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Building an AI-First SDLC: Lessons From Our Claude Pilot Program

Posted on by Rob Duffy

Three months ago, I posed a question to our engineering leadership team: “What if we could use AI tools eliminate the mundane and amplify human creativity in every phase of software development?”

Today, I’m excited to share the results of our Claude pilot program—a 21-day experiment that transformed how five of our core teams approach the software development lifecycle (SDLC). The numbers are impressive, but the real story is about human potential unleashed through intelligent automation.

The Challenge: Reimagining Software Development

At HealthEdge®, we’re no strangers to complex healthcare software challenges. Our teams regularly navigate intricate claims processing systems, regulatory requirements, and mission-critical integrations that directly impact millions of American lives. The traditional SDLC, while proven, often bogs down brilliant engineers in repetitive tasks that consume time better spent on innovation.

We set out to answer a fundamental question: Could we go all-in and create an AI-first SDLC that enhances human creativity rather than replacing it?

The Pilot: 21 Days of Rapid Discovery

We launched our Claude pilot with 53 contributors across five teams, giving them access to Claude for Enterprise with a simple mandate: “Experiment fearlessly, document everything, and share your discoveries.”

The results exceeded our wildest expectations.

Clause Enterprise By the Numbers

  • 49 documented AI enhanced use cases spanning the entire SDLC
  • 680+ hours of estimated time savings in the pilot alone.
  • 53 active contributors across development, QA, architecture, and product teams
  • A single tool eliminated $48,000 in direct business value identified in just hours

But metrics only tell part of the story. The real transformation happened in how our teams began to think about their work.

Powering Success Across Every SDLC Phase

Requirements & Planning

Our teams discovered Claude’s ability to transform scattered ideas into structured requirements. Carl Anderson, Director of Product Management, summarized it perfectly:

“I uploaded all the notes and thoughts I have had on the integration over the last couple of weeks and spent about an hour tweaking the output. Would have normally taken me a week to put together and countless formatting headaches.”

Key achievements:

  • Product requirement document (PRD) generation reduced from 1 week to 1 hour
  • User stories automatically generated from PRDs
  • Requirements analysis and gap identification accelerated dramatically

Architecture & Design

Perhaps most surprisingly, Claude proved exceptional at architectural analysis and design work. Pratishtha Painuly, Software Engineering Manager, led groundbreaking work analyzing our complex SourceUiMetadata database:

“Claude analyzed our database design and provided simplified recommendations, reducing complexity by 90% while maintaining all functional requirements. This level of analysis would typically take weeks of architect time.”

Key achievements:

  • Comprehensive system documentation generated from codebases
  • Migration strategies for Angular to React conversions
  • Database optimization recommendations with implementation roadmaps

Development & Code Generation

The development phase saw some of our most dramatic productivity gains. James Chang, Software Development Manager, pioneered an approach that fundamentally changed how we think about code generation for regulatory updates:

“We went from Visio flowcharts to complete C# implementations with unit tests in minutes. Claude didn’t just generate code—it understood our patterns, our testing standards, and even anticipated edge cases we might have missed.”

Key achievements:

  • Complete edit classes generated from Visio diagrams
  • Unit test suites with edge case coverage automatically created
  • Legacy code analysis and modernization recommendations

Testing & Quality Assurance

Our QA teams found Claude particularly transformative for test creation and analysis. Usha Suryadevara VP of Integration Platforms, achieved remarkable results:

“I converted 490 manual test cases from a TestRail export into 31 user stories across 15 business domains. This type of analysis and conversion would typically take weeks—Claude did it in hours while maintaining accuracy.”

Key achievements:

  • Manual test conversion to automated test stories
  • Test case generation from decision tables
  • Comprehensive test coverage analysis and gap identification

Operations & Maintenance

Even our operational teams found value in Claude’s analytical capabilities, from defect root cause analysis to system optimization recommendations.

Key achievements:

  • Defect pattern analysis and resolution recommendations
  • Performance optimization suggestions for SQL queries
  • System configuration analysis and simplification

The Human Element: What the Numbers Don’t Show

While the productivity metrics are compelling, the most significant impact was cultural. We witnessed:

  • Cross-functional collaboration as teams shared discoveries across disciplines
  • Fearless experimentation as contributors pushed Claude’s boundaries
  • Knowledge sharing that accelerated learning across the organization
  • Creative problem-solving as teams found novel applications for AI assistance

Ryan Mooney, EVP and GM of HealthEdge Source™, captured this beautifully:

“The more we share, the more likely we’re able to learn at the pace of 53 people vs 1. So we’re getting the amplification of tech AND people. Perfection!!”

Five Tactics That Drove Exceptional Engagement

After analyzing our pilot’s success, I’ve identified five key tactics that other leaders can apply to their own AI initiatives:

1. Create Psychological Safety Through “Weaponizing” early experimental prompts

Rather than leaving teams to figure out prompting on their own, we developed systematic approaches to turn experimental prompts into production-ready tools. This gave teams confidence to experiment knowing they could scale successful discoveries.

Implementation tip: Create a “prompt for prompts” system that helps teams transform their successful experiments into repeatable, reliable tools.

2. Gamify Discovery with Tangible Rewards

We introduced weekly contests with real prizes—Claude Code access, Amazon Q Developer licenses, and yes, even jars of spicy pickles from Brooklyn. The key was making rewards both practical and memorable.

Implementation tip: Mix high-value professional tools with quirky, memorable rewards. The combination creates both practical incentive and community humor.

3. Establish Daily Sharing Rituals

Our most successful engagement came from encouraging teams to share discoveries in real-time. We created a culture where every experiment, success, or failure was worth documenting and sharing.

Implementation tip: Make sharing the default behavior, not an exception. Create low-friction ways for teams to document and share their discoveries immediately.

4. Build on Each Other’s Success

We explicitly encouraged teams to build on others’ work. Some of our biggest breakthroughs came when one team’s discovery inspired another team’s innovation.

Implementation tip: Create clear pathways for teams to extend and build upon each other’s work. Make collaboration easier than starting from scratch.

5. Measure What Matters, Celebrate Everything

While we tracked serious metrics like time saved and business value created, we also celebrated every creative use case, unexpected discovery, and moment of breakthrough.

Implementation tip: Develop both quantitative metrics and qualitative celebration practices. Innovation needs both measurement and recognition.

Looking Forward: The AI-First SDLC

Our pilot proved that AI-first development isn’t about replacing human creativity—it’s about amplifying it. We’re now scaling these discoveries across our entire engineering organization with several key initiatives:

Immediate Actions

  • Production tool deployment of our most successful pilot discoveries
  • Training programs to help all teams adopt AI-first practices
  • Infrastructure investments to support organization-wide AI integration

Strategic Investments

  • Custom MCP servers for deep integration with our internal systems
  • AI-powered development environments that understand our codebases and patterns
  • Automated quality gates that leverage AI for comprehensive code review and testing

Cultural Evolution

  • AI-first hiring practices that value AI collaboration skills
  • Updated development standards that assume AI assistance
  • New role definitions that blend traditional skills with AI amplification

The Bottom Line

In 21 days, we didn’t just run a successful pilot—we glimpsed the future of software development. A future where:

  • Engineers focus on creative problem-solving while AI handles routine implementation
  • Quality improves as AI provides comprehensive test coverage and edge case analysis
  • Productivity multiplies as teams accomplish in hours what previously took weeks
  • Innovation accelerates as teams can rapidly prototype and validate ideas

The healthcare technology landscape demands both reliability and innovation. Our Claude pilot proved we don’t have to choose between them.

As we continue scaling these practices across HealthEdge, I’m excited about what our teams will discover next. The journey from experimental pilot to AI-first organization is just beginning.

Want to learn more about how HealthEdge is thoughtfully integrating AI within our existing solutions? Read the data sheet.

How Can AI-Powered DRG Editing Improve Claims Accuracy? 

Posted on by Diana Nguyen

Claims inaccuracy has long been a pain point in the healthcare industry. Processing inefficiencies lead to challenges like payment delays, retroactive changes, damaged member and provider relationships, and even potential regulatory complications for healthcare payers.

Recent advancements in artificial intelligence (AI) and machine learning are driving innovations in claims processing. AI-powered tools can increase precision throughout the claims process, improving efficiency and helping reduce payment errors.

In a recent webinar from HealthEdge Source™, healthcare industry experts shared effective payment integrity strategies based on their experiences, and how AI-powered solutions focused on diagnosis-related group (DRG) editing can help bring health plans into a more digitally advanced—and compliant—future.

The Scope of Claims Accuracy Challenges

Accurate claims processing is foundational to healthcare payments, yet industry statistics around inaccuracies are cause for concern. Historically, it’s been estimated that 3% to 7% of claims contain errors, though some health plans report inaccuracies exceeding 10% (Figure 1). The process is becoming harder to manage as claims grow more complex due to new care methods, constantly changing regulations, and fragmented systems.

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Figure 1

 

For health plans, claims processing errors create a domino effect of issues. Providers have to deal with payment disputes and time-consuming corrections. Manual processes and disconnected systems slow workflows down, adding unnecessary costs. Regulatory risks from poor claims accuracy can hurt quality scores and even impact funding. And ultimately, members lose trust when payment delays and mistakes disrupt their care.

This isn’t just an internal problem for health plans—it’s one that affects the entire healthcare ecosystem. The study featured in the webinar showed that more than 91% of health plan leaders list addressing claims inaccuracies with AI as a top priority, with many wanting to act within the next year (Figure 2).

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Figure 2

How AI is Reshaping Claims Accuracy

Artificial intelligence is becoming an essential tool in tackling claims processing challenges. By automating error detection and simplifying workflows, AI-powered tools can help health plans manage complex claims more efficiently and without manual intervention.

Traditional methods of claims processing often rely on disconnected, reactive processes. AI, on the other hand, integrates into existing systems to improve decision-making as it happens. Here’s how AI makes a difference in claims management:

  • Automating claims adjudication: By taking over repetitive tasks, AI eliminates manual errors and speeds up the claims process. This allows claims to move faster through the system and frees staff to focus on more complex or high-priority work.
  • Validating claims against clinical standards: AI checks codes for accuracy and alignment with customizable guidelines, minimizing mistakes and making sure payments match the care provided.
  • Cutting post-payment recovery time: Errors caught early in the claims process means fewer corrections are needed down the line. AI can identify issues at the start, helping avoid the costly and time-consuming “pay and chase” cycle.

 

 

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Advance DRG Editing with AI-Powered Tools

One particularly compelling application of AI is its use in DRG editing, a process critical to ensuring accurate payments for inpatient care claims.

Diagnosis-related groups define payment amounts for inpatient hospital services based on diagnoses, procedures, and related factors. Errors in coding or sequencing can lead to significant overpayments, making DRG accuracy a top priority for many health plans.

An AI-driven DRG solution can address common challenges by offering a sophisticated approach to analyzing claims and improving coding accuracy. The HealthEdge Source DRG Guide, powered by Gynisus, already produces significant results, helping health plans detect errors earlier, reduce inaccuracies, and improve payment processes.

Key applications of AI in claims editing

The DRG Guide incorporates advanced policy analytics to tackle errors related to:

  • Code exclusions: Identifying conflicting diagnosis codes that cannot logically apply to the same claim.
  • Sequencing errors: Ensuring that primary and secondary diagnosis codes are correctly ordered based on clinical logic and regulatory guidelines.

By performing automated claims reviews at the beginning of the process, the solution helps prevent costly errors from cascading through the adjudication process. This adaptability is critical to ensure efficient and accurate claims management.

For existing HealthEdge customers, using the AI-powered DRG Guide is straightforward and hassle-free. It works just like any new feature you’d find in a release note. There’s no complicated setup or extra technical work involved. Once enabled, the solution seamlessly integrates into your current system, letting you start resolving errors and improving accuracy right away.

Success Metrics and Measurable Impact

Pilot programs using the DRG Guide have shown significant results in improving claims accuracy and reducing costs for health plans by focusing on DRG editing and coding compliance.

Key Insights from the AI-Powered DRG Guide Pilot Programs 

A recent review of 100,000 healthcare claims (from a total of 200 million) provided valuable insights across both inpatient and outpatient cases. Of the claims analyzed, we discovered 19% required adjustments. We also identified $18 million in potential savings within this small sample alone, demonstrating the system’s ability to scale effectively for large volumes of claims.

Real Claims, Real Impact  

HealthEdge Source DRG Guide helped payers identify claims errors before they escalated into bigger (and more costly) issues.

For one health plan, a claim failed CMS guidelines due to coding violations, which traditionally would have resulted in denied reimbursement and additional administrative work. The DRG Guide flagged the errors, corrected them, and aligned them with regulatory guidance. This saved the health plan $22,580 while preventing costly rework.

A second health plan submitted a claim that had diagnosis codes sequenced incorrectly, leading to potential reimbursement errors. The DRG Guide automatically reordered the codes to align with ICD-10 guidelines, eliminating inaccuracies and the need for manual corrections later in the process.

These early results show what’s possible when claims are reviewed early and accurately. Digital solutions like the DRG Guide are helping health plans catch errors before they turn into bigger problems, reducing redundant work and lowering administrative costs. Whether it’s fixing a single claim or scaling up to review higher claims volumes, the process is proving efficient and practical with the DRG Guide.

What’s Next for AI in Claims Accuracy? 

Claims accuracy is a vital factor in developing equitable provider relationships, maintaining regulatory compliance, and securing member satisfaction. AI is reshaping how the healthcare industry tackles data inaccuracies, bringing much-needed precision and efficiency to payment processes.

The early results from AI-powered DRG editing prove its potential to drive significant improvements for health plans. Beyond the cost savings and operational ease, these solutions offer a roadmap for how the industry can transition from reactive to proactive approaches in addressing claims accuracy.

Our current focus on DRG editing is just the beginning. Through the HealthEdge partnership with Gynisus, we’re expanding the scope of our solutions to take on more challenges in claims processing. Looking ahead, we’re focusing on areas like medical necessity evaluations, AI for fraud detection, and streamlining the entire claims process. Our goal is to build a smarter, more efficient digital ecosystem that simplifies workflows and improves accuracy across the board.

AI won’t just make the current system better—it will redefine what “better” means. For health plans seeking to start or expand their AI initiatives, learn more work with AI in payment integrity.

How an Integrated Digital Ecosystem Enables Success for Health Plans 

Posted on by HealthEdge

Health plan executives face increasing demand to deliver frictionless experiences, from streamlined operations to a modern member experience, to stay competitive in a rapidly evolving market. At the core of health plan operations is the core administrative processing system (CAPS). To truly elevate performance, health plans need a next-generation CAPS solution that can support an integrated digital ecosystem.

While these integrations might feel like a technical headache, their importance cannot be overstated. Real-time, seamless connections between systems ensure businesses can operate efficiently and remain agile. When designed strategically, a health plan’s integrated ecosystem can provide significant operational, financial, and experiential benefits. Here’s how integrations within a health plan ecosystem align with business goals and drive success.

Why Integration Matters in a Health Plan’s Ecosystem

A best-in-class health plan ecosystem consists of critical solutions such as billing, claims processing, member services, provider networks, and more. If not properly integrated, each component operates in a silo, causing inefficiencies, data inaccuracies, and cumbersome workflows.

Integrating these systems offers competitive advantages, such as:

  • Real-Time Data Sharing: Critical data, such as claims information or member profiles, flows seamlessly across digital solutions, reducing errors and bottlenecks.
  • Improved Member and Provider Experience: Integration streamlines processes to provide faster responses, improved transparency, and personalized interactions.
  • Operational Efficiency: Automated workflows replace manual processes, reducing administrative burdens and overall operational costs.
  • Agility for Growth: Integrated ecosystems adapt easily to industry changes, such as evolving compliance regulations, value-based care models, or new product launches.
  • Better Decision-Making: Comprehensive, integrated data equips health plan leaders with actionable insights to make informed decisions.

A health plan ecosystem truly thrives when its core system and supporting components communicate seamlessly, creating a foundation for innovation and success.

The Role of Integration in Core Administrative Systems

At the center of health plans lies modern core platforms like HealthRules® Payer, which are purposefully built to integrate seamlessly with other systems. Here’s why integration with HealthRules Payer is paramount to achieving holistic ecosystem functionality:

Seamless Real-Time Connectivity

The HealthRules Payer Solution Suite leverages an enterprise-class integration layer that enables real-time and batch access to data. Stakeholders—including members, providers, brokers, and pharmacies—can interact with reliable, up-to-the-minute information for better service and collaboration.

Simplified Data Exchange with Industry Standards

Gone are the days of dealing with complicated APIs or legacy database systems. Standardized APIs and interoperability tools ensure HealthRules Payer integrates smoothly with partner systems and external exchanges while meeting compliance requirements like HIPAA.

Affordable Maintenance

One challenge of integration is the long-term cost, particularly when updates roll out. HealthEdge combats this with its Upgrade Assurance Program, ensuring that custom integrations remain functional after each update or release at no extra charge. This minimizes downtime and preserves investment longevity.

Streamlined Ecosystem with Proven Touchpoints

HealthEdge’s HealthRules Payer already supports nearly 100 integration touchpoints, from financial systems to provider data management platforms. By offering pre-built connections to common third-party solutions, health plans enjoy faster implementation, reduced costs, and a more robust ecosystem.

Integration in Action Across Key Functions

To better understand how system integration supports health plan goals, here are examples of its impact across critical business areas:

1. Customer Service 

Integrated customer service platforms ensure that a member’s health history, claims data, and benefits information are easily accessible, empowering representatives to resolve issues quickly and efficiently. For example, virtual assistants powered by AI can retrieve and provide this information in real time, cutting call times and improving first-call resolution rates.

2. Human Resources (HR)

From talent acquisition to employee benefits management, integration allows HR systems to connect seamlessly with payroll platforms and benefit providers. This creates a more streamlined process for managing internal administrative needs, keeping employees satisfied and productive.

3. Claims Processing and Adjudication 

Legacy claims systems often create bottlenecks, but integration reduces these inefficiencies. Claims data integrates directly with pricing, provider networks, and compliance systems, enabling faster adjudication for cleaner claims and higher auto-adjudication rates. HealthEdge clients, for instance, achieve first-pass auto-adjudication rates of over 90%.

4. Provider Collaboration and Network Management 

Real-time provider API support ensures accurate payment processing and data-driven contract negotiations. More seamless communication between health plans and providers streamlines payments, strengthens relationships, and fosters trust. HealthEdge® Provider Data Management enables health plans to automate data ingestion and matching across multiple sources, streamlining validation processes and improving compliance.

5. Data Analysis and Reporting 

Integrated systems empower health plans to unify siloed data for comprehensive reporting. Leaders gain actionable insights into member behavior, claim trends, cost efficiencies, and more through analytics dashboards supported by platforms like HealthRules Answers. This data fosters informed decision-making and enables better forecasting.

Building Future-Forward Solutions with HealthEdge

HealthEdge takes an innovative, modern approach to integration with its HealthRules Connector. Built on advanced architecture, this solution goes beyond enabling compatibility. It establishes a foundation for continual improvement by enabling health plans to adopt emerging technologies and industry best practices, whether these involve AI tools for claims adjudication or consumer engagement systems.

By reducing the costs and complexity of ecosystem management, HealthEdge not only helps health plans succeed today but also future-proofs their technology stack.

Harness the Power of Integration to Meet Your Goals

Integrating systems within your health plan ecosystem is no longer optional—it’s essential to stay competitive, reduce costs, and improve member satisfaction. The ability to exchange comprehensive, real-time data while maintaining flexibility significantly enhances operational efficiency and enables your team to achieve and exceed critical business goals.

When selecting a partner for system integration, choose one that prioritizes both innovation and reliability. HealthEdge’s HealthRules Payer ecosystem offers a modern, fully integrated platform designed to surpass industry challenges while making your health plan more agile, efficient, and impactful.

Let’s build a smarter, more connected healthcare ecosystem together. To learn more about how the integrated HealthRules Payer solution can provide better data access across your organization and help your teams prepare for the future of healthcare. Read the case study, How One Regional Health Plan Created a Member-Centric Digital Ecosystem.

ICHRA: The Consumer-Driven Shift Health Plans Can’t Ignore

Posted on by Rachel Krasnow

Healthcare consumers are increasingly demanding choice and control, and health plans are under growing pressure to adapt. In the 2025 HealthEdge® Healthcare Consumer Study, 60% of individuals with employer-sponsored coverage reported that they would likely participate in an Individual Coverage Health Reimbursement Arrangement (ICHRA) if offered.  

This reflects a broader shift toward consumer-directed benefits and a clear expectation for flexibility. 

What is ICHRA and Why Is It Important to Health Plans? 

An ICHRA allows employers to reimburse employees for the cost of individual market coverage, offering tax advantages for both parties. Employees can shop for plans that best meet their personal needs, while employers gain cost predictability and the ability to tailor contributions by employee class or geography. 

Since being introduced in 2020, it’s no surprise that ICHRAs have gained traction with employers seeking alternatives to traditional group plans, especially as benefit needs become more varied across today’s workforce. 

ICHRA Adoption is Accelerating 

Recent data points to a sharp rise in ICHRA adoption. According to HRA Council’s 2024 report, “Overall adoption of HRAs is up nearly 30 percent over 2023, with an 83 percent increase in large employers choosing ICHRA. Small employers remain the largest cohort, with 84% of new adopters now able to offer health insurance to employees for the first time.”  

In addition, more than 200,000 U.S. employees are currently offered an ICHRA or Qualified Small Employer Health Reimbursement Arrangement (QSEHRA) based on HRA Council member data. The estimated total market size, including all administrators and dependents not captured in the core employee data, is approximately 500,000 individuals covered through an ICHRA or QSEHRA as of 2024. 

In addition, health benefits technology companies are responding with significant investment. In 2024 alone, several ICHRA-focused startups secured major funding, underscoring the growing momentum behind this model. 

The Survey Signal: Healthcare Consumers Want More Choices 

As mentioned above, the HealthEdge 2025 Healthcare Consumer Study found that 60% of respondents with employer-based insurance would likely opt into an ICHRA if offered. With 3 in 5 healthcare members showing interest in this type of option, there is undoubtedly a strong desire for personalization, flexibility, and control over health coverage decisions. 

Healthcare consumers increasingly compare their health plan experience to retail and digital services they use every day, like Amazon or Uber. They expect a seamless, convenient, and tailored experience—regardless of whether they’re enrolled through a group offering or an individual marketplace. 

The Challenge for Traditional Health Plans 

While ICHRAs open new doors for employers and employees, they introduce meaningful disruption for traditional health plans. Among the key challenges: 

  • Fragmented risk pools: Younger, healthier employees may shift to individual plans, leaving a more costly group population 
  • Disrupted enrollment patterns: Members shop across carriers, reducing predictability 
  • Pressure to deliver more flexible, consumer-centric products: Health plans must offer value beyond network access and price 

To remain competitive, health plans must enhance the value they bring across plan types and ensure they can deliver the experience consumers expect—regardless of coverage model. 

How HealthEdge Helps Health Plans Compete in a Choice-Driven Market 

The integrated HealthEdge platform equips health plans with the tools they need to meet today’s healthcare consumer expectations, especially in environments where members are empowered to choose from multiple coverage options, like ICHRA. 

GuidingCare® delivers robust care management and utilization management capabilities that support all members, whether they are part of a traditional group plan or an individual-market plan. It enables care coordination, case management, and personalized engagement across settings, helping plans maintain strong clinical connections even as enrollment models shift. 

Wellframe™ brings a digital front door to every member experience. With mobile-first communication, care navigation tools, and personalized health content, Wellframe keeps members connected to their health plan, even when that plan was selected individually through an ICHRA. It enables health plans to deliver the kind of real-time, user-friendly experience that healthcare consumers now expect. 

Together, HealthEdge solutions give health plans the ability to: 

  • Offer consistent, high-touch support across coverage models 
  • Personalize engagement at scale using data-driven insights 
  • Strengthen member relationships in a competitive, choice-driven environment 
  • Provide value beyond benefits selection—building trust, loyalty, and improved outcomes 

By investing in these tools, health plans can position themselves as partners in care, regardless of how members enroll. 

To better understand how healthcare consumers are thinking about coverage, choice, and the role of their health plans, explore the full findings in the 2025 Healthcare Consumer Study. It offers data-driven insights into member perceptions and priorities that can help health plans navigate a rapidly changing benefits landscape. 

The Opportunity for AI Transformation in Healthcare 

Posted on by HealthEdge

Artificial intelligence (AI) is moving beyond the experimental phase, and its applications are certainly no longer reserved for very large enterprises. In healthcare, applying AI to improve workflows and outcomes has evolved to be a strategic imperative, especially for health plans. From managing claims to supporting care teams and engaging members, AI is reshaping how health plans operate. To unlock the potential of AI, health plans must embrace AI as a core capability that changes how work gets done rather than just a bolt-on tool.

The Imperative for AI in Healthcare

Administrative complexity, workforce constraints, and rising expectations for access and personalization are straining health plan operations. These pressures are quantifiable and increasingly urgent. Nearly 30% of healthcare spending in the U.S. is tied to administrative activities. Meanwhile, staffing shortages and evolving compliance requirements limit health plans’ ability to scale support without significantly raising costs.

Artificial intelligence offers a practical response to this challenge. By targeting redundant tasks, enhancing decision-making, and enabling more precise interventions, AI can deliver operational benefits where traditional methods fall short.

According to a McKinsey analysis, AI solutions could save health insurers between $150 million and $300 million in administrative costs for every $10 billion in revenue. These tools also offer the potential to reduce medical costs and improve profitability by accelerating interventions and promoting consistency in how services are delivered.

The Path to AI Transformation

The integration of AI into healthcare operations is a progressive evolution, not a single event. AI is a foundational technology with many applications and various degrees of complexity. Advancing AI adoption requires a deliberate approach that aligns technology with each organization’s readiness and strategy. Each phase of AI maturity delivers greater value and, as adoption progresses, there are greater demands on data infrastructure, governance, and change management.

As organizations move from basic predictive capabilities to fully orchestrated, intelligent workflows, the level of operational transformation increases—and the potential value grows in tandem.

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As health plans advance AI adoption, more transformational capabilities unlock greater opportunities for impact. Predictive analytics, for example, offers early wins with minimal rework of common processes. More advanced AI capabilities, such as agentic AI assistants or multi-agent orchestration, can significantly reduce administrative burden, enhance coordination, and augment human situational assessments. These innovations allow health plan staff to minimize manual work, focus on higher-value work, and improve outcomes across the enterprise.

The following five areas represent key opportunities where AI can deliver measurable impact across core health plan operations:

1. Predictive Insights

Predictive insights represent an established and widely adopted AI pattern in healthcare. These capabilities enhance visibility into future risks, utilization patterns, and member needs. Traditional machine learning models help health plans synthesize historical and real-time data to forecast trends, identify high-risk individuals, and anticipate operational bottlenecks.

Example use cases: AI can be used for risk and member scoring to prioritize care management efforts and to analyze claims and payment trends to detect outliers and optimize financial performance.

2. Workflow Automation

Workflow automation is an immediate and practical application of AI for health plans. With AI embedded into routine processes, organizations can streamline repetitive tasks, reduce errors, and accelerate operational throughput. These capabilities are especially valuable in areas where data exchange and administrative review slow taking action and increase overhead.

Example use cases: Health plans can embed AI into claims workflows, such as intake, validation, adjudication, and denial management, to streamline repetitive, rule-based processes. This reduces manual effort, improves accuracy, helps ensure compliance, and accelerates end-to-end processing. Additionally, some plans are using Optical Character Recognition (OCR) and Intelligent Document Processing (IDP) to auto-map prior authorization forms from providers and patients into care management workflows to ease administrative overhead and improve throughput.

3. Embedded Generative AI

Generative AI expands user efficiency and decision-making by introducing models that can summarize, synthesize and generate content based on unstructured or semi-structured data inputs. When embedded into health plan workflows, generative AI can reduce staff cognitive burden, support faster documentation, and improve access to vast amounts of critical information during time-sensitive moments such as member interactions.

Example use cases: Generative AI can quickly summarize care notes, complex multi-line claims, clinical histories, and multi-page faxes to help care managers surface essential details and determine next steps without manual review. It can also extract structured data from scanned documents, enabling faster intake and configuration processes across clinical and operational workflows.

4. Copilots and Assistants

AI-powered “copilots” act as intelligent, user-friendly assistants that respond to natural language input, surface relevant information, and guide staff through complex tasks. These copilots offer immediate efficiency gains by reducing the need to switch between systems and lowering cognitive load. They also improve accuracy and responsiveness in member and provider interactions. This functionality reflects applications of agentic AI, in which the system acts with some degree of autonomy to interpret requests, retrieve relevant data, and guide users through workflows.

Example use cases: Plans use AI-powered assistants to enable natural-language member self-service experiences in chat and to support call center representatives with real-time responses to eligibility and benefit inquiries.

Health plans are also deploying chat-based copilots integrated with core workflows to help staff and members interact with plan data, explain adjudication logic in plain language, and surface insights from complex documents like provider contracts. These copilots can extract key details to populate core administrative processing or provider data management systems, easing administrative burden and accelerating resolution times.

5. Multi-Agent Orchestration

Multi-agent orchestration represents an advanced application of AI in health plan operations. Unlike automation that handles discrete tasks, multi-agent systems enable AI to coordinate multiple actions, systems, and decisions without requiring manual triggers. These AI agents complete complex, multi-step, yet clearly defined, tasks across different systems using logic that is pre-programmed or guided by rules and workflows defined by domain experts and engineers. The goal is not just automation but orchestration of the processes that are most complex and expensive in terms of time, energy or accuracy so that the right task is completed by the right system, in the right order, with minimal human intervention.

This approach is especially powerful in areas where workflows span multiple applications or departments, and where delays or handoffs are common. Multi-agent orchestration supports real-time decision-making, enables personalized member journeys, and can help close the loop on tasks that traditionally stall due to complexity or fragmentation.

Example use cases: In claims processing, an orchestrated system of AI agents can automatically gather data from member records, validate information across systems, apply plan-specific rules, and adjudicate the claim. This can occur without requiring manual re-entry or oversight. This reduces cycle time, minimizes errors and improves consistency in outcomes.

The Solutions That Support AI Transformation

To support the delivery of AI capabilities across products and customers, HealthEdge has developed a unified enterprise AI strategy. Our strategy is designed to scale and evolve with customer needs and emerging opportunities across HealthRules® Payer, HealthEdge Source™, HealthEdge® Provider Data Management, GuidingCare®, and Wellframe™.

AI-enabled solutions are generating measurable impact today for HealthEdge customers. For example, summarization capabilities embedded in care management workflows help surface relevant member history in seconds, reducing cognitive load and enabling faster, more informed choices.

Responsible AI Innovation 

HealthEdge is committed to responsible AI development that ensures transparency, security, fairness, and enhanced compliance while proactively mitigating risks associated with new technology adoption. Our approach balances innovation with accountability, helping health plans confidently implement AI-enabled features that enhance efficiency and improve outcomes without compromising trust within their organizations or with their partner or member communities.

To support this, HealthEdge has established a robust framework that ensures AI is deployed ethically, securely, and in alignment with industry best practices:

  • AI Principles: Alignment with emerging healthcare industry AI standards and frameworks, including the Healthcare AI Commitments and the Coalition for Health AI (CHAI™).
  • Responsible AI: A dedicated enterprise risk governance model that addresses regulatory compliance, safety, security, bias, privacy, transparency, and fairness. This includes adherence to the NIST AI Risk Management Framework (AI RMF 1.0).
  • Collaboration & Partnership: Active engagement with customers, end users, and industry stakeholders to co-develop AI-enabled capabilities that reflect evolving real-world needs.
  • Operational Value: A focus on AI innovations that deliver tangible improvements in care delivery, operational efficiency, and the member experience.

Responsible AI requires more than technical capability. Health plans need solutions they can trust. These solutions must deliver value while meeting the highest standards of security, fairness, and transparency. Health plans deserve AI solutions that are not only powerful, but also principled, safe, and trusted.

Transform how your health plan operates with AI  

AI adoption is accelerating across the healthcare landscape. However, realizing measurable impact requires more than experimentation. It takes a clear strategy, scalable technology, and a partner that understands how to embed AI into health plan operations.

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Leveraging Next Generation Provider Data Management Tools to Improve Member Satisfaction 

Posted on by HealthEdge

Accurate and reliable data is the backbone of efficient healthcare operations, facilitating key operations—from claims processing to enhanced member engagement. Yet, for many health plans, managing provider data remains a daunting challenge. Poor data quality leads to inaccuracies, inefficiencies, and regulatory breaches, all of which negatively impact member satisfaction.

Enter HealthEdge® Provider Data Management, a next-generation solution that optimizes provider data accuracy and automation, paving the way for seamless healthcare operations and improved member experiences.

The $3 Billion Problem with Provider Data Management

For many payers, integrating and validating provider data is a complex process that often relies on time-consuming manual processes. Physician practices alone spend $2.7 billion every year maintaining accurate provider directories according to a study from Council for Affordable Quality Healthcare (CAQH). The same CAQH study estimates that streamlining directory maintenance through a single platform could save physician practices at least $1.1 billion annually.

Maintaining an accurate provider data repository is essential for health plans looking to thrive in the competitive healthcare industry. Inaccuracies in provider information trickle down to members in the form of outdated provider directories and other misinformation, causing delays in care access, denials of coverage, and increased friction between health plans, members, and providers.

By combining advanced artificial intelligence (AI) capabilities with real-time data and automation, HealthEdge Provider Data Management offers a purpose-built solution to combat common data challenges and enable health plans to deliver better outcomes and experiences.

The Role of AI in Elevating Healthcare Operations

At the heart of HealthEdge Provider Data Management is advanced AI. By leveraging AI-driven intelligence, the solution enhances provider data workflows in the following ways:

  • Data Enrichment: Automatically enriches provider directories, so members only encounter verified, up-to-date records.
  • Matching and Merging: Eliminates duplicate records by intelligently matching and merging provider data from disparate sources.
  • Prediction and Insights: Provides actionable data insights, helping health plans resolve discrepancies before they create downstream issues.

With AI enabling seamless data management and real-time automation, health plans can deliver the personalized engagement members expect while reducing operational complexity.

How HealthEdge Provider Data Management Enhances Member Satisfaction

HealthEdge Provider Data Management helps payers build a single source of truth for provider information by integrating with existing solutions to streamline data workflows and deliver real-time updates.

Based on our work with health plans so far, we’ve identified 5 key improvements made possible using our advanced provider data management solution:

1. Provide Accurate Provider Directories

Health plan networks are constantly shifting, and providers frequently move to different practices, making it difficult for payers to maintain accurate provider directories. However, member frustration often stems from outdated or inaccurate information listed on provider directories. Incorrect data about provider locations or network status can lead to delayed care, out-of-network visits, and delays in claims processing.

HealthEdge Provider Data Management helps ensure accurate and validated provider information by:

2. Simplify Provider Access

Accessible and accurate provider directories are crucial for members looking for timely, in-network care. The HealthEdge Provider Data Management solution supports members in their search, empowering them to confidently schedule appointments and avoid unnecessary hurdles and unexpected bills. By housing provider data within a unified source of truth, the solution helps ensure members find the right provider on the first try.

3. Accelerate Claims Processing

Claims errors due to inaccurate provider data are costly and time-consuming, impacting both members and health plan operations. HealthEdge Provider Data Management automates workflows to eliminate the causes of manual review at their source, helping health plans:

  • Avoid claims delays and denials caused by duplicate or incomplete provider records.
  • Automate claims-related workflows to enhance payment accuracy.
  • Strengthen payment integrity, especially in compliance with regulations like the No Surprises Act.

4. Automate Data Validation 

Instead of spending hours reconciling inaccurate or duplicate provider records, health plan employees can focus on delivering higher-value member-centric services. Streamlining data integration and validation with HealthEdge Provider Data Management allows health plans to reallocate staff resources toward personalized member support, boosting satisfaction and outcomes.

5. Maintain Regulatory Compliance and Transparency

Members and businesses alike value health plans that remain compliant with evolving healthcare regulations. With a robust infrastructure for managing regulatory data requirements, HealthEdge Provider Data Management helps payers achieve:

  • Provider directory compliance to prevent regulatory penalties.
  • Alignment with transparency-focused mandates, fostering member trust.
  • A proactive approach to adapting to new legislative requirements, avoiding disruptions to member services.

A Case Study in Efficiency and Satisfaction with PEHP

Public Employees Health Program (PEHP) serves as a compelling example of the impact HealthEdge Provider Data Management can have. The health program achieved an initial 99.96% success rate in data migration after implementation, allowing them to update large volumes of provider records in near real-time and achieve faster time to value. This also allowed PEHP to reallocate the work of five full-time employees to higher-value assignments.

As a result, PEHP saw an increase in member satisfaction as well as significant cost savings and enhancing operational efficiency. To learn more, read the full case study.

Why Invest in Next-Generation Provider Data Management?

Advanced Provider Data Management is no longer just an operational upgrade; it’s a strategic necessity. Unified and accurate provider data ensures:

  • Streamlined operations: Reduced administrative overhead thanks to fewer manual interventions.
  • Improved member satisfaction: Better access to accurate and timely care.
  • Regulatory readiness: An automated approach to compliance.

These elements collectively position health plans to thrive in an increasingly complex and competitive healthcare landscape.

Get Started with HealthEdge Provider Data Management

Unlock efficiencies, elevate your member satisfaction scores, and align with regulatory standards using HealthEdge Provider Data Management. Contact us to see how we can partner with your organization.