The big book of
generative AI use cases
for healthcare payors
Introduction
The healthcare industry is poised to change more in the next decade than it has in the past half-century, and it’s all thanks to generative AI. These powerful technologies aren’t just enhancements — they will fundamentally restructure the way we address inefficiencies in healthcare.
Current challenges in healthcare
Today’s healthcare landscape is a mess. Administrative costs are soaring, resulting in billions of dollars in lost revenue for insurers and providers alike. Inefficiencies create bottlenecks in clinical workflows, delaying care to patients. Despite all the talk about data-driven healthcare, most healthcare processes are stuck in the mid-20th century. Healthcare is the only industry where the application of IT tends to worsen, not improve productivity, to the frustration and demoralization of clinicians and managers alike. Rising costs, labor shortages, and regulatory pressures are squeezing providers and payors, leading to burnout among healthcare professionals and dissatisfaction among patients.
Why generative AI is the solution
Generative AI emerges as an ideal solution to these pervasive issues by automating high-volume tasks and synthesizing complex data. This not only reduces the strain on resources but also enhances the accuracy and speed of healthcare services. For instance, AI can augment the entire lifecycle of member management, from initial contact through ongoing care coordination.
Importance and benefits of AI for healthcare payors
For healthcare payors, the adoption of generative AI is crucial. AI-driven solutions enhance member engagement through regulation-compliant personalized communication and proactive health management, leading to higher satisfaction and retention rates. Generative AI can dramatically reduce operational costs and administrative overhead, allowing payors to focus on improving patient outcomes and optimizing care pathways. Plus, the ability to analyze vast amounts of data in real-time supports informed decision-making and policy development, enhancing the overall efficiency of care.
At the same time, health payors are uniquely sensitive to challenges around data privacy, governance, and change management. The potential benefits are clear, but the path forward is not. For most payors, the question becomes: where and how do we start?
Consider this your launch pad. Our guide is designed to show you the power of generative AI technologies to help you address your toughest challenges — like cutting administrative costs, boosting member engagement, and analyzing complex member data. By detailing practical applications and demonstrating the benefits through real-world examples, “The big book of generative AI use cases for healthcare payors” serves as a valuable resource to leverage AI to improve efficiency, enhance the quality of care, and optimize operational outcomes.
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What is generative AI?
Generative AI uses natural language processing (NLP) and machine learning (ML) to create data or content that looks like it came from a human. What makes it different from other ML technology, like search algorithms, is that it doesn’t just recognize and categorize existing data. It can also create something that’s new and original, often in the form of text that mimics human-generated writing.
It’s this ability to analyze and synthesize data to create original outputs that makes generative AI so powerful and exciting. But integrating generative AI into healthcare payor systems must be approached with careful governance due to potential privacy and security concerns. It’s crucial for healthcare payors to ensure that the data used and generated by AI tools complies with healthcare regulations such as HIPAA.
By being thoughtful and purposeful in their investment in generative AI tools, healthcare payors can enhance their service offerings while safeguarding sensitive patient information, ultimately leading to better health outcomes and optimized resource use.
A quick overview on the generative AI solution landscape for healthcare
When integrating generative AI into healthcare payor organizations, decision-makers can choose from three primary strategies:
- Developing a custom, in-house AI stack
- Using AI point solutions
- Adopt a fully-integrated platform that serves the entire organization
Each approach has its own set of advantages and drawbacks.
1. Developing a custom in-house AI stack
This approach involves building a bespoke AI solution from the ground up or modifying pre-existing foundation models to suit specific organizational needs.
Advantages
Customization: Tailors solutions to fit unique business requirements and workflows, enhancing operational efficiency.
Control over security: Maintains stringent control over data management and model training, ensuring compliance with healthcare regulations.
Drawbacks
Resource intensity: Demands significant investment in time and expertise for development, integration, and application creation. It requires the efforts of dedicated Machine Language engineers, who are always in short supply.
Delayed ROI: The time from development to operational deployment is considerable, potentially delaying benefits realization. These projects are often scoped to take months, and in practice take over a year before the first applications are in production.
Maintenance demands: Requires ongoing investment in system updates and feature enhancements, which is often a challenge for internal payor IT organizations. The Generative AI space moves fast, and solutions that start out as “cutting-edge” feel obsolete in 6 months.
2. Using AI point solutions
This approach involves using standalone “wrapper” applications that are typically built on top of existing large language models (LLMs) — or AI features added to existing software. These apps are designed to perform specific tasks and are great for incremental productivity and personal work.
Advantages
Task optimization: These solutions are highly efficient at addressing specific operational challenges, making them effective for targeted needs.
User-friendly: Generally easier to deploy and require less technical expertise, facilitating broader adoption across the organization.
Quick deployment: Pre-built solutions can be quickly configured and made operational.
Drawbacks
Limited customization: These solutions offer less flexibility to tailor to complex or unique organizational needs compared to in-house systems.
Inconsistent governance: May lead to fragmented standards and practices across the organization, complicating compliance and security management. Payors have strict information security and governance requirements, and onboarding and managing a large number of separate solutions is a major administrative burden.
Vendor dependence: Relies on third-party providers for updates and functionality, which limits control over the solutions.
3. Adopting a full-stack platform
A full-stack platform like Writer provides a comprehensive generative AI solution that includes everything from foundational models to application layers, all integrated into a single solution.
Advantages
Comprehensive governance: Facilitates strict adherence to legal, regulatory, and organizational standards, reducing risk and ensuring compliance.
Industry specialization: Meets the needs of the healthcare industry with an LLM trained on healthcare data, such as Palmyra-Med — the leading model for accuracy on PubMedQA.
Knowledge retrieval: Connects users with organizational data for quick, accurate question-answering through graph-based retrieval-augmented generation (RAG).
Custom workflow integration: Allows for the creation of custom AI applications, enabling tailored AI-driven processes across the organization through app integrations and APIs.
Enhanced security: Centralizes data and processes, reducing exposure across disparate systems and aligning with privacy regulations.
Ongoing support: Offers continuous updates and support, easing the burden on internal IT resources.
Drawbacks
Higher initial costs: Comprehensive solutions may require a larger upfront investment compared to standalone applications.
Choosing the right AI solution for your healthcare payor organization depends on your specific operational needs, available resources, and strategic objectives. Each option presents varying levels of control, customization, and complexity, so it’s important to think about how to integrate AI in a way that aligns with your business goals and healthcare compliance requirements.
Strategic generative AI use cases for healthcare payors
As healthcare continues to evolve, payor organizations are discovering the transformative power of generative AI to enhance member experiences, streamline operations, and elevate customer support. Let’s discuss the key use cases where generative AI can bring the most value to healthcare payors, walking you through real-world applications of this cutting-edge technology.
Enhancing member engagement
Member engagement is essential for fostering better health outcomes and ensuring member satisfaction. Generative AI introduces innovative solutions to personalize interactions and improve the overall member journey:
Tailored member acquisition
Crafting personalized marketing materials that speak directly to the needs and preferences of potential members can dramatically improve acquisition rates across Medicare Advantage, Marketplace plans, and other consumer-oriented market segments. Generative AI empowers member engagement teams to analyze demographic and behavioral data, crafting content that truly resonates with each individual — resulting in more impactful, personal messages.
Example: A campaign for a Medicare Advantage plan might involve generating tailored content that addresses the unique needs and concerns of potential members in a specific region, such as highlighting benefits like affordable premiums and low out-of-pocket costs.
Behavioral science-driven onboarding
The initial onboarding experience can significantly influence a member’s perception and loyalty. Generative AI integrates insights from behavioral science to design onboarding processes that encourage healthy behaviors and enhance member retention, as well as driving the use of digital self-service tools.
Example: During the onboarding process, generative AI can generate personalized communications that guide new members through initial health assessments or program enrollments. By leveraging data on individual member behavior and preferences, these communications can include tailored messages that resonate more deeply with members, encouraging them to complete necessary health actions early in their membership. This not only improves the member’s engagement with their health plan but also sets a foundation for proactive health management, leading to better health outcomes and member satisfaction.
Targeted health utilization content
Motivating members to make informed health decisions requires compelling communication. Generative AI assists in developing targeted content that encourages members to engage with preventive services and manage their conditions, helping to reduce overall costs while improving health outcomes. By building CMS and other compliance rules directly into content generation and review applications, content can be created and approved in days, not weeks.
Example: For members experiencing low back pain, generative AI can be used to generate personalized health promotion campaigns that advocate for high-value, cost-effective treatments like physical therapy over more invasive and expensive options like surgery. With the right AI solution, a member engagement team can quickly craft tailored messages and reminders that highlight the benefits of physical therapy, such as safety, cost savings, and convenience. From there, they can use AI to disseminate through various channels like email or social media, using language and terms that resonate with the targeted audience segment, which in this case includes individuals with low health literacy.
This targeted approach not only helps in managing healthcare costs by reducing unnecessary procedures but also supports members in making choices that can lead to better health outcomes.
Streamlining operations
Operational efficiency is crucial for the success of healthcare payors. Generative AI transforms operations by automating routine tasks and optimizing case management:
Automating routine tasks
AI technology automates a wide array of administrative functions, from member and provider communications to claims processing. This automation frees up valuable resources, allowing staff to focus on more complex service areas that require human interaction.
Example: The Writer generative AI platform can generate personalized letters at scale, considering member characteristics, clinical information, and plan details, which improves letter production time by 40% and reduces labor costs by 50%.
Efficient utilization management
Generative AI enhances the utilization management process by quickly analyzing claims and medical data to ensure that members receive the necessary care according to best-practice guidelines, streamlining operations and safeguarding member health.
Example: Generative AI can automate the end-to-end review and authorization process for high-cost procedures like surgeries and MRIs. The right AI platform can extract and summarize relevant information from electronic health records (EHRs), retrieve applicable rules and policies, and conduct a preliminary assessment to assess medical necessity. For instance, if a member’s provider requests an MRI, the AI system can analyze the patient’s medical history, symptoms, and relevant clinical guidelines to support assessing whether the procedure meets the criteria for approval.
This automation reduces the administrative burden on healthcare staff (cutting administrative costs by 30-40%), decreases turnaround times for decision-making by 50% or more, and ensures compliance with healthcare regulations. Better and faster decision times are essential to both improving member/provider satisfaction, and complying with increasingly stringent government regulations. By improving the efficiency and accuracy of the utilization management process, generative AI helps healthcare providers and payors manage resources more effectively, leading to better patient outcomes and optimized healthcare spending.
Advanced case management
AI supports case managers by equipping them with tools to identify at-risk members and recommend effective interventions, making case management more proactive and targeted.
Example: With generative AI, the process of sifting through and summarizing vast volumes of medical documentation such as electronic health records (EHRs) becomes efficient and empowering, making case management more effective than ever. Generative AI can assist by quickly extracting key information from these documents, summarizing relevant medical history, and highlighting critical data points such as diagnosis, treatment history, and upcoming care needs. This automation significantly reduces preparation time for case managers, allowing them to focus more on direct patient care and less on administrative tasks.
This use of generative AI not only enhances the efficiency of case managers but also improves the accuracy and consistency of the information being processed, leading to better patient outcomes and more effective case management. Effective AI support can decrease case preparation time by up to 60%, and improve member adherence with recommendations by 20% or more.
Improving customer support
In customer support, generative AI significantly enhances the quality and efficiency of the services healthcare payors provide:
Intelligent call center assistance
AI tools support call center agents by offering real-time access to detailed member information and policy specifics, along with intelligent response suggestions, enabling quicker and more accurate resolutions.
Example: When a member calls to inquire about the copay for physical therapy under their health plan, the call center agent can use a custom-built AI chat app in Writer to query a Knowledge Graph set up for the customer support team. The AI system rapidly retrieves and provides the relevant information, such as copay amounts after deductibles and the number of covered visits per year. This not only speeds up response time but also ensures the accuracy of the information provided to the member, enhancing member satisfaction and reducing the likelihood of errors.
This use of generative AI in call centers significantly decreases the average handling time of calls by 10-20%, and increases the Net Promoter Score (NPS) by 20% or more, demonstrating improved efficiency and member experience.
Personalized member and provider communications
Effective communication is critical in healthcare. Generative AI crafts personalized, clear communications for both members and providers, ensuring that essential information is conveyed accurately and efficiently.
Example: Generative AI can create detailed and personalized letters for providers that outline patient care plans, policy changes, or updates on healthcare regulations that impact provider operations. With the right AI platform, teams can integrate data from various sources, including patient records and provider profiles, to generate content that’s not only relevant but also compliant with healthcare standards and regulations.
This use of generative AI helps ensure that communications are accurate and tailored to the recipient, reducing the administrative burden on staff and improving the clarity and relevance of information sent to healthcare providers. This leads to better informed and more engaged providers, ultimately contributing to improved healthcare outcomes for members.
By embracing generative AI, healthcare payors can not only achieve greater operational efficiency and member satisfaction but also lead the way in innovative healthcare delivery. This strategic integration of AI technologies positions payors to effectively meet the challenges of a dynamic healthcare landscape, enhancing care quality and operational agility.
AI in action: three case studies
In healthcare payor organizations, generative AI has been successfully implemented in several key areas to enhance operational efficiency and member engagement. Here are brief examples of these implementations:
Case study 1: Enhancing member communications
Background: A healthcare payor organization faced challenges in managing the volume and customization of member communications, particularly during member onboarding and routine updates.
Challenge: The manual process of creating and sending member communications was time-consuming and prone to delays, impacting member satisfaction and engagement. Ensuring compliance with Medicare Marketing and communications guidelines was a particular bottleneck.
Solution: The organization implemented a generative AI solution to automate the creation of personalized member communications. This AI system utilized member data to generate customized regulatory compliant onboarding materials and routine correspondence efficiently.
Outcome: The adoption of generative AI reduced the content generation cycle time by 75%, enabling the organization to send communications much faster—from weeks to just days. This improvement led to enhanced member satisfaction and increased engagement rates.
Case study 2: Streamlining utilization management
Background: A healthcare payor organization needed to improve the efficiency and accuracy of its utilization management processes to handle prior authorization requests effectively.
Challenge: The existing manual review process was slow and labor-intensive, leading to delays, dissatisfied members and providers, and high administrative costs.
Solution: The organization implemented a generative AI system designed to automate the review and processing of claims and authorization requests. The system compared these requests against established clinical guidelines and payor policies to ensure compliance and accuracy.
Outcome: The generative AI solution led to a more than 50% reduction in turnaround times and significantly decreased administrative costs. The faster and more accurate processing improved compliance with regulations and enhanced the overall efficiency of healthcare delivery.
Case study 3: Optimizing case management
Background: Case managers at a healthcare payor organization were overwhelmed by the administrative tasks associated with managing large volumes of electronic health records (EHRs).
Challenge: The extensive time required to review and summarize EHRs limited the case managers’ ability to focus on direct patient care and interaction.
Solution: The organization deployed generative AI to assist case managers by automatically summarizing EHRs and generating necessary follow-up documentation. This AI-driven approach streamlined the preparation process for case managers.
Outcome: The implementation of generative AI reduced case preparation time by 60%, allowing case managers to dedicate more time to patient care. This shift not only improved the efficiency of case management processes but also enhanced the quality of care provided to members.
Measuring the ROI of generative AI
In healthcare payor organizations, measuring the return on investment (ROI) for generative AI involves assessing both direct financial benefits and indirect enhancements in operational efficiency and member/provider satisfaction. ROI is calculated by comparing the cost savings and revenue enhancements from AI implementations against the investment made in the technology. Reporting on ROI typically involves a combination of quantitative metrics, such as cost reduction percentages and efficiency gains, and qualitative feedback on improvements in member and provider satisfaction.
Let’s examine some specific examples from the different use-case categories we’ve already discussed:
1. Member engagement:
Use case: Personalized member communications
ROI metrics: Reduction in content generation time, increase in member satisfaction scores.
Example: By integrating generative AI into the content creation process for member communications, healthcare payors can achieve a 75% decrease in content generation time and a 5% increase in member acquisition rates. This not only streamlines the communication process but also enhances member engagement, contributing to higher retention and satisfaction.
2. Operational efficiency:
Use case: Utilization management automation
ROI metrics: Decrease in processing times, reduction in administrative costs.
Example: A generative AI solution automated the end-to-end review and authorization process for high-cost procedures, resulting in a 50% decrease in turnaround times and a 30% reduction in administrative costs. This improvement not only enhanced operational efficiency but also ensured compliance with CMS regulations, leading to better healthcare outcomes and reduced overhead.
3. Customer support:
Use case: Call center agent assist
ROI metrics: Improvement in average handle time (AHT), increase in customer satisfaction (NPS).
Example: Implementing generative AI to assist call center agents allowed a healthcare payor to reduce the average handle time by 10% and increase member satisfaction by 20%. The AI system provided agents with rapid access to relevant information, enabling quicker and more accurate responses to member inquiries, thus improving the overall customer service experience.
These examples illustrate the tangible benefits of generative AI in reducing costs, enhancing efficiency, and improving member interactions in healthcare payor organizations. By measuring and reporting on these outcomes, organizations can validate the value of their AI investments and plan further expansions of AI technology to other areas of operation.
Challenges and considerations in adopting generative AI for healthcare payors
Adopting generative AI in healthcare payor organizations involves navigating complex challenges and regulatory considerations to ensure successful implementation.
Addressing potential challenges in adopting AI solutions
- Integration with existing systems: Integrating AI with legacy healthcare IT systems requires significant technical expertise and can incur substantial costs.
- Data privacy and security: AI systems require extensive data access, posing risks to data privacy and security. Healthcare payors must ensure compliance with data protection regulations such as HIPAA and safeguard against data breaches.
- Scalability and maintenance: Maintaining AI performance as solutions scale is complex, necessitating continuous updates and training of AI models to adapt to evolving healthcare practices.
Regulatory and compliance considerations
- Adherence to healthcare regulations: AI solutions must comply with strict healthcare regulations, ensuring that outputs like patient communications and clinical support tools meet safety and efficacy standards.
- Ensuring transparency and explainability: AI systems should provide transparent and auditable decision-making processes to maintain accountability and user trust.
- Managing bias and variability: Measures must be implemented to detect and mitigate biases in AI applications, ensuring equitable treatment outcomes across patient demographics.
By carefully managing these factors, and by partnering with AI technology providers with experience navigating such challenges in enterprise environments, healthcare payors can leverage generative AI to enhance efficiency and patient care outcomes effectively.
Future outlook of generative AI in healthcare
The future of generative AI in healthcare payor systems is poised for significant transformation, driven by technological advancements and evolving industry needs. The long-term vision and emerging trends indicate a trajectory towards more integrated, efficient, and patient-centered care.
Long-term vision for AI in healthcare payor systems
Healthcare payors are looking towards AI solutions that can integrate seamlessly with existing systems, providing a layer of intelligence that enhances operational workflows and patient interactions. The goal is to leverage AI to reduce the cognitive load on healthcare professionals and administrative staff, allowing them to focus more on strategic initiatives and patient care.
The future outlook for generative AI in healthcare payor systems is marked by a shift towards more intelligent, efficient, and patient-focused solutions. As these technologies continue to evolve, they hold the promise of transforming the landscape of healthcare, making it more responsive to the needs of patients and providers alike.
The long-term vision for AI in healthcare payor systems focuses on comprehensive automation and enhanced decision-making capabilities. Generative AI is expected to play a crucial role in synthesizing patient medical records at scale, automating utilization management with high accuracy, and designing personalized treatment plans. This will not only streamline administrative processes but also support clinical decision-making, which will lead to better efficiency and effectiveness of healthcare services. In the coming years, we see most healthcare AI impact being delivered in Level 1 and 2 applications, with Level 3 and 4 applications increasing over time.
Transform healthcare with full-stack generative AI
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