The insurance industry is a complex and highly competitive area that requires efficiency, accuracy, and exceptional customer service to thrive. Effective claims processing is crucial for maintaining customer satisfaction and trust, yet it remains one of the most challenging aspects of the insurance business.
The traditional method of evaluating claims, often bogged down by paperwork and manual checks, is rapidly becoming outdated. Some of the common problems that plague the claims practice are:
- Long average cycle times.
- Higher than desired operations costs.
- Claims processing inaccuracies lead to overpayments.
- The manual work of duplicating claims data across systems.
- Slow claims resolutions lead to poor customer satisfaction and retention.
Artificial Intelligence (AI) is at the forefront of this transformation, especially in the realm of claims processing. Claim processing automation offers a transformative solution to these challenges by leveraging technology to streamline and expedite the entire process.
Claim process automation involves implementing advanced technologies to optimize the handling of insurance claims. It streamlines various aspects of claims processing, including data entry, document verification, decision-making, and communication with stakeholders. By leveraging intelligent algorithms, machine learning, and optical character recognition (OCR), claims automation significantly enhances the efficiency and accuracy of claims processing.
What is NLP?
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling machines to understand human language and naturally communicate with humans. NLP 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.
By understanding the structure and meaning of human language, powerful insights can be derived from large volumes of formulated data entry. NLP algorithms analyze language patterns, extract relevant information, and categorize data for efficient claims processing.
Challenges with Unstructured Data
One of the major challenges facing health insurance carriers is the ability to make data-driven decisions on the front lines. There is a tremendous amount of relevant data created during the normal course of business and, as a result, institutional knowledge remains locked up in claims.
Most of this unstructured data contains key information in the form of notes written by doctors, nurses, and examiners. This information is time-consuming to evaluate for the examiners, claim adjudicators and investigators who are responsible for delivering outcomes.
Unlike structured fields, each examiner has a distinct way of capturing notes in the free-form text that is neither standardized nor reportable. A typical claim has dozens of notes that rarely impact the initial structured data captured.
Therefore, it is hard to understand what is happening in claims at scale, because the most vital information is in unstructured data that is difficult to analyze and interpret. More importantly, as the industry moves toward predictive modelling, failure to understand the full medical profile of a claimant can introduce errors into automated workflows.
Healthcare NLP uses specialized engines capable of scrubbing large sets of unstructured data to discover previously missed or improperly coded patient conditions. When combined with optical character recognition (OCR), NLP can understand handwritten text, thus allowing anything from doctor’s notes to be more easily incorporated into a structured format and enabling healthcare providers to manage patient data more accurately while providing superior services for their patients.
NLP in healthcare
Employing natural language processing (NLP) across the industry has already become a necessity rather than an emerging trend. Advanced systems combining NLP with machine learning algorithms unravel the complexities of human language, enabling efficient data processing and making NLP a go-to tool for everyone from overworked clinicians to care and insurance providers as well as ROI-focused medical sales and marketing teams.
The increase in the number of institutions that use NLP in healthcare comes from the global push for digital transformation and data-driven processes, sparking the rise of smart ecosystems. Data is now driving many everyday decisions and actions through various smart devices. The technology capable of analyzing unstructured text and generating workable data is propelling hospitals toward smarter digitalized healthcare, enabling data-driven diagnostics and treatment.
Today, smart devices are linking up to create ecosystems, paving the way for smart homes, neighbourhoods, and cities. In healthcare, this means the emergence of smart hospitals focused on simplifying documentation and administration through tech solutions.
Use Case of NLP in Healthcare
- NLP in Medical Coding: Medical coding is an essential process in the healthcare industry that involves the translation of medical diagnoses, procedures, and treatments into standardized codes. These codes are used for various purposes, including billing, insurance claims, and data analysis. Traditional coding methods require manual review of patient records and extensive manual coding, which can be time-consuming and prone to errors. NLP algorithms can analyze textual data and extract relevant information, reducing the time and effort required for coding. This automation not only accelerates the coding process but also minimizes the risk of coding errors, leading to improved accuracy in healthcare documentation.
- Management of electronic health records: Patients’ health records include all their medical data since their birth and are updated whenever they receive a routine checkup or are diagnosed with a medical condition. But it often happens that many medical records exist only in non-digital form, such as printed or handwritten notes. NLP helps to digitize such records to maintain a complete story of a person’s health. At the same time, it can assist in structuring any updated content added to the EHR: X-rays, CAT scans, lab tests, etc. Advanced ML models can use this data for deep analysis and treatment predictions.
- Enhancing Coding Compliance: Coding compliance is crucial in the healthcare industry to ensure accurate reimbursement and legal compliance. NLP play a vital role in enhancing coding compliance by analyzing clinical narratives and identifying potential coding discrepancies. NLP helps flag documentation gaps, missing diagnoses, or inadequate procedure descriptions, allowing healthcare providers to address these issues proactively. By ensuring coding compliance, NLP contributes to reducing audit risks and financial penalties.
- Overcoming Language Barriers: Language barriers can pose challenges in accurately documenting patient information. NLP technologies can break down language barriers by translating and interpreting medical data in multiple languages. This capability facilitates efficient communication and documentation for healthcare providers, especially in multicultural and multilingual settings. NLP’s language processing capabilities enable accurate coding and streamline healthcare delivery for patients from diverse linguistic backgrounds.
- Fraud Detection: One of the most significant pain points in the insurance industry is the prevalence of fraudulent claims, which can lead to substantial financial losses. With its advanced pattern recognition, NLP can sift through vast amounts of data in record time, pinpointing inconsistencies, or unusual patterns in claim submissions. These red flags are then brought to the attention of human investigators for a deeper dive. When trained on enough historical instances of fraud, NLP algorithms can efficiently identify discrepancies in insurance claims.
Challenges of using NLP in healthcare
Compared to earlier models, today’s NLP models are better trained on more data. Still, when implementing a healthcare NLP solution in patient care, medical institutions and software developers need to consider various limitations:
- Data quality: The language of healthcare is very specialized, full of jargon and unique terminology that cannot be found elsewhere. Additionally, many words have several meanings, which might cause difficulties with text recognition and classification. In addition, ML models need specialized training to be able to work with medical texts.
- Bias: Input data may not represent the diversity of the focus population. Such bias may stem from a variety of factors, from sampling errors to social or demographic phenomena. As a result, NLP solutions may generate equally biased outputs. Such risks can be mitigated by using NLP tools based on explainable AI. These tools rationalize their results, allowing researchers to verify the absence or presence of any partiality in evaluations.
- Legacy healthcare systems: Many medical institutions still use outdated software and documentation management systems that are not compatible with NLP technologies and are incapable of processing large volumes of data.
- Interpretability: Many advanced NLP algorithms provide results that may be difficult to interpret and apply. To achieve workable outputs, NLP models need to be transparent and clear for healthcare providers using them.
Best Practices for Implementing NLP
- 1. Invest in high-quality training data: High-quality NLP models require high-quality training data. Investing in massive quantities of diverse, and well-labeled data can dramatically improve the efficacy of your models. Although the insurance industry has ample amounts of data, it needs to be transformed into a machine-readable format before it can be used with your models. Begin by converting digital data like emails and call transcripts before moving on to more demanding data sources like handwritten documents or audio recordings – these will require competency in optical character recognition (OCR) and automated speech recognition (ASR) respectively.
- 2. Collaborate with domain experts: NLP is a tool in service of the user. As such, it is important to collaborate with domain experts such as underwriters, claims adjusters, and fraud analysts, to ensure that your models are designed to meet their specific needs. These domain experts can use their specialized knowledge to identify domain-specific tasks that would benefit from automation.
- 3. Careful project selection: To give your NLP initiative a greater chance to succeed, try to start with a project that is minimal risk. In other words, you should not start with your hardest problems, as these tend to have a higher chance of failure. For example, fully automating customer support may be too difficult, as that requires AI competencies outside of NLP. Instead, your first project should help build up your basic NLP capabilities and infrastructure. Projects like text classification or document categorization are comparatively simple and can provide a firm base to build.
- 4. Test and evaluate the performance of the models: Investing in AI models also requires an investment in their maintenance. You should regularly test and evaluate your models’ performance to make sure they are performing as expected. Regular evaluations can help you identify issues like model drift – changes in a model’s operational environment, or core variables that cause it to lose accuracy. In cases where model drift is present, you may need to retrain your models. Depending on the type of model, you may choose to continuously monitor its key performance indicators and retrain it when performance dips below a certain level or retrain it at regularly scheduled intervals.
- 5. Ensure compliance with relevant regulations: The insurance industry is heavily regulated, and it is important to make sure that your NLP models are used in compliance with relevant regulations, such as privacy laws and fair lending laws. You will need to give special attention to data protection regulations when training your models with user data and work to eliminate any harmful bias in your training dataset. Taking preventative steps like these can help avert legal issues and protect the interests of your business and customers.
Conclusion
The transformative power of AI in claims processing is undeniable. As it continues to evolve and integrate deeper into the insurance sector, the claims process will become even more streamlined, efficient, and customer-centric. Insurers embracing these AI-driven NLP models are not only optimizing their operations but are also enhancing their value proposition to policyholders.
Although there is still much work to be done before natural language processing in healthcare reaches its full potential, its current applications in healthcare have already demonstrated its immense potential to improve patient care and streamline healthcare systems. NLP in healthcare will be a crucial component of our health systems moving forward.
