasingly significant role in healthcare organizations as intelligent assistants for MA organizations, enabling them to process vast amounts of unstructured text data efficiently and make informed decisions, leading to improved predictive analytics too.
