Less Admin, More Care: How the Care Management Note Summarizer from HealthEdge GuidingCare® Helps Care Managers Reclaim Time

Care managers enter the healthcare field to support patients—not drown in paperwork. Yet for many, that’s the daily reality: hours spent sorting through handwritten notes, summarizing past encounters, and piecing together a member’s story before the next touchpoint. It’s time-consuming, mentally exhausting, and pulls focus from what matters most—delivering meaningful, one-on-one care.

The increasingly heavy documentation burden is one of the leading causes of burnout in care management. According to a recent study, nearly 75% of health workers say documentation impedes patient care. Each member interaction can generate pages of notes—some structured, some freeform, captured through chats, assessments, or phone calls. Without a tool to bring it all together, care managers are left to manually sift through scattered information just to surface key facts and decide what to do next. It’s not just inefficient—it’s unsustainable.

The Care Management Note Summarizer from HealthEdge GuidingCare® aims to change that.

What is the Care Management Note Summarizer?

This generative AI-powered feature, embedded within the GuidingCare platform, transforms how care managers work by summarizing lengthy, complex notes in a matter of seconds. Instead of spending 30 minutes manually reviewing past documentation, care managers can quickly understand what information matters most, freeing up time and mental bandwidth for more personalized, effective care.

The summarizer pulls in everything from historical care manager notes to chat transcripts and distills it into a clear, actionable summary. It’s not just a passive reporting tool; it actively identifies gaps, flags new opportunities, and suggests additional goals or interventions that can be added to a member’s care plan. It even surfaces personal context—like a member mentioning their pet was sick—to allow care managers to personalize interactions, build rapport, and strengthen trust.

This isn’t a one-size-fits-all solution. The Care Management Note Summarizer is purpose-built for the data environments and workflows that exist within GuidingCare. It’s also been specifically trained to understand how clinical information is documented and how that documentation translates into care decisions. That means it delivers more relevant, targeted insights than generic AI tools—insights that align with both compliance needs and care quality goals.

The Hidden Costs of Administrative Work

Without this feature, hidden costs of administrative work can pile up quickly. Care managers must spend excessive time digging through notes, which delays care, strains team capacity, and increases the likelihood of missed information. In the worst cases, members may have to remind their care managers about prior conversations, eroding trust and confidence.

Early users are already seeing a difference. Initial feedback from care managers using the Care Management Note Summarizer points to meaningful time savings and a noticeable improvement in the quality of member interactions. Care managers also report feeling more prepared going into encounters—and less overwhelmed by documentation afterward.

Responsible AI Innovation at HealthEdge®

As with any AI innovation, responsible development is critical. HealthEdge has established a robust internal AI Governance Committee that includes leaders from product, engineering, compliance, and security. This team ensures that every AI use case meets evolving industry standards for ethics, transparency, and fairness. They also actively monitor external frameworks—such as guidance from the NIST AI Risk Management Framework and the latest discussions from the NIST AI Healthcare Council—to align internal practices with leading regulatory and ethical standards in the healthcare space.

This transparency and accountability are key to adoption and ongoing evolution. HealthEdge understands that organizations can’t just tack on an AI solution—they need confidence in how it’s designed, deployed, and maintained. That’s why every implementation is guided by best practices and clear communication with customers.

The current summarization capability is just the first step. HealthEdge is already working on expanded functionality, including digital assistants that can fetch and present information on command, conversational interfaces, and AI-driven automations that draft messages or update care plans directly.

These tools aren’t replacing care managers—they’re amplifying their capacity. By lightening the administrative load, AI gives care managers more time to do what they do best: support, guide, and build lasting relationships with members.

The Care Management Note Summarizer is a step forward in modernizing care management. And it’s only the beginning of what’s possible with AI-powered innovation from HealthEdge.

To explore how HealthEdge is shaping the future of care with responsible, practical AI, visit Artificial Intelligence | HealthEdge.

 

How Health Plans Can Scale Engagement with AI (and Why It Matters) 

Healthcare consumers are raising the bar for what they expect from their health plans. Members want experiences that are personalized, convenient, and responsive. Meeting these expectations across large, diverse member populations is a growing challenge for health plans. That’s where artificial intelligence (AI) can make a meaningful impact.

By using AI to scale member engagement and streamline interactions, health plans have the opportunity to deliver more relevant, proactive, and efficient experiences, without adding administrative burden. Yet according to the 2025 Healthcare Consumer Study from HealthEdge®, only 21% of healthcare consumers say they’ve used—or even know they have access to—AI-powered tools from their health plan.

These survey findings show a big opportunity for health plans. Consumers are ready to embrace AI assistance—but health plans must make it accessible, trustworthy, and demonstrate its value. Below, we explore what the data tells us, the strategies health plans can use to meet rising expectations, and why HealthEdge is uniquely positioned to help with solutions like GuidingCare® and AI-powered workflow tools.

Survey reveals strong interest in AI, despite low user adoption

The 2025 Healthcare Consumer Study highlights a clear disconnect between interest and usage. Only 1 in 5 members have already used an AI tool offered by their health plan—but 64% say they’re open to it.

The members interested in these tools place particular value on AI features like:

  • Chatbots, virtual assistants, and coaches (94%)
  • Personalized health education and resource suggestions (92%)
  • Cost-saving benefit tools and provider recommendations (90%)
  • Tracking health goals and progress (86%)

So why is adoption of AI tools still so low?

The answer lies in how health plans have historically implemented AI. To date, most AI investment has focused on supporting backend operations, such as improving claims processing, ensuring payment accuracy, and detecting fraud. While these applications are important, they’re often invisible to members.

Even when health plans do use member-facing AI-driven tools, they may not provide information on the technologies behind AI or machine learning (ML) tools. As a result, healthcare consumers are largely unaware of how AI is currently being used across the industry and what value it brings—or could bring—to their experience.

To bridge this gap, health plans need to shift more of their AI focus to member-facing tools that directly enhance engagement, education, and care navigation—which impact health plan costs and operational savings.

Consumer skepticism remains a barrier to AI adoption

Despite their openness to using AI, many healthcare consumers still have reservations. The survey uncovered several key concerns:

  • 26% worry about the quality and accuracy of AI-generated health information
  • 20% are concerned about data privacy
  • 20% cite data security as a barrier to using AI tools from their health plan

To overcome this skepticism, healthcare consumers said health plans could earn their trust by increasing transparency on how and when AI tools are being used, as well as providing data privacy certifications and explanations on how personal data is used and protected.

Ultimately, health plans that take a thoughtful, transparent approach can turn AI from a point of hesitation into a driver of member confidence.

Why now is the right time for AI-powered member engagement

There are several industry trends converging to make AI a strategic priority. Consumer expectations have shifted—they want self-service tools, quick answers, and proactive engagement. At the same time, health plans face rising cost pressures and administrative complexity. AI-driven tools can help summarize and surface key member information to help providers prioritize member outreach.

Evolving healthcare regulations aim to encourage payers to streamline workflows, improve data transparency, and enhance proactive care coordination and delivery. AI-powered solutions can help reduce administrative burden while giving payers more effective ways to engage and retain members.

By deploying AI thoughtfully, health plans can meet members’ expectations and operational needs at the same time.

HealthEdge solutions for AI-driven engagement

HealthEdge is leading the way in delivering AI-powered tools that help health plans transform care and services, while keeping member needs at the center.

Enterprise AI strategy for health plans

At HealthEdge, our comprehensive approach to AI focuses on using these tools responsibly, building trust, and layering capabilities incrementally. This ensures health plans don’t treat AI as a bolt-on feature, but as a core capability embedded across care and operations.

GuidingCare leverages AI for care coordination

GuidingCare uses AI to simplify complex care pathways: triaging cases, identifying care gaps, and summarizing key clinical data. This allows care teams to focus on high-impact interactions while AI handles routine administrative details. It drives both efficiency and personalization in member outreach.

Wellframe’s AI-driven member engagement

Wellframe leverages AI to transform member engagement, creating concise, actionable summaries of member data. This empowers care teams to deliver personalized, timely support, focus on high-impact interactions, and drive greater efficiency and improved member outcomes.

Transforming operations with HealthRules® Payer

Within HealthRules Payer, our core administrative processing system (CAPS), AI helps payers streamline and accelerate workflows, reduce administrative costs, and modernize member experiences.

AI-powered workflow in HealthEdge Source™

HealthEdge Source integrates machine learning to improve payment integrity and claims processing. AI-driven analytics detect patterns, highlight high-risk claims, and enable faster, more accurate reviews, improving the overall member experience.

Intelligently merge data with AI-powered Provider Data Management

The HealthEdge® Provider Data Management solution leverages advanced AI that enables payers to develop a single source of truth for provider data. AI-driven data ingestion and matching help ensure accuracy, consistency, and complete data lineage across all health plan operations.

Dig deeper on the benefits of AI-powered tools

The 2025 Healthcare Consumer Survey shows that healthcare consumers are ready for more intuitive, digital-first engagement powered by AI, but they also want reassurance, clarity, and trust.

HealthEdge leads the way, embedding AI into core platforms like HealthRules Payer, HealthEdge Source, HealthEdge Provider Data Management, GuidingCare, and Wellframe to empower health plans to modernize operations and meet rising expectations. By focusing on transparency, accuracy, and member value, health plans can build confidence while scaling their impact.

To explore these insights and more, download the full 2025 Healthcare Consumer Survey report here.

Enterprise AI for Health Plans: A Fireside Chat with HealthEdge® CTO Rob Duffy

AI is changing business operations across industries. See how HealthEdge® is integrating AI to automate workflows & improve business outcomes.

Artificial intelligence (AI) is reshaping how industries operate. It’s not just a transformational opportunity for health plans – it’s an urgent one. Rising costs, labor shortages, increased compliance requirements, and administrative complexity are pushing plans to rethink how work gets done, and AI offers a way forward. We sat down recently with Rob Duffy, Chief Technology Officer at HealthEdge®, to explore what becoming an AI-native enterprise means for health plans and the partners who support them.

Rob shares his vision for reimagining the structure of work with AI, his approach to responsible AI adoption, and why the most significant breakthroughs in healthcare will come not from front-end tools but from transforming the everyday processes that quietly power the system.

Generative AI is currently a catalyst for innovation in many industries. What do you see as its most transformative applications for health plans over the next few years?

Rob Duffy: People often jump straight to front-end applications or member-facing tools when talking about AI. And those are important. But the most transformative potential lies in how we refactor work itself.

Across the healthcare industry, many processes still rely on long, manual sequences— read this, look that up, log into three systems, extract five data points, and re-enter them somewhere else. A single task can require 20 separate steps that take up time and create bottlenecks. Agentic AI  refers to AI systems and models that can act proactively and autonomously to achieve goals. Agentic AI can take a meaningful subset of those steps off your plate.

Imagine not having to check multiple systems to verify one claim or read 30 pages of documentation to find the two sentences that matter. AI can summarize, extract, auto-complete and present the information you need when you need it. The result? If you’re a care manager, you have more time for members. If you’re in operations, you can resolve backlogs faster and more accurately.

Ultimately, this kind of transformation frees up human capacity for higher-order work and drives better digital experiences. But step one is about reducing friction. We need to start by eliminating 40 to 50 percent of the redundant steps people are still performing across the enterprise. From there, the real innovation can begin.

HealthEdge is on a mission to become an AI-native enterprise. Can you give us a high-level overview of what this transformation entails?

Duffy: Think about the cloud migrations we’ve all gone through over the past decade. Those efforts had structure. We created centers of excellence, developed frameworks, categorized workloads using frameworks like the 6 Rs: rehost, refactor, rearchitect, rebuild, retire, and replace. And we used those for every workload to say, “How will we treat this workload in the cloud?”

That kind of framework doesn’t exist yet for AI. What most organizations are doing now is experimenting. They’re handing people tools like ChatGPT or Copilot and saying, “Try it out.” That can be useful in the short term, but it won’t drive systemic transformation.

At HealthEdge, we’re flipping that approach. We treat AI transformation like a major migration effort. We’ve created an Agentic Center of Excellence and developed our own model for identifying, mapping, and migrating work. We then move work through our version of a factory, just as we would with infrastructure. This time, however, the work we’re mapping encompasses human activities rather than technical systems – tasks, decisions, interactions, clicks, and manual reviews are all in the hands of people.

We ask: Can this be eliminated? Can this be automated? Can we augment the human in the loop instead of replacing them outright? Once we evaluate it, we push it into what we’re calling the “agentic factory,” a structured, repeatable way to move work from human execution to AI systems.

But agentic AI isn’t about replacing people – it’s about partnering with them and augmenting how they work. We need to give people a model that helps them see what AI is doing, why it’s helping, and how they’re still in control.

That’s why HealthEdge is being intentional. We’re not just putting technology out there, but building governance, guidance, and adoption models that help teams know how to use it, when it’s appropriate, and how to build trust.

As our transformation progresses, HealthEdge is focused not only on internal enablement but also on establishing a blueprint that others in healthcare can follow. By leading with process discipline and repeatable models, the company is making it easier for health plans to adapt and scale responsibly.

The industry doesn’t need more experimentation. It needs scalable progress. We are focused on achieving real business outcomes, reducing friction in high-effort workflows, and improving the delivery of care. That means embedding AI directly into our core digital healthcare platforms, not layering it on top. When we do that, we can streamline operations, improve the member experience, and drive real value for our customers.

That’s the kind of impact we aim for: not just a smarter tool, but a smarter system. That’s what HealthEdge is building.

Can you tell us about your agentic AI vision for HealthEdge?

Duffy: My vision is simple: stop doing proofs of concept and start doing real work. We’ve reached a point where experimentation alone isn’t enough. If the only AI in your life is a robot vacuum that sweeps the kitchen floor, you’re not tapping into the full potential of agentic systems.

Our goal is to identify the repetitive, low-value tasks no one wants to do and automate them with intelligence, not just with scripts or bots, but with systems that can reason, respond and learn.

For us, this isn’t about one use case or one solution. It’s about building the infrastructure and processes to apply AI across the board, with governance and accountability built in from the start. That’s how we get to scale, and that’s how we deliver real results for health plans, providers, and members.

Healthcare is a notoriously complex and risk-averse industry. How is HealthEdge balancing innovation and accountability to ensure trust, security and fairness in its AI initiatives?

Duffy: Innovation only works when it’s trusted. To that end, we’re taking a measured and structured approach. First, we’ve developed an internal AI adoption policy and enterprise risk governance model. It draws on frameworks like the NIST AI Risk Management Framework and is guided by principles of fairness, transparency, safety and regulatory compliance.

Second, every candidate technology goes through a structured evaluation process. We don’t deploy a tool just because it’s available. We assess its performance, examine its risk profile, and determine whether it aligns with our internal standards and our customers’ values.

Third, we have a governance council to oversee this work and provide input on direction and oversight. Internally, we want to become excellent at managing innovation responsibly so that externally, we can lead with confidence and transparency.

Our goal is not just to innovate, but to do so in a way that earns and maintains trust. If we get that balance right, the rewards for our customers will be enormous.

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For other organizations in healthcare looking to adopt AI, what strategic advice would you offer to help them achieve impactful outcomes while avoiding common pitfalls?

Duffy: Treat AI adoption like a transformation project, not a side experiment. Too often, organizations pursue whiz-bang use cases because they’re flashy or popular. But those don’t always translate into meaningful impact.

I recommend starting with a work inventory. Map what your teams are doing. Look for places where tasks are repetitive, rules-based, or require high cognitive load but low judgment. Then, apply the same rigor you would to a cloud migration. Build a repeatable model. Plan it. Staff it. Execute in waves.

If you do this right, you’ll find that 15 to 20 percent of your people’s work today could be streamlined, automated or augmented. That’s not a hypothetical number. That’s an actual opportunity. It’s not about replacing people. It’s about giving them better tools and freeing them to focus on higher-value work.

What excites you the most about the future of AI in healthcare?

Duffy: There’s a quote I love from Michio Kaku: “When a technology becomes sufficiently advanced, it becomes both everywhere and nowhere.” He used electricity as the example. It’s in everything we do, but we never really notice it. We just expect it to work.

That’s what I want for AI. I want it to become so embedded in our systems, so well-integrated into the experience of delivering and receiving care, that it fades into the background. That’s when you know you’ve really made it.

Imagine a world where care managers don’t have to dig through files to understand a member’s history, claims are processed instantly with no manual review, and members receive proactive outreach because AI knows when they need support. That world is coming, and we’re building toward it now.

Ready to transform your health plan with enterprise AI?

Explore HealthEdge’s Enterprise AI strategy to start your AI transformation today. Contact us to get started.

How Modern Provider Data Management Positions Health Plans for Success

The healthcare industry is at an intersection of technology, artificial intelligence (AI), and operational efficiency. For payers, ensuring provider data integrity is fundamental to scaling business operations and thriving in a competitive environment.

In a recent speaking session at the America’s Health Insurance Plans (AHIP) 2025 event, leaders from HealthEdge® and the Public Employees Health Program (PEHP) discussed the ways modern provider data management solutions can eliminate operational inefficiencies and drive measurable results for other health plans. They also shared unique insights on how PEHP leveraged the HealthEdge Provider Data Management solution to help reduce friction, save costs, and increase automation.

The Hidden Costs of Fragmented Provider Data

Provider data serves as the foundation for nearly every health plan operation, yet many organizations struggle with disparate systems. Gathering data from multiple sources, verifying, and uploading to a single database can create costly inefficiencies.

PEHP’s experience with managing provider information illustrates the magnitude of these challenges can reach: before implementing a comprehensive provider data management solution, the payer managed 65,000 practitioners across multiple disparate systems and contended with significant data quality issues.

“We had 7,000 data issues with our practitioners, 1,500 duplicate locations, and practitioners listed multiple times for each location they practiced at,” said Lance Toms, Operations Management Director at PEHP. “We were exponentially increasing our claims backlog.”

These data quality problems created a cascade of operational issues, requiring five full-time employees dedicated solely to manual data entry and processing. The organization relied on paper applications and multiple fillable PDFs, with manual data entry processes taking up to two weeks to complete. These practices led to payment delays, provider abrasion, and increased administrative costs.

Address Critical Data Management Pain Points with an Integrated Solution

With ongoing advancements in digital technologies, AI, and machine learning, payers have access to a modern, comprehensive approach to provider data management. Traditional systems and processes often lack the continuity and sophistication needed to handle the dynamic nature of provider information, which includes frequent changes in credentialing status, network affiliations, and practice locations.

“Provider data is no longer a back-office data set,” said Parvathy Sashidhar, Senior Director of Product Management at HealthEdge. “It’s front and center when it comes to your AI transformation, technology shifts, or any major initiatives within your organization.”

 

Modern provider data management platforms address these challenges through key capabilities like:

Automated Data Integration and Cleansing

AI-powered provider data management platforms can process both structured and unstructured data formats, utilizing generative AI frameworks to transform disparate data sources into standardized formats. This eliminates the manual processing that previously consumed significant staff resources.

Real-Time Validation and Enrichment

Modern systems can perform over 300 built-in quality checks, including real-time NPPES validations and address standardizations. This proactive approach prevents inaccuracies from impacting downstream systems.

Intelligent Duplicate Prevention

Advanced trust hierarchies and validation logic address duplicate records at their source, eliminating one of the most persistent challenges in provider data management.

Quantifiable Results Drive Operational Excellence

PEHP’s implementation of HealthEdge Provider Data Management delivered measurable improvements across multiple operational areas. The data migration process achieved a 99.96% success rate and was completed in just 3.5 hours, addressing years of accumulated data quality issues and delivering immediate efficiency gains in less than one full business day.

The automation capabilities allowed PEHP to reallocate resources previously dedicated to manual data processing.

“We’ve been able to reduce between two and five FTEs just for data exchange alone and allocate their time differently,” Toms noted. This resource optimization enabled staff to focus on higher-value activities that directly support business objectives.

At the same time, PEHP enhanced claims processing efficiency, achieving a 13% to 15% increase in auto-adjudication rates immediately following implementation. This improvement reduced claims backlogs and accelerated payment processing for providers.

Finally, the solution’s dashboard capabilities provide comprehensive visibility into critical data like provider networks, organizations, and practitioner roles. This enhanced visibility enables data analytics teams to focus on performance analysis rather than data reconciliation, supporting more strategic decision-making.

3 Strategic Advantages of a Modern Provider Data Management Solution

Beyond immediate operational improvements, modern provider data management platforms can position health plans for future growth and innovation. The foundation of accurate, accessible provider data enables advanced analytics, AI-driven insights, and automated workflows that drive competitive advantage.

1. Compliance Support and Risk Management

Regulatory requirements, including the No Surprises Act and various state mandates, demand accurate, up-to-date provider directory information. Automated compliance support ensures timely updates and reduces the risk of penalties associated with inaccurate provider data.

2. Member Experience Enhancement

Accurate provider directories directly impact member satisfaction by ensuring members can locate in-network providers and access care without unexpected costs. This reduces member service calls and improves the overall member experience.

3. Future-Ready Architecture

Cloud-native platforms built on modern architectures support scalability and integration with emerging technologies. This flexibility enables health plans to adapt to changing regulatory requirements and market conditions without significant system overhauls.

Implementation Considerations for Provider Data Management Solutions

As with any new technology adoption, successful provider data management implementation requires careful planning and execution. Key change management considerations include factors like integration capabilities, custom configurations, and the availability of staff adoption training.

An effective provider data management tool should offer both native integration with existing core administrative processing systems (CAPS) and the flexibility to connect with external systems through configurable APIs. This ensures seamless data flow across the entire technology ecosystem.

Business rules and validation processes should also be configurable to accommodate a health plan’s unique organizational requirements. Low-code, no-code frameworks, like the ones HealthEdge Provider Data Management uses, enable rapid customization without extensive technical expertise.

But to get full value from advanced provider data management solutions, staff need to know how to use them. Offering staff training, sharing regular implementation updates, and embracing feedback about process adaptation are critical for success.

Positioning Your Health Plan for Future Success

The healthcare industry continues to evolve, with increasing emphasis on value-based care, population health management, and regulatory compliance. Provider data management serves as a critical foundation for these initiatives, enabling health plans to operate efficiently while delivering superior member and provider experiences.

“We’re really excited about putting this platform in place that allows us to do the things we’ve been trying to do for so many years,” said Toms. “We now have the tools to do so.”

Organizations that invest in modern provider data management capabilities today position themselves for success in an increasingly complex healthcare environment. The combination of operational efficiency, regulatory compliance, and enhanced member satisfaction creates a sustainable competitive advantage that drives long-term organizational success.

Learn more about how the HealthEdge Provider Data Management solution can help your health plan ensure provider data accuracy to stay ahead of the shifting regulatory landscape. Read the data sheet.

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

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? 

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.