Category: Uncategorized

Why Health Plans Need More Than Capacity to Compete in an Era of Growth, Complexity, and AI

For years, scalability was largely viewed as a technology concern. As long as claims were processing, members were enrolling, and systems stayed online, scalability rarely made its way into executive discussions.

That is changing.

Today, health plans face a convergence of pressures unlike anything the industry has experienced before. Costs continue to rise. Regulatory requirements are becoming more complex. Medicare Advantage enrollment continues to grow. New interoperability mandates require real-time access to information.

At the same time, organizations are expected to modernize operations, improve member and provider experiences, and prepare for an AI-driven future. According to the HealthEdge® 2026 Healthcare Payer Survey, managing costs remains the industry’s top challenge, while modernization, regulatory compliance, and growth pressures continue to climb on executive priority lists.

The challenge is that every one of these priorities depends on a common foundation: the ability of core administrative systems to perform at scale. Scalability is important not only for handling the growing volumes and complexities of claims processing but also during peak periods, such as open enrollment, effective dates, and end-of-year claims.

Growth Typically Means More Complexity

Growth remains a strategic priority for many health plans, whether through expanded enrollment, new lines of business, acquisitions, or geographic expansion. But growth brings operational complexity.

Medicare Advantage illustrates why scalability matters now. More than 35 million beneficiaries are enrolled in Medicare Advantage plans, an increase of approximately 1.1 million members in a single year. Medicare Advantage populations also generate significantly more claims activity than commercial populations, creating greater processing demands across enrollment, claims, prior authorization, billing, and provider interactions.

At the same time, health plans are being asked to support increasingly sophisticated benefit structures, value-based arrangements, interoperability requirements, and digital experiences. As organizations grow, a critical question emerges:

Can the platform at the center of your operations keep up?

A New Definition of Scalability

When many organizations think about scalability, they focus on processing higher transaction volumes.

But for the most innovative health plans, the real question is not whether a platform can handle more claims. It’s whether the platform can support the organization’s long-term strategy.

A scalable platform enables growth by giving health plans the confidence to expand into new markets, support rising enrollment, launch new products, and pursue acquisition opportunities without worrying that legacy operational systems will become a bottleneck. It transforms growth from an operational risk into a strategic opportunity.

Scalability also improves responsiveness. As transaction volumes increase, health plans must continue delivering fast, reliable interactions across claims processing, eligibility verification, prior authorization, enrollment, and provider-facing services. Higher throughput and lower latency help organizations maintain service levels during periods of peak demand while supporting increasingly complex operations.

Reliability matters just as much. Open enrollment periods, year-end claims activity, regulatory deadlines, and seasonal utilization surges all place extraordinary demands on administrative systems. Platforms that can maintain stable performance under concurrent workloads give leaders greater confidence that business objectives can be achieved without sacrificing service quality or operational performance.

There are financial implications as well. According to the HealthEdge payer survey, 27% of health plan leaders identified modernization or consolidation of core administrative systems as a primary strategy for reducing costs and improving efficiency. A scalable, cloud-optimized platform reduces administrative friction, the risk of system downtime, and enables organizations to achieve greater economies of scale as volume grows.

Why the Cloud Changes the Equation

Health plans expectations for core administrative platforms have shifted dramatically in response to modern cloud architecture.

Cloud-based platforms like AWS provide the flexibility, performance, and resiliency needed to support rapid growth and ongoing innovation. They also create the foundation for real-time data exchange, advanced analytics, automation, and AI-enabled operations.

HealthEdge recently demonstrated what a shared AI platform can achieve. In partnership with AWS, enhancements to the HealthRules® Payer platform delivered a fourfold increase in demonstrated scalability while reducing key transaction latencies by more than 50 percent. Performance testing demonstrated the ability to support claims volumes equivalent to a health plan with more than 40 million members on a single instance while maintaining strong performance across critical operational functions.

The significance of these findings extends well beyond benchmark results. It demonstrates how modern cloud architecture can help health plans prepare for future growth without compromising speed, reliability, or responsiveness.

The Connection Between Scalability and AI

According to the most recent HealthEdge payer survey, 94% of health plans are either actively using or adopting AI across administrative or clinical functions. Nearly half report widespread or departmental adoption. AI is rapidly becoming a core strategy for improving efficiency, reducing costs, enhancing decision-making, and modernizing operations.

But AI success depends on more than algorithms.

Organizations need platforms capable of supporting greater data volumes, real-time information exchange, automation workflows, and increasingly sophisticated operational processes. Without a scalable foundation, AI initiatives risk becoming constrained by the very systems they are intended to improve.

This is yet another reason HealthEdge has invested heavily in cloud modernization and AI infrastructure.

The Cost of Waiting

Many organizations still view scalability as something to address only after performance challenges begin to emerge. The problem with that approach is that scalability issues rarely surface at convenient times. They tend to appear during periods of growth, acquisitions, open enrollment, regulatory change, product launches, or other business-critical initiatives.

Organizations that invest in scalable platforms before they need them are often better positioned to adapt to market changes, support innovation, pursue growth opportunities, and respond to evolving regulatory requirements with confidence.

Looking Beyond Today’s Demands

The most successful health plans are not building for today’s transaction volumes. They are increasingly prioritizing modernization, automation, interoperability, and connected operations as they navigate mounting industry pressures. Scalability sits at the center of all of those priorities.

Download the Full White Paper

To explore the full results of HealthEdge’s scalability testing, including the engineering innovations, AWS collaboration, testing methodology, performance benchmarks, and business implications for health plans, download the white paper: Platform Scalability: The Business Case for Growth, Performance, and Future Readiness.

 

Key Takeaways

• January 1, 2027 is a major regulatory milestone. Multiple regulatory deadlines—including prior authorization API requirements, work requirement mandates, and the AMA obstetrics billing restructure—take effect simultaneously.
• The WCAG 2.1 deadline extension is runway, not a reprieve. Health plans with federal and state contracts should accelerate digital accessibility remediation now to avoid exposure across audits, Star Ratings, and contract reviews.
• The No Surprises Act IDR process has been overhauled. A new final rule introduces standardized remark codes, mandatory payer registration, and a streamlined dispute portal—all requiring immediate workflow assessment.
• Medicaid State Directed Payments face new caps. A proposed rule could significantly limit payment structures, and the comment window closes July 21, 2026.
• OB billing restructure planning can’t wait. The American Medical Association’s (AMA) global maternity code overhaul takes effect January 1, 2027, and the operational implications span contracts, claims configuration, and utilization management.

Across the healthcare industry, operational complexity is mounting, timelines are converging, and January 1, 2027 is shaping up to be one of the most consequential compliance deadlines in recent memory.

In June, the HealthEdge® Regulatory Compliance User Group brought together health plan compliance professionals to examine six major regulatory developments—all of which carry significant operational implications.

Here’s what health plan compliance teams need to know right now to be prepared for January 1.

Prior Authorization and Interoperability APIs: What’s Your Readiness Status?

Deadlines tied to the Centers for Medicare and Medicaid Services (CMS) Interoperability and Prior Authorization Final Rule (CMS-0057-F) remain among the highest-priority items for many health plan leaders. Has your health plan aligned on its implementation strategy?

If not, join the Regulatory Compliance User Group on July 21 at 1pm ET as we address API readiness, the emerging National Provider Directory, and provider data considerations surfacing through ongoing CMS dialogue.

The WCAG 2.1 Extension Isn’t Permission to Wait

In April 2026, the Department of Justice issued an Interim Final Rule extending the compliance deadline for digital accessibility under the ADA Title II rule. Large entities now have until May 2028 to conform to WCAG 2.1 Level A and Level AA standards—a one-year extension from the original deadline. The public comment period closes in early July 2026.

Health plans that contract with federal or state government—including Medicare Advantage organizations, Medicaid managed care organizations (MCOs), and issuers on federal or state-based Exchanges—are bound by additional compliance obligations. These can include specific accessibility, language access, and beneficiary communication standards tied to contract renewal, audit, and oversight cycles.

The extension is an opportunity to accelerate remediation in a way that can withstand scrutiny from multiple regulators and contracting bodies. Delays can negatively impact program audits, state Medicaid contract reviews, Star Ratings and quality oversight, and routine readiness reviews tied to federal and state contracting.

Plans that don’t yet have an accessibility audit or a phased remediation plan in place should start now.

How Does the No Surprises Act IDR Final Rule Change Payer Operations?

Published in early June 2026, the No Surprises Act Independent Dispute Resolution (IDR) Operations Final Rule is the most comprehensive update to the dispute resolution process since the program launched in April 2022. The volume of disputes has dramatically exceeded original projections—5.1 million annual submissions versus an initial estimate of 22,000—and created significant backlogs and administrative strain across the system.

Health plans must prepare for key changes, including:

• Standardized remark codes on all out-of-network remittances
• Mandatory payer registration, including disclosure of legal business name, plan sponsor name, and registration number with each initial payment or denial
• Federal IDR portal as the exclusive channel for dispute initiation, replacing bilateral outreach and response requirement
• Batch disputes capped at 50 line items, subject to defined criteria
• Administrative fee reduced from approximately $115 to $15 per party per dispute
• A phased centralized IDR gateway platform, expected later in 2026

Health plans should assess current remittance and notification workflows against these requirements and prioritize system updates accordingly.

Medicaid State Directed Payments: What’s at Stake in the Proposed Rule?

CMS released a proposed rule on May 20, 2026, targeting Medicaid State Directed Payments (SDPs). The rule suggests significant payment caps and transparency requirements that Medicaid plans will need to evaluate carefully. The comment period closes July 21, 2026—a firm deadline for plans that want to shape the final outcome.

Key proposed provisions include:

• A cap of 100% of Medicare rates for SDPs in Medicaid expansion states, and 110% for non-expansion states, effective for rating periods on or after July 4, 2025
• Where no Medicare rate exists, the limit defaults to 100% of the state plan approved rate, with limited grandfathering exceptions
• Assessment at the individual claim or service level—not in aggregate
• A phased reduction of grandfathered SDPs by 10 percentage points annually beginning with rating periods on or after January 1, 2028

Medicaid plans should evaluate current SDP structures now and engage state partners in the comment process ahead of potential finalization.

Community Engagement and Work Requirements: Managing Complexity Across 45 States

The One Big Beautiful Bill Act (OBBBA), signed into law on July 4, 2025, established community engagement and work requirements for Medicaid-eligible adults. The federal implementation deadline is January 1, 2027, applying across all 43 Affordable Care Act (ACA) expansion states, plus Georgia and Wisconsin. The requirement is 80 hours per month for able-bodied adults ages 19 to 64.

CMS published an Interim Final Rule on June 1, 2026, providing guidance on:

• Qualifying activities, including employment, education, and community service
• Self-attestation, permitted through 2027, with documentation required thereafter when reasonably available
• Medical frailty exemptions, now requiring evidence that a condition significantly impairs the ability to meet the 80-hour threshold
• MCO role clarification allows member outreach and navigation support, but eligibility determinations remain exclusively with the state

Nebraska was the first state to enforce work requirements as of May 1, 2026, with approximately 72,000 low-income adults now subject to the program. Arkansas, Montana, and Iowa are implementing their programs in July 2026.

Health plans operating across multiple states face substantial operational challenges. Data matching requirements span payroll records, federal data hub inputs, Supplemental Nutrition Assistance Program (SNAP) participation, school enrollment, VA benefit records, and corrections agency data. Claims accuracy—particularly procedure codes tied to chronic conditions—directly affects exemption determinations.

Plans should assess encounter data completeness and develop member outreach workflows aligned to each state’s verification approach.

The AMA Obstetrics Billing Restructure: Why Planning Has to Start Now

Effective January 1, 2027, the AMA’s existing global bundled CPT codes for maternity care will be retired—17 codes deleted, 12 new codes added, and six revised. Providers will instead bill separately across four phases: antepartum, labor management, delivery, and postpartum.

The American College of Obstetricians and Gynecologists (ACOG) recommends that health plans and providers begin transitioning antepartum visit billing to unbundled evaluation and management (E/M) coding no later than September 1, 2026 to help avoid administrative burden and incorrect billing once the global codes are retired.

Based on historical updates to the Physician Fee Schedule, values for the new maternity codes will likely be proposed mid-2026 and finalized around November 2026 as part of the CY 2027 Physician Fee Schedule, though this timeline has not yet been confirmed by CMS.

6 Key Priorities for Health Plans Compliance Teams Ahead of January 2027

With multiple regulatory deadlines converging on January 1, 2027, health plan compliance teams should prioritize the following actions:

• Activate prior authorization and interoperability API testing now (and send the right stakeholders to the July HealthEdge® Regulatory User Group forum).
• Accelerate WCAG 2.1 remediation across digital and member-facing platforms, using the extended deadline as runway—not a reason to wait.
• Assess remittance and payment workflows against the new IDR Operations Final Rule requirements, including payer registration and standardized remarks.
• Submit comments on the Medicaid State Directed Payments Proposed Rule by July 21, 2026, and evaluate current SDP structures against the proposed caps.
• Map community engagement implementation timelines by state, assess encounter data completeness, and develop member outreach workflows aligned to each state’s verification approach.
• Begin OB billing restructure planning immediately, including provider contract renegotiations, claims configuration timelines, and utilization management workflow updates across all impacted product areas.

With multiple deadlines converging simultaneously, prioritization and cross-functional coordination aren’t optional—they’re essential. Health plans that begin structured planning now, rather than waiting for final rules, will be better positioned to meet these deadlines without operational disruption.

HealthEdge® customers: Join our Regulatory and Compliance User Group, a forum for regulatory professionals to collaborate and share best practices.

Not yet a customer? Sign up for our Regulatory and Compliance newsletter to stay current on regulatory developments and their implications for your health plan.

Frequently Asked Questions

What is the HealthEdge Regulatory Compliance User Group?

It’s a health plan-focused community open to all HealthEdge customers. The group meets monthly to discuss regulatory updates, their implications for HealthEdge products, and best practices among compliance professionals.

Why is January 1, 2027 such a critical compliance deadline?

Multiple major regulatory changes—including the Prior Authorization and Interoperability API requirements, AMA obstetrics billing restructure, and community engagement work requirements—all take effect on January 1, 2027, creating an unusually concentrated set of operational demands.

What does the WCAG 2.1 deadline extension mean for health plans with government contracts?

The extension to May 2028 doesn’t reduce compliance obligations for plans with CMS or state Medicaid contracts. Those plans carry parallel accessibility and communication requirements tied to contract renewal, program audits, and Star Ratings oversight. The extension should be used to accelerate remediation, not defer it.

What are the most important operational changes in the No Surprises Act IDR Operations Final Rule?

Key changes include mandatory payer registration, standardized remark codes on out-of-network remittances, exclusive use of the federal IDR portal for dispute initiation, batch dispute caps of 50 line items, and a reduced administrative fee of $15 per party per dispute.

When is the deadline to comment on the Medicaid State Directed Payments Proposed Rule?

The comment period closes July 21, 2026. Medicaid health plans and their state partners should evaluate current SDP structures and submit comments before that date.

How does the AMA obstetrics billing restructure affect health plan operations beyond claims?

The transition from global bundled CPT codes to phase-specific billing affects provider contract terms, utilization management workflows, case management processes, and claims system configuration—all of which require planning ahead of the January 1, 2027 effective date.

What should health plans do now to prepare for community engagement and work requirement implementation?

Plans should map implementation timelines by state, assess encounter data completeness—particularly procedure codes tied to chronic conditions—and develop member outreach workflows aligned to each state’s specific verification approach.

Key Takeaways

• HealthEdge® partnered with Ellipsis Health to integrate Sage, an AI-powered voice agent, directly into the GuidingCare® platform—giving health plans a scalable way to automate member outreach.
• Sage uses clinically validated conversational AI and an Empathy Engine to conduct emotionally intelligent phone conversations with members across a range of care management programs.
• Key use cases include health risk assessments (HRAs), post-discharge follow-up, risk profiling, care gap closure, and member enrollment outreach.
• The partnership directly addresses clinical workforce shortages by automating high-volume, routine outreach and freeing nurses to focus on complex, high-acuity cases.
• GuidingCare customers can deploy Sage quickly through a referral partnership, with conversation flows configurable to their specific clinical goals and workflows.

Scaling Smarter with Sage and GuidingCare

Health plans are under pressure to meet increasing demands for member engagement, care coordination, and improved clinical outcomes. But the ongoing healthcare workforce shortage makes it difficult for payers to scale resources.

The result is familiar: care managers are stretched thin by outreach and administrative requirements, reducing the time they can spend proactively engaging with patients and assessing potential gaps in care.

Payers need a scalable way to automate member outreach. That’s why HealthEdge® partnered with Ellipsis Health to integrate Sage, an AI-powered voice agent for care management, directly into the GuidingCare platform. Sage enables health plans to

automate outbound calls, broaden member reach, drive proactive health management, and lower administrative costs.

What Is AI-Powered Virtual Nursing?

AI-powered virtual nursing tools use conversational AI to conduct phone-based outreach with health plan members. Unlike traditional automated calls or Interactive Voice Response (IVR) systems, AI-powered voice agents like Sage are designed to hold natural, emotionally intelligent conversations—and adapt in real time based on how a member responds.

How Sage Works—and How it Stands Out

The new partnership between HealthEdge and Ellipsis Health makes AI-powered virtual nursing available to GuidingCare users. Sage is integrated within the GuidingCare ecosystem, allowing it to draw from existing member information to personalize each conversation and deliver updated information from these interactions back into the platform.

For health plans already using GuidingCare or Wellframe, it’s a seamless extension of their existing care management infrastructure.

Sage is not a scripted phone bot. Instead, it mirrors human conversation—including intonations and natural pauses—using Ellipsis Health’s patented sentiment matching technology, which was trained using millions of live clinical patient calls. This allows Sage to engage in active listening and interviewing, and adjust its responses according to member cues.

What does this look like for health plan members? During conversations, Sage can:

• Deliver affirmations and acknowledgements that reflect active listening
• Recognize emotional cues, like irritation, sadness, or confusion
• Calmly redirect conversations to stay focused on the goal of the call

Why is this important for health plans? Because member engagement is more than a checklist of questions asked over the phone. It’s about building a trusting relationship with members so they feel supported and actively involved in their own health.

Where Health Plans Can Put Sage to Work

Sage helps payers address the highest-volume, most time-intensive outreach tasks care management teams face. Configurable to each health plan’s clinical goals and workflows, Sage supports:

• Clinical workforce optimization: Extend the reach of care managers with automated routine outreach and empower staff to focus on high-acuity cases and help support more members.
• Predictable cost savings: The technology behind Sage enables cost-efficient, scalable outreach and delivers rapid ROI with improved engagement outcomes.
• Seamless ecosystem integration: GuidingCare customers can quickly deploy this AI solution via referral partnership, enabling customized engagements based on health plan’s individual goals, as well as Medicare and Medicaid requirements.
• Health risk assessment (HRAs) completion: Scale outreach to meet regulatory, quality, and risk adjustment requirements. Sage conducts the full HRA in a single outreach call and adds updates to GuidingCare automatically.

Sage gives care management teams back hours of time per week—time that can be redirected toward the members who need the highest levels of clinical expertise and intervention.

“As a nurse, I’ve seen and experienced what happens when care managers are stretched thin,” says Marla Kraak, Product Director of Care Management at HealthEdge®. “Members fall through the cracks—not because anyone stopped caring, but because there simply aren’t enough hours. An AI voice agent that can handle routine HRA outreach and flag members who need a real conversation isn’t replacing the nurse. It’s making sure the nurse shows up where it counts.”

Scaling Engagement Without Sacrificing Quality

A core challenge in care management is finding the right balance between scale and quality. Reaching more members matters, but so does ensuring those interactions remain meaningful, effective, and capable of driving better outcomes at a lower total cost of care. Sage is built to engage entire member populations simultaneously, enabling health plans to scale high-volume outreach far beyond what manual efforts alone can support. With conversations that are personalized, clinically grounded, and emotionally aware, Sage helps plans identify needs earlier, close gaps in engagement, and guide members toward the right interventions before issues escalate into more costly utilization.

For health plan decision makers focused on lowering total cost of care, that combination matters. Timelier outreach, better member navigation, and stronger follow-through can help reduce avoidable emergency department visits, unnecessary utilization, and delays in care—while supporting more efficient use of care management resources.

For health plan IT leaders, integration details are equally important. Sage connects directly to the GuidingCare platform, allowing member data and outcomes to flow seamlessly between systems without manual reconciliation or another disconnected point solution to manage. The result is a more unified platform that supports both operational efficiency and more cost-effective care management at scale.

A Practical Path Forward for Care Management Leaders

With Sage, the combination of clinical intelligence, emotional design, and seamless platform integration empowers health plans to extend their existing resources and better support members across the risk spectrum.

GuidingCare customers: Want to learn more about elevating your care management strategy with Sage? Contact your Customer Success Executive for a demo.

Not yet a HealthEdge customer? Download the Care Solutions data sheet to learn how your health plan can leverage AI-driven tools within your care management operations.

Frequently Asked Questions

What is Sage, and how does it differ from a standard automated phone system?

Sage is an AI care management voice agent developed by Ellipsis Health. Unlike traditional IVR or scripted call systems, Sage uses a patented Empathy Engine to analyze vocal cues in real time and adapt its responses based on emotional signals. It’s trained on millions of live clinical patient calls, making it capable of holding natural, clinically informed conversations with members.

How does the HealthEdge and Ellipsis Health integration work?

Sage integrates directly with HealthEdge’s GuidingCare platform. It draws on member data stored in GuidingCare to personalize outreach conversations, and it returns outcomes back into the platform once calls are completed. This creates a closed-loop workflow with no manual data reconciliation required.

What types of care management programs can Sage support?

Sage supports a range of high-volume outreach programs, including health risk assessments, post-discharge follow-up calls, risk profiling, care gap closure, and member enrollment outreach. Conversation flows are configurable to each health plan’s specific clinical goals and compliance requirements.

How does this partnership address clinical workforce shortages?

By automating routine, high-volume outreach tasks, Sage frees nursing staff to focus on complex, high-acuity cases that genuinely require clinical expertise. Health plans can engage entire member populations simultaneously without adding headcount, making care management programs more scalable and cost-efficient.

Is this solution compatible with Medicare and Medicaid programs?

Yes. Sage is designed to be highly configurable, allowing health plans to align conversation flows to the specific requirements of Medicare, Medicaid, and other program types. GuidingCare customers can work with their Customer Success Executive to configure deployments accordingly.

What is a health risk assessment, and why does it matter for health plans?

A health risk assessment (HRA) is a structured set of questions designed to evaluate a member’s general health status, lifestyle factors, and potential care gaps. For payers, HRAs are critical for risk adjustment, quality reporting, and regulatory compliance. Completing them at scale—and accurately—directly affects a health plan’s risk profile and program performance.

How quickly can GuidingCare customers deploy Sage?

Sage is available to GuidingCare customers through a referral partnership, designed for efficient deployment. Health plans can contact their HealthEdge Customer Success Executive to begin the process and explore configuration options aligned to their care management strategy.

For many health plans, HEDIS® final submission feels like the finish line. In reality, it’s the moment when months of quality improvement efforts, chart retrieval activities, and data validation processes are put to the test. A single discrepancy can affect reported performance, compliance outcomes, and ultimately the quality ratings that influence revenue and member growth.

To help health plans navigate this important stage, we’ve outlined best practices for submission readiness, key quality assurance (QA) activities, and lessons learned to strengthen future HEDIS cycles.

Laying the Groundwork for Success in 3 Steps

The most successful HEDIS submissions don’t begin in the weeks leading up to the deadline — they’re built on a foundation of preparation, organization, and cross-functional collaboration throughout the measurement year.

As the final submission approaches, health plans should focus on the following three critical areas:

1.     Establish a Clear Submission Timeline

Submission deadlines often create a flurry of activity across quality, operations, analytics, compliance, and vendor teams. Without a structured timeline, critical validation and review activities can become compressed, increasing the risk of errors.

Leading organizations establish milestone-based submission plans that include:

• Data validation checkpoints
• Medical record review completion targets
• Measure-level review sessions
• Compliance reviews
• Executive sign-off milestones
• Submission readiness assessments

Organizations that implement structured submission timelines often see a marked reduction in submission errors, highlighting the value of proactive planning and accountability.

2.     Reconcile and Validate All Data Sources

As HEDIS measures increasingly rely on supplemental data, electronic clinical data systems (ECDS), and multiple reporting sources, ensuring consistency across datasets has become more challenging and more important than ever.

One of the most common challenges during final submission is ensuring consistency across multiple data sources. Claims data, pharmacy records, encounter information, laboratory results, supplemental data, and medical record review findings must all align before submission. Even minor discrepancies can create downstream reporting issues and require costly last-minute remediation efforts.

Health plans should perform comprehensive reconciliation activities to confirm:

• Measure calculations are accurate
• Supplemental data is properly integrated
• Member eligibility files are current
• Medical record review findings are reflected appropriately
• Vendor-delivered data aligns with internal reporting

Strong retrieval performance also plays a significant role in submission success. Across its client base, HealthEdge Quality360™ has achieved retrieval rates exceeding 95%, helping plans maximize data completeness and reduce reporting gaps.

3.     Align Stakeholders Early

Success requires coordination across multiple departments, including quality improvement, operations, compliance, analytics, IT, provider engagement, and executive leadership.

Establishing clear ownership and accountability before submission deadlines helps ensure everyone is aligned on reporting requirements, measure interpretations, and validation responsibilities.

The Power of Precision: QA in Action

As submission deadlines approach, quality assurance becomes the final safeguard against reporting errors and compliance risks. To ensure submission readiness, health plans should go beyond basic validation and incorporate measure-level reviews, documentation verification, and regulatory compliance checks into their final QA process. Let’s dig into each of the comprehensive QA best practices:

Conduct Comprehensive Measure Reviews

To help identify anomalies before they impact final results, every measure should undergo a conclusive review to validate:

• Numerator and denominator accuracy
• Exclusions and exceptions
• Supplemental data inclusion
• Medical record review outcomes
• Measure-specific logic and calculations

Leverage Automation to Improve Accuracy

Manual review processes remain important, but automation can significantly improve efficiency and consistency by helping health plans:

• Identify data discrepancies
• Detect incomplete records
• Validate measure calculations
• Highlight compliance concerns
• Reduce manual review effort

HealthEdge’s automated QA capabilities have helped improve data accuracy by 30% while reducing submission preparation time by 15%.

Verify Regulatory Compliance

Compliance validation is often one of the last and most important steps before final submission. Health plans should verify alignment with current NCQA requirements, audit standards, and submission guidelines to minimize risk and ensure confidence in reported results.

HealthEdge clients have achieved a 100% medical record review validation pass rate, underscoring the importance of comprehensive QA and audit-readiness processes.

Building a Smarter Future: Lessons from the Field

Organizations that consistently improve quality outcomes often treat each submission cycle as a learning opportunity to identify what worked, what didn’t, and how to improve future performance.

They view their final submission not as the end of the measurement year, but as the beginning of preparation for the next one. Here’s how they do it:

Capture Lessons Learned

To help teams refine workflows and avoid repeating challenges in future years, conduct structured reviews after submission to document:

• Process bottlenecks
• Data quality challenges
• Resource constraints
• Vendor performance
• Successful strategies and interventions

Gather Feedback Across Teams

Soliciting feedback from quality teams, analysts, auditors, provider engagement teams, and operational stakeholders can uncover opportunities for improvement that may otherwise be overlooked.

Turn Insights into Action

Continuous improvement requires more than documenting lessons learned. It requires acting on them.

Health plans that systematically incorporate lessons learned into future quality programs have achieved a 20% improvement in Star Ratings within two years. The impact can be even more dramatic when organizations combine process improvements with targeted quality strategies.

Through HealthEdge Stars Consulting, one health plan achieved a full 1-Star gain in a single year, a milestone accomplished by only 1.5% of plans.

Turning Final Submission into Future Success

Final submissions are an opportunity to validate the quality initiatives, operational processes, and member engagement efforts that took place throughout the year.

By establishing structured submission processes, implementing rigorous quality assurance practices, and capturing lessons learned for future improvement, health plans can reduce risk, strengthen compliance, and improve the accuracy of their reported results.

As HEDIS requirements continue to evolve and quality performance becomes increasingly tied to financial outcomes, organizations that approach final submission with discipline and precision will be better positioned to achieve stronger quality results, higher Star Ratings, and better member outcomes.

Discover how HealthEdge Quality360® and Stars Consulting can help your organization improve data accuracy, strengthen compliance, and maximize quality performance.

Download the brochure, HealthEdge Quality360: The Next-Generation Integrated HEDIS® Engine & Analytics Platform.

Key Takeaways

• For health plans, the implementation period is one of the highest-risk, highest-stakes phases of any technology investment—and AI can significantly reduce that risk.
• HealthEdge Delivery Services integrates AI-powered tools across the 4 key stages of solution implementation: Discovery, Configuration, Testing, and Deployment and Stabilization.
• The combination of deep domain expertise and AI-enhanced tooling means health plans get faster time to value without sacrificing quality or compliance.

How HealthEdge Applies AI Across 4 Key Stages of Implementation

For health plans, selecting the best-fitting technology solution is only half the battle. How payers approach implementation can drastically affect adoption timelines and time to value. Stakes are high, and new implementations impact critical areas like claims processing, benefit configuration, provider relationships, and regulatory compliance all at once.

Historically, common challenges include long implementation timelines, troubleshooting and rework, varying levels of implementation consultant expertise, and late discovery of requirement gaps. But at HealthEdge, our Delivery Services team applies AI where implementation work has traditionally been highest in effort, variation, and risk — across four key stages: Discovery & Requirements Analysis, Configuration & Build, Testing, and Deployment & Stabilization.

In addition, Delivery Services maintains a human in the loop model when using AI. AI outputs are treated as intelligent drafts that experienced implementation consultants validate and refine — preserving quality while enabling speed.

Stage One: Discovery and Requirements Analysis

Discovery and Requirements Analysis is the stage that sets the foundation. This is where HealthEdge teams and health plan leaders define project scope, align stakeholders, and identify initial requirements.

The Challenge

Health plans must provide a significant amount of information up front, often under time pressure. Workflows may not be fully documented, and critical details can be spread across multiple teams and systems. For HealthEdge, implementation consultants must quickly absorb large volumes of legacy documentation, a time-consuming process where gaps or missed information can result in costly rework later.

How HealthEdge uses AI During Discovery and Requirements Analysis

HealthEdge implementation consultants use AI tools to analyze the intake documentation and automatically pull out the relevant information, cross-reference requirements against platform capabilities to identify any gaps, and surface any potential compliance issues early.

In addition, rather than arriving to a session with a blank questionnaire, our implementation consultants bring a pre-populated toolkit of information based on an AI-assisted analysis of the health plan’s existing documentation, public regulatory information from each state or the Centers for Medicare and Medicaid Services (CMS), and comparable prior implementations. The session is spent validating and refining, not capturing potentially repetitive information.

What Health Plans Can Expect

• Initial discovery timelines reduced by 50% (from 8 weeks to 4)
• Less administrative burden on the health plan team
• Earlier risk identification
• A stronger foundation for the stages that follow

Stage Two: Configuration and Build

During the configuration stage, the HealthEdge implementation team translates Discovery requirements into a functioning system — configuring benefit structures, building integrations, establishing workflows, and creating business rules that govern how the health plan operates.

The Challenge

The configuration and build stage is complex and requires significant manual effort. Teams spend considerable time drafting configuration artifacts, creating business rules, and translating documentation into system setup.

How HealthEdge uses AI During Configuration and Build

Teams start with intelligent drafts that experienced implementation consultants review, refine and validate – eliminating the need to build from scratch. For GuidingCare, this includes generating draft business rules and translating specifications into ready-to-review forms and workflows. For HealthRules® Payer, the Delivery team uses AI to accelerate the conversion of plan artifacts into configuration-ready inputs – extracting benefits and plan provisions from source documents and generating draft configuration logic.

What Health Plans Can Expect

• Faster time to value
• Reduced manual work
• Greater consistency across modules
• Lower risk of errors reaching production

Stage Three: Testing

Testing is where the solution comes to life. The HealthEdge implementation team ensures the configured system meets the health plan’s operational requirements and performs as expected across real-world scenarios—before it impacts members, providers, and operations.

The Challenge

Testing is time-consuming and resource-intensive, and many health plans have limited resources to dedicate. Both the HealthEdge implementation team and the health plan users must validate numerous workflows, integrations, and benefit configurations under strict timelines. Traditional manual testing approaches often leave coverage gaps and allow defects to surface too late in the process.

How HealthEdge uses AI During Testing

Testing is where AI delivers some of its most measurable impact. Delivery Services uses AI to automatically generate test cases, produce regression tests when rules or workflows change, and identify recurring defect patterns early. For upgrades, AI maps what has changed to a health plan’s specific workflows, so teams know exactly where to focus testing.

What Health Plans Can Expect

• Faster testing cycles
• More comprehensive coverage
• Reduced workload for health plan teams
• Lower go-live risk.

Stage Four: Deployment and Stabilization

During Deployment and Stabilization, the HealthEdge implementation team transitions system management to the health plan, monitors performance, and maintains high responsiveness to ensure a stable operational rollout.

The Challenge

Health plan customers and the HealthEdge Implementation team must triage and prioritize issues rapidly while keeping leadership informed, which can cause significant pressure.

How HealthEdge uses AI During Deployment and Stabilization

HealthEdge uses AI to keep implementations on track and issues from escalating. The team leverages AI to help monitor project risks, triage defects by severity and business impact, and analyze patterns across issues to shift teams from reactive troubleshooting to proactive quality management.

What Health Plans Can Expect

• Faster issue resolution
• Shorter stabilization periods
• Lower operational disruption
• Faster realization of business value

How HealthEdge Implementations Position Payers to Achieve Real Results

AI-enhanced delivery directly impacts outcomes health plan leaders care about across the implementation lifecycle:

• Faster time to value: Leveraging AI helps accelerate every stage of implementation, so health plans can realize the benefits of their investment sooner.
• Higher quality outcomes: Standardized patterns, automated validation, and broader test coverage help reduce rework and lower the risk of downstream adjustments.
• Lower-risk, scalable delivery: AI tools help Implementation teams and users combine platform knowledge and institutional experience to streamline adoption.
• A delivery model built for the future: For HealthEdge, the human-in-the-loop approach makes sure AI enhances expert judgment to give health plans a partner equipped to handle complexity without sacrificing rigor.

Ready to See AI-Enhanced Delivery in Action?

Interested in learning how AI-enhanced delivery can accelerate your next implementation? Existing customers, reach out to your HealthEdge Customer Success Executive to get started.

Want to learn more about how HealthEdge is leveraging AI across internal functions and within our award-winning solutions? Watch the recent webinar with HealthEdge Chief Technology Officer Rob Duffy, “AI Capabilities: Transforming Payer Strategies with HealthEdge.”

 

Frequently Asked Questions about HealthEdge Implementation and AI

What is HealthEdge Delivery Services?

HealthEdge Delivery Services is responsible for deploying, upgrading, and optimizing HealthEdge solutions for health plan customers. The team brings deep expertise in health plan operations, benefit configurations, testing, data migration, and integration design.

What are the four stages of implementation?

The four stages are Discovery, Configuration, Testing, and Deployment and Stabilization. AI-powered tools are embedded across all four to reduce manual effort, improve accuracy, and accelerate timelines.

How does AI reduce implementation risk for health plans?

AI reduces implementation risk by catching problems early and preventing them from compounding. During Discovery, AI flags requirement gaps and compliance issues before configuration begins. In Testing, it generates test cases and detects defect patterns before go-live. At Deployment, AI shifts teams from reactive troubleshooting to proactive issue management. Throughout, experienced implementation consultants validate every AI output – preserving quality while enabling speed.

Does AI replace the expertise of HealthEdge implementation consultants?

No. AI amplifies implementation consultants’ expertise by handling high-volume, repetitive tasks—such as documentation generation, test scenario creation, and requirements analysis—so implementation consultants can focus on complex, judgment-intensive work where their domain knowledge matters most.

How does AI-enhanced implementation affect time to value for health plans?

By compressing the time required for discovery, requirements gathering, documentation, testing, and validation tasks, AI-enhanced delivery significantly shortens implementation timelines. That means health plans can begin realizing the operational and financial benefits of their HealthEdge solution sooner.

Which HealthEdge solutions does Delivery Services support?

HealthEdge Delivery Services supports implementation across the HealthEdge product portfolio, including HealthRules® Payer, HealthEdge GuidingCare®, and other integrated platform components. Customers should connect with their Customer Success Executive to discuss scope and available services.

This is part 3 of a blog series on Ethical AI. For context on this series and why we’re writing it, see our introduction in part 1, Ethical AI: Privacy and Security.

How Can Payers Address Bias in AI Systems?

The previous article in this series covered what AI bias is, how it surfaces in outputs, and how it can enter AI systems. The key points: fairness is a key component of ethical AI, and its effects can be subtle and difficult to detect. Bias exists along a variety of demographic, medical, and socioeconomic lines, and can be unwittingly introduced even with good intentions.

This post addresses the practical question that follows: how can an organization address bias in AI systems?

The answer varies by role. Every person at HealthEdge® engages with AI in some capacity—as a user, as a tool selector, or as a builder—and the interventions available differ accordingly.

Guidelines for AI Users

The most immediate leverage most people have over AI bias is how they interact with AI systems day-to-day—and that leverage is more significant than it might appear.

Before writing a prompt, consider the framing. AI output reflects the inputs provided, and embedded assumptions shape the result. For example, consider querying an AI for a healthcare use case. Would this input be described the same way if the person had a different name, different insurance coverage, or a different background? For example, perceptions of a patient being “drug-seeking” versus exhibiting “undertreated pain” represent the same clinical presentation framed differently, and the AI model will respond to each in meaningfully different ways. In addition, stay aware of the degree and quality of context you provide—if that differs across cases, the model’s output quality may differ as well.

Be aware of common bias patterns when reviewing AI output. For example, recommendations that vary by demographic attribute, summaries that are shorter or less detailed for certain groups, tone differences across groups, or assumptions that fill ambiguous information with stereotypes.

When an output seems problematic, reporting the observation and adapting in the short term are both important steps. In the interim, adjusting prompts to counteract observed patterns is a practical response.

Guidelines for AI Buyers

Beyond individual interactions, many people at HealthEdge influence which AI tools and vendors the organization adopts.

For any decision involving the selection of AI tools—for enterprise software, vendor collaborations, or personal day-to-day use—bias considerations should be explicitly included in the assessment.

Evaluation should include:

• What bias metrics does the system use, and what justifies that choice for this use case?
• What training populations does the model reflect?
• Are disaggregated performance metrics available?
• Are there published model cards or transparency reports that acknowledge known limitations?
• What monitoring occurs after deployment?

Responses like these should serve as warning signs:

• Dismissing bias concerns with “we don’t use race or gender” and ignoring proxy variables
• Claiming a tool is “objective” or “unbiased” without supporting evidence
• An inability to name a specific bias metric
• No acknowledgment of model limitations
• Testing that is limited to pre-deployment with no ongoing monitoring

Guidelines for AI Creators

For those involved in building AI features—whether as designers, engineers, product managers, or testers—the responsibility to address bias is essential. Building fair AI systems should be part of each step of the software development lifecycle.

1. During design: The definition of “fair” should be established before development begins. Subject matter experts (SMEs) with a deep understanding of the use case should be included early in the process to identify the areas where bias is most likely or would cause the most harm.
2. During development: Teams should audit source data for representation gaps and prompts for embedded assumptions. For example, what does an instruction to “be concise” or “extract the most relevant information” implicitly prioritize? Try testing alternative phrasings and reviewing few-shot examples for demographic diversity.
3. During evaluation: Performance should be measured on subgroups rather than relying solely on aggregate metrics. Counterfactual testing should be built into the evaluation pipeline by systematically varying one demographic dimension at a time and measuring output differences. Edge cases and ambiguous inputs, where bias is likely to surface, should be included in the test set. Qualitative review is also necessary, as differences in tone, framing, and agency attribution may not appear in automated accuracy metrics and could require direct human comparison of outputs.

After deployment, monitoring for drift is important, since patterns can emerge or intensify over time. Known disparities should be documented even when they cannot be fully resolved, as transparency about the limitations of AI tools enables informed use and creates a foundation for future improvement.

Building Trust Through Shared Accountability

The steps outlined above span different roles and different stages of the AI lifecycle, but they share a common thread. The central theme is that fairness in AI is not a task to be delegated or a box to be checked at the end of a project. Instead, it requires deliberate attention across every stage of the process—from everyday use to vendor selection, and prompt construction through deployment and post-release monitoring.

The organizations that sustain that attention will not simply avoid causing harm but build systems that healthcare organizations and the members they serve can genuinely trust.