The natural language processing (NLP) market worldwide is predicted to grow 14 times larger than what it was back in the year 2017. It is expected to grow from $3 billion back then to $43 billion in 2025.1
As we start rolling up our sleeves to get over quarter two (Q2), several technologies are continuing to transform healthcare soon. Payers must be coerced to adapt with many abatements coming sooner than later.
Healthcare payers are redefining their role to deliver higher value-based care to patients as many healthcare organizations are transitioning from a fee-for-service (FFS) model to a value-based care model. In the healthcare industry, natural language processing is the dominant artificial intelligence (AI) application centered on reviewing and auditing clinical documents.
NLP in the insurance industry allows to analyze unstructured patient clinical notes, create reports, and transcribe the patient’s interactions, through AI. Adopting NLP, AI and machine learning solutions in risk adjustment workflow will help the insurance companies to get rid of manual, paper-based clinical and administrative processes which are repetitive, error-prone, and time-taking too.
Artificial intelligence for risk management in the health insurance space
Medical record retrieval methods don’t leverage substantial electronic connectivity and can lead to costly, inaccurate, and slower results for payers and providers, and medical coders.
In addition, working with multiple vendors, and medical coding companies, using different communication methods may lead to missing unreported diagnosis Hierarchical Condition Category (HCC) and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes.
NLP-powered AI-enabled solutions can help improve availability, timeliness, and cost-effectiveness for medical record retrieval, review, and audit.
AI solutions harness the power of data to help transform care outcomes, reduce efforts, and improve financial performance. It eliminates silos between patients, providers, and payers.
Machine learning (ML) also enhances a retrospective chart review approach!
What is a retrospective chart review model in the healthcare ecosystem?
A retrospective chart review approach, also known as medical record review, is designed to research and predict healthcare outcomes using pre-recorded, patient-centered data.2
What are the latest trends for payers in the health care sector?
1. Modern risk adjustment workflow
Automation is the latest buzzword and it is rapidly being adopted in the healthcare industry. From ensuring a seamless patient data integration through multiple electronic health record (EHR) systems to offering a lesser-click chart review workflow model, payers can adopt multiple risk adjustment solutions from a single dashboard.
2. NLP-Powered Risk Adjustment tool
Solutions that are based on NLP will ensure healthcare payer’s risk satisfies patients at high-risk, missing chronic conditions, and the claim codes that lack proper documentation.
Artificial intelligence gathers and assesses those diagnosis codes by identifying the retrieval modality and predicting disease conditions possibly supported in the chart for the medical coder’s review.
RAAPID, the next-gen personalized and customized risk adjustment solution, uses a decade-long experience, tried and tested NLP solutions that make it easy to identify and validate the correct chronic conditions on the go.
3. Real-time dashboard for medical coders
With medical coders working on multiple patient chart review processes, now AI is helping to expedite the chart review and audit workflow by automating and offering HCC codes and ICD-CM-10 code validation in real-time through software-as-a-service (SaaS)-based and application programming interface (API)-based technologies.
In addition, get immediate access to know the status of the medical coder’s tasks and improve decision-making to boost productivity.
Healthcare insurance companies can stay ahead of the curve by adhering to the above three main trends in 2022 and beyond.
RAAPID.AI, a modern NLP-powered, AI-based risk management solution, will increase automation for repetitive tasks and improve chart review workflow for medical coders to ensure accurate risk adjustment factor score derivation and quality healthcare, as a whole.
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