According to a recent study by the World Economic Forum, the global healthcare AI market is expected to reach USD 16 Trillion by 2030. This significant growth, driven by increasing adoption of AI technologies, rising healthcare costs, and growing demand for personalized medicine, underscores AI's potential to address some of today's most pressing healthcare challenges for the payers. The potential for the global application of AI in healthcare is vast, offering a beacon of hope and inspiration for the industry.
AI is not just a tool but a transformative force in healthcare. AI is revolutionizing the industry, from drug discovery and development to personalized treatment plans and remote patient monitoring. While the technology adoption across different Payers is still under exploration, understanding how AI is applied in these areas can inspire us to leverage its potential. The efficiency improvement and cost savings from AI implementation should reassure and instill confidence in the audience about the benefits of AI in healthcare.
Use Cases of AI for Payers
As industry veterans who have worked in the insurance sector for a long time, we have noticed some extremely efficient use cases to deliver maximum benefits. Adopting these use cases would help insurance companies struggling to manage operational costs and gain better profitability during these difficult times. Some of these use cases are:
1. Conversational AI
We use conversational AI to develop a bot that efficiently handles inquiries related to benefits, authorizations, and claims while minimizing costs. Traditional approaches often involve proprietary solutions that can be expensive and restrictive while requiring additional resources.We usually recommend a solution that leverages open-source AI models and a Kubernetes architecture. This approach offers several advantages: cost efficiency, flexibility and customization, and community-driven innovation. The bot workflow usually involves speech recognition, natural language understanding and processing, response generation, and Kubernetes deployment.
We have delivered clients with significant cost savings by adopting this open-source approach while building a powerful and customizable conversational AI bot. The bot will effectively handle benefits-related inquiries, authorizations, and claims, improving customer satisfaction and reducing operational costs. Additionally, the bot's deployment on Kubernetes will ensure scalability, reliability, and portability.
2. Grievance and Appeals
The high agent costs of handling grievances and appeals usually challenge the payers. To address this, they sought a solution to streamline the process and reduce the time agents spend on each case. Our team's data-driven approach to tackling this problem was successful. By conducting an exploratory data analysis (EDA), we gained valuable insights into the existing grievance and appeal codes, resolution times, and economic impact.Data exploration, followed by categorization and clustering allows developing data clusters using different data segments based on similarities and differences. This is followed by machine learning models, where supervised models predict the appropriate grievance or appeal code for each case.
This data-driven approach has yielded significant results for clients, including code refinement and time savings that translated directly into cost savings. Improved efficiency and enhanced customer satisfaction led to faster issue resolution, and more accurate classifications improved customer satisfaction.
The power of data-driven insights in optimizing business processes is great. By leveraging EDA and machine learning, we were able to effectively address the client's challenge of high agent costs associated with grievance and appeal handling. The results highlight the potential for significant cost savings and improved efficiency through data-driven decision-making.
3. Agent Assist
Clients needed help managing the time-consuming and error-prone task of manual transcription and summarization for agent calls. This process could have improved efficiency and allowed agents to focus on delivering exceptional customer service.Our team developed an AI-driven solution to address these challenges, leveraging open-source AI models for real-time transcription and summarization. The solution was tailored to the specific needs of the call center environment, ensuring accuracy and efficiency. This comes with real-time transcription and automated summarization. The models are customizable and integrated with multiple use cases. Implementing this AI-powered solution yielded significant benefits, such as improved efficiency, enhanced accuracy, empowered agents, and increased call volume.
The transformative power of AI in revolutionizing call center operations by automating transcription and summarization significantly enhanced efficiency, accuracy, and agent empowerment. This solution provides a valuable tool for businesses seeking to improve customer service and reduce operational costs.
4. Knowledge Management
Each payer has numerous documents that must be recorded, shared, and stored. Navigating and retrieving policy documents and plans scattered across multiple systems was challenging. A traditional search system often proved time-consuming and inefficient, hindering productivity.The AI-driven solution implemented provided a conversational search interface. This solution offered personalized recommendations, a conversational interface, and AI-powered customization. The system adapts to user queries, refining search results and providing more accurate and relevant documents based on customer requirements.
The solution's key features include voice and text input, document summarization, and prompt-based queries. Implementing this AI-powered conversational search system yielded significant benefits, such as improved efficiency, productivity, and user experience.
This implementation demonstrates the transformative power of AI in enhancing information retrieval and access. By implementing a conversational search interface, we addressed the client's challenge of navigating multiple systems and provided a more efficient and user-friendly solution. This approach can be applied to various industries and organizations that rely on large volumes of documents and information.
5. Fraud Detection
Several clients faced the critical challenge of verifying the authenticity of benefits and member documents. Traditional methods to detect sophisticated forgeries and fraudulent approvals were often time-consuming and prone to human errors.
An AI-driven solution, a detailed fraud analysis engine powered by AI, was developed to address this issue. This solution utilized machine learning techniques to analyze various aspects of the documents, identifying potential red flags and anomalies indicative of fraud associated with each submission.
The solution's key features include an ensemble of neural networks that the solution leveraged to combine the strengths of multiple models to improve accuracy and robustness. Multidimensional analysis lets the engine analyze documents from multiple perspectives, including visual features, textual content, and metadata. Anomaly detection in the solution was designed to identify deviations from expected patterns and behaviors, flagging potential fraudulent documents.
Implementing this AI-powered fraud detection engine yielded significant benefits, such as an ensemble of neural networks that provided more accurate and reliable detection of fraudulent documents compared to traditional methods. The automated analysis significantly reduced the time and resources required for manual document verification. By identifying potential fraud early on, the solution helped to prevent financial losses and protect the organization's reputation. The AI-based solution could handle large volumes of documents, ensuring that all documents were thoroughly analyzed.
This case study demonstrates the power of AI in combating document fraud. By leveraging machine learning and advanced analytical techniques, we developed a solution that effectively detects fraudulent documents, reducing risk and protecting organizations from financial losses. This technology can be applied to various industries where document authenticity is critical.
6. Claims Automation
Traditional claims adjudication processes are often time-consuming, labor-intensive, and prone to errors. The need for manual review and decision-making can lead to delays and inconsistencies in claim settlements.
To address these challenges, AI-powered automation can be implemented to streamline the claims adjudication process. By leveraging machine learning algorithms, AI can analyze vast amounts of data, identify patterns, and make informed decisions.
The system ingests various data sources, including claim applications, medical records, and policy information. Data is cleaned, standardized, and prepared for analysis. Relevant features are extracted from the data, such as patient demographics, diagnosis codes, and treatment procedures. Machine learning models are trained on historical claims data to learn patterns and correlations between claim characteristics and outcomes. Whereas for Claim Assessments, AI algorithms analyze incoming claims, comparing them to the learned patterns and making predictions about claim validity, eligibility, and appropriate benefits.
Claims can be automatically adjudicated based on the AI-generated assessment, reducing the need for manual review and approval. AI can process claims faster than manual methods, reducing processing time and improving claimant turnaround times. By automating routine tasks, AI can help organizations reduce operational costs and allocate resources more effectively. AI-powered claims adjudication offers a transformative solution for insurance companies and organizations that handle large claims.
These are some of the most frequently used use cases we have delivered to our clients in the USA. However, with time, these technologies can be utilized by payers globally. With rising inflation costs and a sensitive market, these implementations will help the payers become more profitable over time.
Please feel free to contact us if your insurance company needs help reducing overheads and managing profitability in this volatile market. CirrusLabs has years of domain expertise that will help us customize solutions to scale your insurance business.