Natural Language Processing In Healthcare Payments

Alaffia Health leverages several artificial intelligence technologies to power its payment-integrity-as-a-service platform. Their use of natural language processing in healthcare payment integrity, along with other advanced technology, enables Alaffia’s high degree of efficiency in reviewing healthcare claims for fraud, waste, and abuse charges.

Artificial intelligence is a cluster of related technologies enabled by advances in computing power and data storage that perform certain tasks previously requiring human intelligence to complete. AI has the power to automate repetitive tasks, analyze and process vast amounts of data, and complete pattern recognition and problem solving tasks. And it’s behind the scenes of almost every aspect of modern life, from Netflix recommendations to Alexa or Siri virtual assistants.

There are two key AI technologies used in Alaffia’s payment integrity platform: machine learning and natural language processing. 

As we discussed in a recent blog post, machine learning is a process in which a computer program teaches itself to accomplish a task. AI implementation specialists accomplish this through designing algorithms and then feeding large amounts of data through the computer. The system will develop an ‘answer key’ based on this data. This makes machine learning a powerful tool for pattern recognition and anomaly detection.  

What Is Natural Language Processing

Typically, computers run based on a code or programming language. They may display their output or input in the language of their user, but under the hood, programming happens in code. 

Natural language processing is a term for computers that understand human language speech and text organically, without need for translation. It sounds simple, but there are many ways natural language processing is already making a big difference in our world. Most people use natural language processing every day, in such uses as:

  • Predictive text and autocorrect
  • Virtual assistants and chatbots (like Alexa and Siri) 
  • Automated translation and transcription
  • Email spam filters
  • Automated phone switchboards
  • Internet search results

There are five key elements to natural language processing. 

  • Optical character recognition (OCR) is how a computer reads handwritten or printed text and converts it to digital text. It can scan unstructured data sets such as handwritten chart notes and convert it to searchable data and standardized formatting.
  • Named entity recognition (NER) is part of the information extraction process. It segments data as it’s ingested, and recognizes and parses names of people, locations, organizations, or other specifics.
  • Sentiment Analysis uses NLP, data analysis, and pattern recognition to determine its tone and sentiment. It’s useful as a scalable method of determining how consumers perceive a company, product, or organization.
  • Text Classification or text categorization analyzes and tags text according to designated criteria for search and ID purposes.
  • Topic Modeling classifies collections of documents and groups them based on common words and phrases for ease of categorization. When combined with sentiment analysis, it can group text or documents based on semantics and tone.

OCR and NER are both key technologies for natural language processing in healthcare. 

Natural Language Processing In Healthcare

The use cases for natural language processing in healthcare are obvious. Healthcare produces vast quantities of data in many different formats, from charts to claims to uniform bills, and much of it is unstructured. The capacity to process, analyze, and provide relevant insights from this data in its written format is a game changer.

AI can scan hundreds of thousands of charts (including unstructured notes) and provide aggregated data for unique insights on public health or developing trends. It can help discover undiagnosed conditions based on analysis of a patient’s history and flag the data for a doctor’s review. It can scan claims and parse documentation to demonstrate inefficiencies or highlight potential fraud, waste, and abuse costs.

NLP is becoming commonplace in clinical, diagnostic, and patient experience use cases. Alaffia Health’s cutting-edge payment integrity platform is making use of this capability for payment integrity as well.

How Alaffia Health Uses Natural Language Processing

Alaffia Health’s cutting edge payment-integrity-as-a-service platform leverages both machine learning and natural language processing to drive efficiency and review more of the high-cost claims that are most likely to contain fraud, waste, and abuse charges. 

While AI is no substitute for human intuition, the human/AI partnership pioneered by Alaffia provides the best of both worlds. Natural language processing allows Alaffia’s platform to ingest and analyze claims at a far higher rate than human analysts can accomplish manually. The platform flags claims identified as likely to contain fraudulent or incorrect charges for attention by Alaffia’s experienced team of payment integrity analysts. It then automatically requests supporting documentation from providers for analysts’ review. 

Experts estimate that up to 80% of medical claims contain at least one fraud, waste, and/or abuse charge. Human analysts can only review so many claims in a given day. By conducting an initial audit and prioritizing claims likely to contain the most recoverable overbilled charges, Alaffia’s system prevents and recovers more overbilled charges than a human team could review without AI.

The Bottom Line

Machine learning and natural language processing are technologies that continuously improve with use as they accumulate a larger dataset to work from, and they require significant expertise and resources to develop. It’s not as simple as plugging up a computer and loading a program. That’s why smart healthcare payer organizations are partnering with proven payment integrity experts who already have the capacity they need.

American healthcare payers lose an estimated $300 billion per year to fraud, waste, and abuse charges. Reducing your vulnerability is the best way your organization can control costs. Alaffia Health’s platform integrates quickly and easily with their partners’ systems. We work on a contingency-based fee structure, so we only get paid for results.

Schedule a call to learn more about how Alaffia’s cutting-edge technology can help your organization control costs today.