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Operating within the U.S. healthcare industry can be challenging. Decades of layered regulations, siloed processes, and a sprawling network of stakeholders have created an environment where payment errors and inefficiencies are inevitable. This intricate framework makes true payment integrity a constant battle for health plans, which are often forced into reactive, manual cycles of chasing down errors.

However, this ingrained complexity also presents a clear opportunity for transformation. With rising operating costs, shifting regulations, and growing member expectations, payers are turning to modern, integrated solutions to address these challenges more effectively.

Applying AI to Payment Integrity

For health plans, the biggest opportunity lies in catching errors before they become expensive problems. While most still rely on traditional methods, more than 90% see advanced technology and artificial intelligence (AI) as essential for payment accuracy. The leap in technology will help move the industry from a “pay and chase” model to a more proactive, preventive approach.

AI tools are built for this challenge. Instead of dedicating whole teams to cross-checking contracts, combing through fee schedules, and poring over regulations, AI-powered tools can translate provider contract terms into workflows, check billing codes, and flag inconsistencies. Using AI tools in health plan workflows can reduce processing times and increase payment accuracy—replacing weeks of manual review with speed, accuracy, and control.

Key Considerations before Adopting AI for Claims Processing

Putting AI to work for payment integrity means tackling more than just technology upgrades. Success starts with getting the basics right: clean, unified data and streamlined ingestion processes provide the foundation for reliable results. Modernization doesn’t stop at buying new software. It means consolidating systems, removing manual obstacles, and building a stable, cloud-based backbone. With these elements in place, AI can support the entire claims lifecycle and adapt to changing policies and increasing claim volumes. Without solid data governance and updated systems, even the most advanced AI will fall short.

When it comes to AI, ethical considerations also can’t be an afterthought. Algorithms need to be explainable and fair, especially when they weigh in on high-stakes or gray-area cases. Regular review and transparency keep systems in check. Mitigating bias and protecting data privacy are essential best practices. Solutions like HealthEdge Source™ are built with these safeguards in mind, providing frameworks that help teams avoid ethical missteps.

Strategic Implementation: Recommendations for Health Plans

1. Start small!

A successful approach starts with practical, incremental steps. Start small. Pilot specific use cases. Prove benefits with measurable outcomes, and build user confidence before expanding. Change management also matters. Position AI as a support tool, not a threat or added burden.

Ease of integration is critical. The best AI implementations fit into familiar workflows, remove manual tasks, and let teams focus on higher-value work. If new systems feel tacked on or disruptive, adoption will stall. Thoughtful integration streamlines operations and clears the way for broader benefits.

2. To build or not to build?

Deciding whether to build a solution in-house, buy off-the-shelf technology, or partner for integration really depends on what the organization is aiming to solve, how much control is needed, and the resources available.

Build when the challenge sits at the core of daily operations and demands a solution tailored to unique organizational needs. In these situations, customization, control, and adaptability are essential, especially when the health plan has proprietary intellectual property or data assets not available elsewhere.

For example, HealthEdge Source is building AI-driven enhancements to an existing Retroactive Change Manager tool. The technology will be able to identify and explain their root causes, such as policy changes or specific editing rules. This gives clear, actionable insights so that health plan teams can address errors while still maintaining control over valuable internal data and technology.

Buy or Partner when proven solutions already exist to meet the needs and objectives. Rather than reinventing the tools needed for detecting fraud, waste, and abuse, HealthEdge® partnered with Codoxo. The company’s AI-driven cost containment platform also helps accelerate the deployment of sophisticated analytics and provider education tools, reducing risk and expense while delivering measurable impact.

Integrate when the objective is to enhance an existing platform with advanced, specialized capabilities. HealthEdge Source DRG Guide, powered by Gynisus, exemplifies this approach by integrating complex Diagnosis Related Group (DRG) validation and guidance directly into payment integrity workflows. This integration provides precise, real-time decision support for DRG assignments, reducing costly errors in claims adjudication and improving payment accuracy. Integration lets health plans quickly leverage innovative approaches without the heavy lifting of full rebuilds.

To summarize, build if the need is highly specialized, in-house expertise is strong, and direct control over design is a must. Buy or partner if the problem is industry-wide, and speed to value is important. And integrate to enhance current systems with new tools that offer a performance boost without major disruption.

3. Safeguard operations

As AI extends reach and capability, security and compliance become even more important. Regardless of the model, strict controls are essential. Limit access, encrypt data in storage and in transit, and maintain detailed audit logs. Modern cloud platforms offer built-in security features, but effective governance frameworks are required to scale safely as data sets and automation expand. Strategic oversight ensures that growth in technology doesn’t introduce new vulnerabilities.

10 Key Questions Payers Need to Answer Before Adopting AI for Payment Integrity

1. What specific problem are we trying to solve with AI?
2. Do we have the right people and expertise to manage AI deployment?
3. Will this solution fit in smoothly with our current systems and processes?
4. How will we keep our data secure and meet compliance requirements?
5. What are the short-term and long-term costs of this investment?
6. How will success be measured, and what results do we expect?
7. Is there a clear plan for training staff and ensuring long-term adoption?
8. What risks could AI introduce, and how will we address them?
9. Are there reputable partners or vendors who can support our goals?
10. How will we keep the AI system up to date as needs and technology evolve?

The Future of AI in Payment Integrity

AI is starting to play a bigger role in healthcare—instead of staff having to enter data by hand, intelligent systems can now capture information from conversations, documents, and other communications in real-time. In addition, decision support tools can offer real-time administrative support, catching mistakes early and reducing repetitive administrative work. AI tools can run quietly in the background so staff can spend more time on challenges that need human insight, like handling complex decisions or building relationships with providers.

AI in payment integrity isn’t a flashy add-on. It’s a driver that allows health plan to finally break free from the old patterns of rework, error, and frustrations. Leading organizations are transitioning from fragmented, manual workflows to AI-driven, connected systems designed for accuracy and efficiency. Success will depend on making payment integrity systems smarter and easier to use as the industry evolves.

Is your health plan focused on streamlining claims management and enhancing payment integrity? Read our whitepaper, The Role of AI in Elevating Payment Accuracy.

In our first blog, we explored how HealthEdge’s AI-powered optical character recognition (OCR) platform is transforming prior authorization and other document-heavy workflows. Now, we’re taking you behind the scenes to show how we built it.

Creating an enterprise-grade OCR platform for healthcare requires more than just text extraction. It demands a sophisticated architecture that can handle diverse document types, maintain compliance standards, and scale to process thousands of documents daily. At HealthEdge®, we built our AI Platform’s OCR solution around a modular, three-stage pipeline that balances flexibility with reliability across multiple healthcare workflows.

The first product built on this platform is a solution for processing Prior Authorization forms. You can read more about it at: Transforming Healthcare Document Processing: How HealthEdge’s AI Platform Revolutionized Prior Authorization with Intelligent OCR. While the last article detailed the use case, this article will focus more on the technical architecture.

Multi-Stage Processing Architecture

Our OCR platform implements a three-stage approach: classification, extraction, and resolution. This modular design allows us to optimize each stage independently while maintaining flexibility for different document categories and use cases.

In this section, we will take a closer look at each stage in the multi-stage architecture upon which the OCR platform is built.

Robust Classification

The heart of our system lies in these configurable document categories that serve as processing blueprints. This enables us to define strategies for each document category and run dedicated models. This targeted approach to extraction enables a more accurate and fine-tuned result as opposed to generalized models. Classification also allows different Resolve stages, during which the output data format can be different between document types. That is, this allows fields to be added/omitted depending on the source document type. Fallback mechanisms can also be implemented to handle edge cases when documents can’t be classified with sufficient confidence. Most of this functionality can be quickly reconfigured to new document categories without code changes.

In our configuration for prior authorization forms, the classification layer uses Azure Document Intelligence Custom Classifier Models to intelligently route documents to appropriate processing workflows. The classifier is trained on a small handful of example documents to determine which standard Prior Authorization Form was provided.

We support multiple extraction approaches to handle the varied nature of healthcare documents. Our General Key-Value Extraction uses Azure’s prebuilt layout model with keyValuePairs functionality, where an LLM processes the raw output according to user-defined schemas. For example, the strategy can pull out data like name, phone number, and member ID, but may also capture extraneous data. The LLM is then prompted to filter this set of rough data pairs to conform to a clean user-defined schema.

Flexible Extraction Strategies

This approach requires no training but may extract unnecessary information that needs filtering, as the general extraction model will extract all possible interesting pairs of data from the document that may or may not be relevant, and the LLM can be prompted for 0-shot data filtering to only the specified subset of needed data. For more precise results, our Custom Extraction strategy leverages Azure’s Custom Extraction Model trained on user-labeled documents, where the user manually gives samples of the extraction results. While this requires a minimum of five labeled training documents and training times that can vary from minutes to hours, it provides high accuracy for relevant fields with comprehensive confidence and location metadata.

For simpler use cases, we offer Content Understanding through Azure’s service with custom analyzers trained via schema definitions. This service uses multiple LLMs that are tasked with understanding the document and picking out the user requested data. This service also cross-validates the results across multiple LLMs to ensure confidence and accuracy. This is easy to configure but provides limited location and confidence data for complex fields like tables. Our Markdown Extraction approach converts documents to markdown text and uses LLMs for field extraction. While cost-efficient and flexible, it provides no location or confidence metadata, though we can enhance it with two-stage processing for better accuracy.

Deterministic Resolution

The configuration process involves providing training data with document examples and their expected output. Once generated, this code provides consistent and repeatable results, eliminating the variability inherent in LLM-based approaches. For organizations requiring maximum predictability in their document processing workflows, this deterministic approach offers significant advantages over typical AI-based resolution methods.

Production-Ready Integration Architecture

Our platform adheres to an API-first design philosophy, exposing REST endpoints for each processing stage, including document classification, field extraction, result resolution, and code generation for deterministic mapping. Production deployments typically use automated file watchers that detect new documents in configured source locations, trigger the processing pipeline with proper tenant identification, handle background processing through all three stages, use queue-based messaging for completion notifications, and deliver results to designated output locations.

The platform handles multi-tenancy through tenant isolation in data processing and storage, configuration inheritance with customer-specific overrides, comprehensive audit logging with tenant attribution, and role-based access control. This architecture enables us to serve multiple healthcare organizations from a single platform instance while maintaining strict data isolation and security boundaries.

Performance and Reliability Characteristics

Our background processing architecture enables horizontal scaling without impacting user-facing performance. The platform can process thousands of documents simultaneously while maintaining consistent response times for interactive operations. Each extraction includes confidence scores that enable intelligent fallback strategies, including threshold-based routing for low-confidence extractions, human review queues for validation requirements, automated reprocessing with alternative strategies, and comprehensive logging for debugging and optimization.

Security and compliance are built into the technical architecture. We maintain HIPAA-compliant data handling throughout the processing pipeline, generate comprehensive audit trails for every processing step, ensure no autonomous actions occur without human validation, and implement encrypted data transmission with secure storage protocols. This technical foundation ensures that healthcare organizations can trust the platform with sensitive patient information while meeting regulatory requirements.

Real-World Use Cases and Applications

The platform’s versatility is demonstrated through a diverse range of healthcare applications, currently in production and planned for development. Our primary use case involves prior authorization processing for GuidingCare®, handling fax forms to extract patient information, medication requests, service codes, and diagnosis details from various payer-specific forms. We’re expanding into provider demographics management through existing infrastructure, processing provider update forms with demographic changes and credential modifications.

Beyond these current deployments, the platform’s modular architecture supports appeals processing with complex narrative sections, care management documentation including treatment summaries and discharge planning forms, and claims processing workflows handling both standard forms like CMS-1500 and payer-specific formats. The system’s technical versatility extends to multi-language healthcare forms, handwritten clinical notes, and mixed-format documents that combine structured fields with narrative sections.

The platform excels in scenarios requiring seasonal volume fluctuations, such as open enrollment periods and regulatory reporting deadlines, while enabling rapid new customer onboarding through configurable document types. This flexibility allows healthcare organizations to process everything from utilization management workflows to quality assurance documentation and member enrollment forms using the same underlying technical infrastructure.

This architectural approach demonstrates how thoughtful platform design enables both flexibility and reliability in healthcare document processing. By building modular, configurable systems with multiple processing strategies and robust security measures, we’ve created a foundation that can scale across diverse use cases while maintaining the accuracy and compliance standards essential for patient care. The result is a platform that doesn’t just solve today’s document processing challenges but provides the technical foundation for tomorrow’s healthcare automation needs.

To learn more about HealthEdge’s AI-first strategy, visit the AI blog series on our website.

The One Big Beautiful Bill Act (OBBBA) introduces far-reaching, fast-moving regulatory changes that demand adaptability from health plans. Some provisions are already in effect, while additional rulemaking continues to shift compliance requirements.

State-level differences in Medicaid, Medicare, and Affordable Care Act (ACA) eligibility, as well as new rules for Home and Community-Based Services (HCBS) and Long-Term Services and Supports (LTSS), will create coverage disruptions for members.

At the same time, states can apply to access $50 billion in rural health funding to expand care access and advance digital tools for care management and engagement. The question is no longer whether change is coming, but whether your organization is ready to pivot when it does.

What’s at Stake for Health Plans after the OBBBA

Unprepared health plans face more than administrative disruption. Shortened eligibility cycles and tighter requirements could trigger unprecedented member churn. Teams that lack automation and workflow intelligence will be stretched thin, creating operational strain just as funding is tightening.

Perhaps most critically, members themselves want greater focus on preventive care and wellness as well as seamless digital experiences. Plans that fail to meet those expectations risk losing engagement, trust, and long-term loyalty.

Compliance Readiness is a Moving Target

Being “ready” for OBBBA isn’t a one-and-done milestone—it’s a continuous capability. Regulations and eligibility rules will shift rapidly and differ by state, so health plans need systems that can adapt in real time. Predictive modeling and scenario planning can help plans stay ahead of regulatory changes, while AI-driven automation reduces administrative burden without sacrificing quality.

Equally important is digital engagement. Outreach must be personalized, mobile-friendly, and scalable, particularly for rural and vulnerable populations who will be most affected by these policy shifts.

Why Vendor Partnerships Matter for Health Plans

No health plan can manage this complexity alone. Strategic vendor partnerships are the multiplier that turns readiness into a competitive advantage. HealthEdge, for example, offers an integrated ecosystem that helps plans respond quickly, remain compliant, and retain members through stronger experiences.

Integrated solutions like HealthEdge HealthRules® Payer, HealthEdge Source™, HealthEdge Provider Data Management, HealthEdge GuidingCare®, and HealthEdge Wellframe® empower payers to manage eligibility and claims processing in real time, customize rules and edits, maintain accurate provider data, and deliver scalable digital engagement across member populations. Together, these integrated digital solutions help reduce friction, improve compliance, and allow plans to adapt with AI-powered insights and analysis without disrupting day-to-day operations.

Choosing the Right Partner

The right technology partner should do more than just check boxes. They should act on eligibility data in real time, integrate seamlessly with your existing systems, and use AI to scale operations. A connected ecosystem that reduces IT complexity and consolidates vendors is no longer optional—it’s essential to stay competitive in this volatile regulatory environment.

The New Competitive Advantage

OBBBA is more than a regulatory hurdle. For plans that approach readiness with intent and invest in strong collaborations, it becomes a strategic opportunity to build resilience, retain members, and shape the next era of healthcare delivery. Through these partnerships, health plans can realize measurable results like those HealthEdge achieves with its existing customers:

• Centralized data and workflows to provide seamless reporting for CMS and state regulators, particularly for Medicaid and D-SNP programs.
• Streamline care management processes so staff can double their caseloads 
• Achieve 99.5% claims processing accuracy,  ensuring quality and reducing rework

See how VillageCareMAX partnered with HealthEdge to streamline operations and enhance reporting. Read the case study.

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.