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The healthcare industry is in constant motion. Regulations change and care models evolve, while competitive pressures and member expectations grow more complex every year. For health plans, this pace of change challenges how quickly they can adapt and serve members without introducing delays or costly workarounds.

At the heart of this challenge are the internal business rules that define how payers manage care, process authorizations, generate alerts, and manage exceptions. When rules and processes fall behind market demands, organizations face various risks: members may encounter gaps in care, and care teams could struggle to provide coordinated care. When business rules aren’t quickly updated to reflect regulatory changes, compliance risks increase, leading to audit issues and potential financial penalties. Additionally, when health plans depend solely on third-party vendors for automated rules management, they may face slower turnaround times and higher costs.

The HealthEdge GuidingCare® Rules Designer helps by enabling business agility for health plans. Powered by the GuidingCare Rules Engine, GuidingCare Rules Designer gives health plans control over how rules are created, tested, and deployed. Instead of waiting for third-party vendors or navigating complex release cycles, technical users can make direct changes. The result is faster adaptation and better support for members and care teams.

Where Advanced Rule Execution Meets Easy, Visual Design

At its core, the GuidingCare Rules Designer is a purpose-built user platform for writing, testing, and deploying rules. While the GuidingCare Rules Engine provides the back-end execution, the Rules Designer makes it accessible through a modern, intuitive environment.

Traditional rule management often forces users into proprietary languages or cumbersome development frameworks. With GuidingCare Rules Designer, the process is visual and interactive. Analysts and developers can build flows, test scenarios, and refine logic with speed and agility. For the first time, health plans have a tool that supports technical users in a collaborative and flexible design space.

Key features of the GuidingCare Rules Designer include:

• Visual Rule Modeling: Users can design rules through a graphical, point-and-click interface to visually model relationships and dependencies. This feature provides a clear understanding of the rule’s function and intent when building out complex logic.
• Built-in Testing Suite: Simulate real-world scenarios and step-through decision paths with advanced debugging tools to ensure rules work as intended before deployment.
• Flexible Rules Deployment Options: Deploy to quality assurance (QA) or pre-production environments on demand, independent of release cycles, allowing teams to implement updates or compliance changes without delay.
• Concurrent Development: Enable multiple users to work within the same rule set simultaneously, improving collaboration and eliminating time consuming handoffs in rule creation.

From Faster Rules Deployment to Better Care and Compliance

GuidingCare customers leverage the Rules Designer to achieve meaningful strategic advantages and meet industry demands, such as:

Enhanced speed and performance: Users can design, test, and deploy new rules in days rather than weeks, achieving faster runtime, execution, and responsiveness for even the most complex workflows.

Organizational autonomy: Payers can reduce reliance on third-party vendors, keep sensitive data in-house, and give technical teams ownership of business logic.

Regulatory agility: Users can update rules quickly to align with evolving requirements from the Centers for Medicare and Medicaid Services (CMS) and National Committee for Quality Assurance (NCQA), strengthening audit readiness and compliance confidence.

Operational efficiency: The solution allows users to automate workflows to reduce manual effort and standardize processes, freeing care teams from the cumbersome administrative tasks that stand in the way of delivering focused, quality care to members.

Care coordination: With integrated alerts, care teams can detect missed preventive or clinical interventions and trigger targeted outreach for members who need it most.

Implementing Health Plan Rules Transformation

For a technical analyst at a health plan, the new workflow with GuidingCare Rules Designer is both intuitive and fast. Instead of submitting requests to an outside vendor and waiting for the next release cycle, the analyst can log into the Rules Designer and build rules directly in the visual flow editor.

Analysts can create logic to generate alerts for missed preventive screenings, set up automated authorizations, or trigger care management workflows based on clinical data. A powerful set of debugging and troubleshooting tools ensure high confidence and quality.

With the GuidingCare Rules Designer, new rules can be live in production within days. This can include custom care team notifications and updated workflows, empowering them to act with confidence—from member outreach and documentation to care coordination. Processes that once required weeks of back-and-forth can now be completed with speed, accuracy, and control.

Built for Today, Ready for Tomorrow

The first release of GuidingCare Rules Designer is already transforming how health plans manage workflows. And just as importantly, it helps lay the foundation for continued innovation.

HealthEdge® is investing in ways to make the tool even more powerful, including expanding visibility into how rules operate, offering more flexible deployment options, and improving user-friendliness for non-technical users. Future enhancements will also enrich rule logic with broader data sources and emerging technologies.

This forward-looking approach reflects the HealthEdge vision of a care management platform that not only keeps pace with change but helps drive it.

Start Adapting Your Business with Confidence

GuidingCare is trusted by health plans covering more than 30 million members. With the GuidingCare Rules Designer, our health plan partners can gain greater control over how care is coordinated, customized, and delivered. The solution is purpose-built to address the complexity of modern healthcare, enabling plans to adapt more quickly and operate more efficiently.

The future of care management depends on flexibility, control and speed. The GuidingCare Rules Designer delivers all three, empowering technical teams to build and deploy workflows with confidence.

Discover how VillageCareMAX enhanced reporting and operations by partnering with GuidingCare. Read the Case Study.

Artificial intelligence (AI) is quickly becoming foundational to the future of healthcare. From automating claims reviews to powering proactive care management, AI has the potential to help health plans operate more efficiently, serve members more effectively, and drive better outcomes at scale. But for all this opportunity, AI also introduces a new level of scrutiny—especially in healthcare—where data sensitivity, regulatory risk, and clinical impact are high.

A 2024 McKinsey survey shows 85% of healthcare leaders have started or are thinking about using Generative AI (GenAI), yet concerns around privacy, bias, and lack of transparency remain key barriers to widespread adoption. For health plans, these concerns are real and valid.

As AI becomes more embedded in payer operations, health plans must ensure that innovation is paired with responsibility. That’s why responsible AI isn’t just a buzzword—it’s a business imperative.

Why Responsible AI Matters in Healthcare

Healthcare is unlike any other industry when it comes to the ethical stakes of automation. AI decisions here don’t just affect margins or marketing. They can influence a member’s access to care, a provider’s reimbursement, or the outcome of a care intervention.

The emphasis on responsible AI acknowledges this reality, encouraging payers and other healthcare organizations to build systems that are:

• Safe and secure
• Transparent and explainable
• Fair and unbiased
• Compliant with evolving regulations

In the 2025 HealthEdge® consumer study, 64% of healthcare consumers said they were open to the use of AI in health insurance. But many also expressed concerns about how their data would be used and how decisions would be made on their behalf. That lack of trust presents a serious challenge for adoption—one that can only be addressed through responsible design and deployment.

“The path to AI adoption starts with trust. That’s why every AI strategy in healthcare must begin with ethics, governance, and transparency,” says Rob Duffy, HealthEdge Chief Technology Officer. Read his full perspective here.

And regulators are watching, too. The NIST AI Risk Management Framework and evolving federal guidance from the Centers for Medicare and Medicaid Services (CMS) and the Federal Trade Commission (FTC) highlight the increasing need for auditable, fair, and ethical AI practices.

Strategies for Implementing Responsible AI in Healthcare

1. Prioritize Data Privacy and Security

Protecting sensitive health information is non-negotiable. Health plans must ensure that AI systems are built on secure architectures, with robust encryption, permission-based access compliant with HIPAA and other regulatory standards.

At HealthEdge, we implement strict privacy controls at every stage of AI development and data handling because responsible innovation starts with secure foundations.

2. Mitigate Algorithmic Bias

Biased algorithms can result in inequitable care, inaccurate risk scoring, or unfair coverage decisions. To mitigate this, health plans should:

• Ensure models are trained on diverse and representative datasets
• Conduct continuous testing and validation
• Include diverse stakeholder input across AI development cycles

HealthEdge’s AI teams embed fairness checks and bias detection into our model review process, ensuring every system we build is safe and equitable for users.

3. Ensure Transparency and Explainability

One of the most common concerns from providers and members alike is, “How did the AI reach this decision?” In healthcare, that question needs a clear and credible answer.

Solutions like HealthEdge Source™ use large language models (LLMs) to explain discrepancies in payment integrity in plain language. This helps users understand “the why” behind administrative and clinical decisions, enhancing confidence in the tools and streamlining the appeals process.

4. Collaborate with Stakeholders

AI systems are only as good as the real-world insights they incorporate. HealthEdge engages clinicians, business users, data scientists, and customers early and often to ensure our AI reflects the complexities of healthcare.

We also partner with leading ethical AI innovators like Codoxo and Gynisus, integrating advanced solutions into our platforms to enhance accuracy, reduce fraud, and maintain ethical standards.

5. Commit to Ongoing Monitoring

Responsible AI is not a one-time exercise—it’s a lifecycle commitment. Models must be retrained, monitored, and governed continuously to avoid drift, degradation, or unintended consequences.

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The HealthEdge Approach: AI with Integrity

As HealthEdge becomes an AI-native enterprise, we’ve made responsible AI use a cornerstone of our strategy. We believe that innovation must serve people: our customers, our employees, and the members they support.

Our approach includes:

• Adhering to the NIST AI Risk Management Framework
• Embedding explainable AI in platforms like HealthEdge Source and GuidingCare®
• Investing in workforce transformation through internal AI education and upskilling
• Protecting consumer trust while driving real-world results like improved accuracy and reduced administrative burden

“We’re not just building powerful tools. We’re building confidence in how those tools are used,” says Andrew Witkowski, Senior Director of Machine Learning, who leads the HealthEdge Workforce Transformation Lab.

Practical Advice for Health Plan Leaders

Responsible AI isn’t just an ethical priority—it should be considered a competitive advantage. Health plans that embrace trust-first innovation will be better positioned to scale automation, personalize member experiences, and improve outcomes.

Here’s how to get started:

• Conduct an AI ethics audit to identify risks and blind spots
• Educate your teams on transparency, bias, and explainability
• Partner with trusted vendors who put responsibility at the core of their AI strategy

Next Steps: Innovation That Earns Trust

AI will power healthcare’s future, but only if it earns the trust of the people it serves. Responsible AI offers a path forward: one that’s ethical, effective, and sustainable.

At HealthEdge, we’re committed to leading by example. Join us as we build smarter systems and a smarter healthcare ecosystem. Explore our AI strategy and solutions today.

By Ethan Zhu + Justin Wolkowicz

From Burden to Breakthrough: Tackling Document Chaos with AI

At HealthEdge, our AI-first approach isn’t about abstract promises. It’s about solving real, persistent pain points across the healthcare ecosystem. One of the most pressing? The manual, error-prone world of document processing. Despite the digital transformation sweeping through the industry, faxed and scanned forms still clog workflows and slow down care.

Our AI Team saw this challenge as an opportunity. What began as a targeted experiment in intelligent document recognition has evolved into a powerful, enterprise-grade Optical Character Recognition (OCR) platform that’s transforming how health plans handle prior authorizations and other document-heavy processes.

The Bottleneck: Why Prior Authorization Forms Are So Painful

In healthcare settings, large volumes of documents are still submitted via scanned or faxed pages in non-uniform formats. This creates a significant workload that necessitates manual, error-prone processes for data conversion into standardized formats. Traditional OCR methods can extract text and numbers from images, but they cannot intelligently understand the meaning of the surrounding context of the interesting data.

Prior authorization forms are a prime example. The volume is substantial: our customers process between 50,000 to 100,000 documents each quarter. Each form requires extracting approximately 50 fields from documents with variable layouts, field names, handwritten sections, and non-standardized formats.

The current manual process is inefficient and error-prone. Staff must open a faxed document and must first identify the correct patient. They manually search for member information, but handwritten names often don’t match system records exactly. When the initial search fails, staff must try alternative search methods like member IDs, requiring multiple passes through the fax document to locate the correct identifier.

Once the member is identified, staff manually build the digital prior authorization form, navigating across 10–12 different workflow screens, copying and pasting information, and transcribing handwriting from the fax into various fields. They must cross-reference member eligibility, verify benefit information, check authorization history, and ensure all data points align correctly. This process is time-intensive and introduces errors that can delay patient care or create compliance issues.

The impact extends beyond efficiency. Approximately 45% of processed fax documents are never entered into any digital system and thus will never be used for authorizing a downstream claim. For patient care, this becomes a significant bottleneck as many major care services must wait for authorization before they can be delivered.

Building a Platform for Real-World Complexity

To address this, the HealthEdge AI Team designed a configurable OCR solution that reduces manual workload while maintaining confidence and auditability of results. Our solution is not restricted to processing prior authorization forms. It can be expanded to process a variety of documents, including provider demographics documents, appeals processing, care management documents, and claims-related forms.

Our approach focused on three objectives:

• Eliminate manual data entry through intelligent document processing
• Ensure healthcare compliance with built-in security and audit trails
• Enable rapid deployment across different document types and use cases

The solution classifies documents into categories for targeted processing. For each category, we use specific strategies that deliver better performance and enable the extraction of different field types. This allows us to adapt the solution to new use cases and customer needs.

Our OCR engine is capable of converting fax information into structured JSON data, reducing manual data entry and improving productivity. The field names of the detected output can be easily matched to whatever data model our users need. It handles diverse document formats, including handwritten notes and multilingual content, which traditional OCR systems cannot process effectively.

Most methods are capable of providing confidence scores and bounding boxes for every field, giving users visibility into processing accuracy. The confidence score helps identify fields that may require human verification, and the bounding boxes let the human quickly verify the origin of the extracted information. All extracted data remains editable, ensuring human oversight for sensitive healthcare information. The system never takes automated actions without user approval.

Proven Results: Measurable Workflow Improvements

The transformation from manual to AI-assisted document processing delivers measurable operational improvements:

• The automated workflow eliminates most manual steps through intelligent member-matching algorithms that pre-populate patient information. Relevant data is extracted and highlighted with confidence scores, allowing staff to create authorizations with minimal manual input.
• What previously required extensive searching, copying, and cross-referencing across multiple screens now happens automatically in the background. Organizations can scale from processing 10 fax files per day to over 100 per person through asynchronous, background processing that can handle large volumes of data.
• Healthcare staff can now focus their expertise on authorization decisions and patient care coordination instead of repetitive data entry tasks. This shift improves both job satisfaction and care quality by allowing clinical staff to spend more time on clinical activities. Staff no longer have to struggle with handwritten text interpretation or member identification challenges that previously consumed significant time.
• Comprehensive audit trails can now be easily captured, tracking every user action and decision. This supports HIPAA compliance while providing transparency for healthcare quality assurance and regulatory requirements. Every processing step is documented and attributable to specific users, creating a complete compliance framework without adding administrative burden.

From Experiment to Essential: What Comes Next

This document processing platform is just one example of how HealthEdge’s AI teams are creating tools that not only work but also scale. Built for real-world complexity, with guardrails for compliance and transparency, it embodies our vision: use AI to augment teams, not replace them.

Looking ahead, our focus is on expanding adoption across customers and product lines, integrating across HealthEdge solutions, and continuing to evolve the platform to handle new document types and emerging use cases.

AI-enabled document processing is becoming a viable solution to long-standing inefficiencies, offering health plans a clearer path toward reducing manual effort and administrative error, while enhancing cost savings.

To learn more about our AI strategy, visit our AI blog series on our website.

At HealthEdge, our AI Team was created with a clear mission: to accelerate the innovation and adoption of AI technologies that deliver real value for both our customers and our internal teams. Like any transformative technology initiative, we faced a pivotal question early on: should AI capabilities be decentralized into product teams to maximize speed and innovation, or centralized to establish standards and eliminate redundancy?

The answer wasn’t one-size-fits-all. We chose a hybrid approach, balancing autonomy and alignment, by defining three distinct roles for our AI Team: Enablement, Platform Development, and End-to-End Solutioning. We also developed a simple decision-making framework to determine when and how our team engages in each role. This structure enables us to scale AI effectively, maintain quality, and quickly leverage cutting-edge AI tooling and methodologies across the organization.

• Enablement: In the Enablement role, the AI Team guides stakeholder teams in applying AI technologies. We might suggest no-code solutions such as Claude Desktop in combination with MCP (Model Context Protocol) tools to automate a simple but time-consuming operational process. For a more AI-ready stakeholder team, we might offer architectural guidance on how to set up a multi-agent system using LangGraph with the appropriate handoffs, evaluations, and guardrails.

At HealthEdge, the AI Team plays the Enablement role by providing chat support and regular “office hours” to users in our Claude Pilot Program. We share best practices and reusable templates for concepts such as prompt engineering and context management. We’ve also partnered with HealthEdge’s Learning & Development team to centralize learning resources and present about AI innovations to the entire organization.

• Platform Development: The AI Team’s core contribution is developing a scalable, robust platform of reusable AI components that provide value across the business. This includes core features of a generative AI system, such as multi-agent architectures, tools, and RAG (retrieval augmented generation), as well as supportive functions like logging, traceability, evaluations, and guardrails. It also includes building out common use cases such as information summarization or Q&A. Individual product teams then configure or combine these components to fit their own needs.

For example, the AI Team built the Claims Summarizer platform as a flexible tool that delivers consistent value across different products. Product teams define their own configurations to achieve uniform results despite varying applications. A claims review analyst can use the Claims Summarizer to quickly assess key claim details before adjudication in our flagship HealthRules Payer product. Similarly, a care manager can leverage the tool to understand a member’s medical history in GuidingCare before determining next steps for care.

• End-to-End Solutioning: Occasionally, it is necessary for the AI Team to build a complete end-to-end solution beyond just providing functional components to product teams. This can be mandated for high-priority, complex use cases where AI expertise is required for successful delivery of value. Complexity may entail highly networked multi-agent architectures leveraging a broad range of tools or sensitive outputs, necessitating robust evaluations and guardrails. End-to-end solutioning is also a good opportunity for the AI Team to showcase what is possible with AI technology while simultaneously building out the platform to allow other teams to follow the pattern.

At HealthEdge, the AI Team took ownership of an automated document extraction workflow for prior authorization. This involved using OCR to extract key data fields from various prior authorization forms and leveraging AI to map them to internal elements. The large variety of form templates and the lack of one-to-one mappings of data fields made this complex use case a good candidate for the AI Team to take on end-to-end. The project also had a high business impact, with the potential savings of automating the processing of hundreds of thousands of prior authorization forms annually. Given that errors could lead to increased operational costs and delays in care, the AI Team’s thoughtful architecture and thorough evaluations were critical to its success.

CARBS: AI Team’s Role Decision Framework

Given the high demand for the AI Team’s expertise across a large number of initiatives, it was necessary to develop a framework for determining which of the three roles the team would play for a given project, conveniently fitting the acronym “CARBS”:

• Complexity: how much AI expertise does the project require?
• AI Readiness: how much AI expertise does the stakeholder team have?
• Risk: how sensitive is the output (due to privacy, regulatory, or clinical concerns)?
• Business Impact: how much value does the project bring to the organization?
• Scalability: how reusable is the solution across the organization?

When the CARBS framework is combined with the AI Team’s various roles, we maintain quality and avoid repeated work while scaling our impact to the organization.

Why This Matters

The AI Team is designed to collaborate with, not replace, our domain experts. Domain teams continue to own their products, define user needs, and validate success criteria. The AI Team acts as a multiplier, providing them with tools and infrastructure that they might not otherwise have time or expertise to build themselves.

The AI Team’s approach ensures we can move fast without sacrificing quality, avoid redundant work, and scale innovation efficiently. Whether we’re enabling teams with the right tools, building reusable AI capabilities, or delivering complex solutions, our focus is always on turning AI’s potential into tangible results for HealthEdge and the people we serve. For more information about HealthEdge and our AI strategy, visit our blog series found here on our website.

For many health plans, it’s easy to get stuck in a costly cycle of claims rework: pay a claim, spot an error months later, hire a recovery vendor, then repeat. This reactive approach uses unnecessary resources, impacts provider relationships, and reduces efficiency.

Breaking this cycle requires advanced technology and an open-book approach to payment integrity—focusing on transparency, collaboration, and proactive problem-solving.

Examining the Costs of Fixing Errors After Payment

Let’s look at a common scenario faced by health plans. A patient suffers a ski injury and receives multiple diagnostic imaging procedures for their foot and leg. The provider submits a claim with each procedure listed separately. The health plan pays the full amount for each line item and quickly moves on.

Months later, during a routine post-payment review, the health plan discovers the system didn’t apply the Multiple Procedure Payment Reduction (MPPR) rule. As a result, there’s an overpayment of $295.75 on that single claim. This “small” mistake adds up when multiplied across thousands of claims.

Now, the health plan must hire a recovery vendor to chase down the overpayment. But these vendors typically recoup only 50-60% of lost funds, and charge a percentage fee based on the recovered amount. That means a significant portion of the overpaid amount is never returned to the health plan.

Claims recovery also contributes to increased administrative burden. Health plan teams must verify the vendor’s findings, notify the providers, negotiate repayment, and reprocess claims. Providers, in turn, must spend additional time adjusting claims and appealing disputed recoveries. These negative experiences can result in provider abrasion, potentially reducing provider willingness to work with your health plan and impacting member satisfaction.

On an individual basis, these cases may seem minor. But on a larger scale, repeated errors drain budgets and operational bandwidth. Taking an open-book approach promotes collaboration with providers, increases transparency in claims processing, and reduces the risk of disputes or overpayments.

Enhance Initial Claims Accuracy with HealthEdge Source™

The HealthEdge Source payment integrity solution transforms claims processing by helping stop inaccuracies at their source. Traditional technologies handle claims step-by-step, starting with pricing and then moving to editing. This handoff between steps is where errors often slip through. HealthEdge Source takes a more integrated approach using parallel processing. This means that all claim rules, policy edits, pricing checks, and reimbursement calculations happen in the same step for enhanced control and accuracy.

Let’s revisit the ski injury scenario. With parallel processing, HealthEdge Source reviews the claim, applies MPPR edits, recalculates payments, and adjusts for secondary procedures in real time. If there’s a discrepancy or a potential overpayment, it’s caught before the claim is finalized. Instead of waiting months to uncover mistakes, the plan and provider receive timely, accurate payment information.

The operational benefits are immediate:

• By stopping overpayments before they happen, health plans avoid losing money to errors and recovery vendor fees.
• Internal teams are freed from managing vendor contracts, auditing claims, and reprocessing payments.
• Enhanced accuracy cuts down on disputed claims while fostering provider trust.
• This approach easily adapts to growing claim volumes and regulatory changes, future-proofing payment operations.

Access Real-Time Claims Editing

Healthcare is getting more complex. Errors will only grow costlier if left unchecked. Stopping them before they start saves money, frees resources, and puts you ahead of regulatory changes.

An open-book strategy encourages cost and pricing transparency to help eliminate unnecessary spending. By adopting an open-book approach to payment integrity with advanced technology like HealthEdge Source, health plans can stop overpayments, improve transparency, and strengthen provider relationships. This allows teams to focus on member satisfaction and growth instead of backtracking. Break the pay-and-chase cycle and make integrity the standard for your team.

“If people change the way they think about payment integrity, it will start to inspire people to work on improving the system.” – Ryan Mooney, HealthEdge Chief Product Officer]

Discover how Platform Access from HealthEdge Source makes it possible for health plans to gain more control of the claim process and streamline edit configurations to save time and reduce hassle. Watch the webinar on-demand: Edit Smart Not Harder with HealthEdge Source™