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When health plans implement new software, upgrade existing platforms, or roll out new features, one factor is key to achieving success is effective testing.`

While testing may not always grab headlines, it plays a foundational role in delivering the reliable, compliant, and high-performing systems that health plans need. Whether you’re launching a claims adjudication engine, enhancing care management workflows, or integrating third-party solutions, testing ensures everything works together as intended, before your teams and members rely on it in the real world.

What is Software Testing for Health Plans?

Software testing for health plans is, at its core, the process of evaluating a system or application to ensure it functions as expected, meets business requirements, and performs reliably and securely. It identifies gaps, defects, or missing requirements before the software reaches production.

In short: testing confirms that your technology delivers on its promises.

Among health plans, that could mean validating that a claim adjudicates correctly, that a member’s eligibility is reflected accurately, or that provider data flows seamlessly across integrated systems.

When Is Software Testing Conducted?

Testing isn’t just a final checkbox before go-live. It is a continuous discipline. At HealthEdge, testing is embedded throughout the software lifecycle:

• During implementation: To ensure the solution is configured to meet a health plan’s unique business needs.
• With each upgrade or enhancement: To verify new functionality works and doesn’t disrupt existing workflows.
• When integrating systems: To validate seamless data exchange across modules and third-party platforms.
• After configuration changes: Even small updates to business rules or workflows require validation.
• During data migrations: To confirm that legacy data is transferred accurately and remains usable.
• Post–go-live: Monitoring and testing continue to safeguard performance and compliance.

Whether it’s a major platform overhaul or a minor update, every change introduces risk, and proper software testing mitigates that risk.

Types of Software Testing: Categories and Levels

To fully appreciate the value of software testing, it’s important to understand both why we test and how much we test. HealthEdge employs a multi-layered testing strategy that includes testing categories and testing levels, each playing a distinct role in validating system quality and performance.

A testing category explains the purpose of the test. Common testing categories include:

• Functional Testing: Verifies that the system performs intended tasks correctly, such as processing a claim or authorizing a service.
• Non-Functional Testing: Assesses how well the system operates under real-world conditions: speed, scalability, reliability, and security.
• Regression Testing: Ensures that new changes haven’t broken existing functionality, especially important during upgrades or patch releases.

A testing level describes the scope of the test being performed or how much is being tested.

Each level of testing increases in scope and complexity. Together, they ensure that everything from individual modules to full workflows is working properly across the system. 

Unit Testing

This is the most granular level of testing, focused on the smallest functional components of an application. A “unit” might be a single module or a specific calculation logic. The purpose of unit testing is to validate that each piece works in isolation, based on its defined business logic.

Why it matters for health plans: Catching and resolving issues early prevents them from escalating into more complex integration problems down the line. For example, ensuring that pricing logic correctly interprets a provider contract before it’s incorporated into the larger claims system.

System Integration Testing (SIT)

SIT tests the interaction between integrated units or modules of a system. Even if each component functions correctly on its own, they may not work together properly. This level ensures that components communicate effectively and behave as expected when combined.

Why it matters for health plans: Think of SIT as validating the handshakes between components of a system, which is critical in healthcare, where disparate modules, such as enrollment and assessments, must work together flawlessly to enable a care manager to create a new care plan.

End-to-End Testing (E2E)

E2E testing simulates real-world workflows from start to finish across all integrated systems and touchpoints. It validates that the entire application behaves correctly in a complete business scenario. For example, a test that starts with a member eligibility check, proceeds to service authorization, moves to a claim submission, and ends with adjudication and payment.

Why it matters for health plans: For health plans, this is a critical step to confirm that the member, provider, and payer experiences align and that data flows without interruption or error across all phases of the business process.

User Acceptance Testing (UAT)

UAT is the final gate before go-live. It’s where real users (business stakeholders, operations staff, or clinicians) test the system in scenarios that reflect actual business operations. Unlike earlier stages, this isn’t about code correctness—it’s about usability, practicality, and business fit.

Why it matters for health plans: UAT confirms that the system supports real business operations as intended, not just technical requirements. For example, business users may validate that benefit accumulators calculate correctly across benefit tiers before go-live.

What Testing Requires: Scenarios, Cases, and Data

Behind every successful testing strategy is a solid foundation of well-defined artifacts: test scenarios, test cases, and test data. These components ensure that testing is not only comprehensive but also replicable, traceable, and tied to real-world use.

• Test Scenarios
• A test scenario describes what is being tested at a high level. It’s typically aligned with a business workflow or functional goal, such as “submitting a healthcare claim” or “checking member eligibility.” Scenarios help ensure that critical business processes are covered.
• Test Cases
• Test cases define how a test scenario will be validated. They include specific steps, input values, and expected outcomes to verify that a function performs correctly. For example, a test case for claim submission may include logging into the portal, entering claim details, and verifying a submission confirmation.
• Test Data
• Test data consists of the input values used during test execution—such as member IDs, policy numbers, claim amounts, or provider credentials. Using realistic, representative data that has been de-identified for privacy is essential for simulating actual conditions and uncovering edge cases or errors.

Together, these testing artifacts help teams validate software behavior, trace issues back to requirements, and demonstrate that systems are ready for production use.

The Value of Software Testing

Ultimately, testing isn’t just about identifying bugs. It’s about delivering confidence. Comprehensive testing minimizes risk and prevents costly disruptions by identifying issues early in the process. It ensures that the system is aligned with business needs, validates critical workflows, supports compliance with industry standards, and safeguards the accuracy of member and provider data. Perhaps most importantly, it builds trust across the organization.

When testing is done right, health plans experience smoother go-lives, faster user adoption, and greater assurance that their technology will perform reliably in real-world scenarios.

Ready to Strengthen Your Testing Strategy?

From implementation to upgrades, the HealthEdge Global Professional Services team brings best-in-class testing frameworks, tools, and expertise to every engagement. Let us help you launch with confidence, upgrade without disruption, and deliver reliable results to your members and providers. Read our case study to learn more: From Bottleneck to Breakthrough — How Health Plans are Automating Prior Authorization with HealthEdge.

Recently, the HealthEdge® Chief Solutions Officer hosted a discussion with four health plan executives to discuss how they’re using advanced, integrated technology solutions to enhance claims processing, enrollment, payment accuracy, and provider data management at their organizations.

Panelists included:

• Vice President (VP) of a New York City-based plan serving low-income members
• Director of Operations Management of a Utah-based health plan
• Director of Vendor Management of a nonprofit health plan serving Medicaid and CHP+ members
• Senior Vice President (SVP) of Health Plan Information Services of a New York-based payer serving Medicare and Medicaid members

For the past 30 years, conversations about core processing have centered on auto-adjudication rates. While this metric is still important for controlling costs, health plan leaders are now expanding their focus to a broader set of operational goals. The rapid pace of change in healthcare requires a more holistic approach to claims management and processing.

Data Architecture that Supports Interoperability

The SVP of Health Plan Information Services noted that interoperability mandates like the Interoperability and Prior Authorization Final Rule (CMS-0057-F) from The Centers for Medicare and Medicaid Services (CMS) drove his organization to adjust its data architecture so both care providers and customer service teams have the data they need to support health plan members.

“55% of our membership is in an Independent Physician Association (IPA),” said the SVP. “We’re trying to measure and grow that number. Utilizing the [HealthEdge Source™] platform to get data and work with those IPAs grow our membership, I think will bring better quality and experience to our members.”

By investing in application programming interface (API) integration tools to connect disparate systems, the plan has already seen significant improvements in its customer service operations. Integrating issue tracking within their CRM with the core system has provided member services teams with greater visibility and created a better call center experience for members.

“We have a number of complicated relationships between entities in our metro area, with complicated risk and financial arrangements,” said the Vice President. “All of that business logic has to exist in a provider data hierarchy that makes sense for us.”

By using APIs and web services to automate inbound information, the plan is successfully reducing turnaround times and lowering costs per transaction. This transition represents a multi-year journey from an inefficient legacy state to a modernized system that is transforming operational efficiency.

Taming the Provider Data Challenge

Provider data management remains a persistent and universal challenge for health plans, according to our panelists. Inaccurate provider data leads to downstream issues like claims and payment errors, which negatively impacts health plan costs as well as provider and member satisfaction.

“Our members experience us the most through our providers,” said the Vice President. “So when we get claims wrong…when we get the check wrong, when we get the directory wrong, our members feel that. Our providers feel it first, our members feel it through them, they see our name on the card and they groan. So it’s really important we improve that experience.”

As a customer of the HealthRules® Payer, HealthEdge Source, and HealthEdge Provider Data Management solutions, the Vice President also emphasized the importance of data transparency.

“Provider data flows through both systems,” he said. “Having that data flow through our ecosystem in a tightly coupled manner is what’s going to drive higher quality—and that’s what we really need at the end of the day.”

The Director of Operations Management also shared his experience navigating regulatory hurdles, as the payer’s state contract requires written approval for any AI application using state data. To address this, the organization established an AI governance committee and internal policies to streamline compliance.

“Now that we have the [HealthEdge® Provider Data Management] platform, we can do everything we wanted to do,” said the Director of Operations Management. “We can take data from every different source, we can give them APIs that say, ‘this is how you can give us the data we need.’ We can have that data governance to say, ‘this is what we keep, this is what we have to have, this is how we have to have it to make the data auditable.’”

The Strategic Role of AI and Automation

The conversation around AI is evolving from a data-centric view to a focus on automation and efficiency.

“What we’re focusing on right now is turnaround time on issue resolution and data exchange—when we take data in, how quickly can we get it processed?” said the Director of Operations Management. “We had so much manual functionality from enrollment to authorizations, and the question now is ‘How do we automate that?’”

Similarly, another panelist emphasized the abilities of AI-powered tools that give more time back to the humans in charge of payer operations.

“We’re planning to use AI and agentic AI to help bolster our quality overall,” said the Vice President, “and free up our FTEs to focus on more skilled, necessary activities and decision-making to keep things up-to-date. There’s a lot of advancement there, and it’s really exciting.”

But adopting AI solutions can be challenging for many health plans. For the Director of Vendor Management, strict state guidelines hinder the payer’s ability to leverage AI-powered tools. The health plan sought to leverage an AI solution to automate note-taking for conversations between interdisciplinary care teams—but it took six months to get approval, delaying their abilities to hold engaged conversations without breaking focus. Ultimately, this challenge led the payer to form its own AI governance procedures and trainings to try and innovate internally while staying in adherence with state regulations.

Integrating the Digital Health Ecosystem

The forward-thinking strategies shared by these leaders emphasize the importance of innovation and a proactive approach to addressing the evolving landscape of healthcare. By using automation, advanced data management and enhanced architecture, health plans can stay flexible while navigating regulatory demands and industry pressures. These insights show that collaboration and adaptability are key to driving meaningful change in healthcare.

“We’ve got 30 years of legacy systems on an antiquated core platform that had band-aid after band-aid built upon it, and that’s not a sustainable model,” said the Vice President. “So going to the HealthEdge ecosystem is a strategic move for us to bring our data assets closer together.”

Discover how the Public Employees Health Program (PEHP) leveraged the integrated HealthEdge Provider Data Management solution to improve data matching, regulatory compliance, and operation efficiency. Read the case study: Provider Data Management Enhances Value of Data and Employees Alike.

Health plans are racing ahead with artificial intelligence (AI). Members are moving more cautiously. In 2026, the question is no longer whether AI will shape healthcare. It’s whether payers and members are aligned on how it should.

According to the 2026 Healthcare Payer Survey Report from HealthEdge®, 94% of payers are either live with or actively adopting AI, and nearly half (47%) report widespread or departmental use. But as adoption accelerates inside health plans, a critical question emerges:

Are members ready for the AI-powered experiences payers are building?

And more importantly, what will it take for health plans to succeed with AI in 2026 and beyond?

Where the Industry Stands: AI Is Moving Fast

The survey data makes one thing clear: AI momentum is strong among healthcare leaders.

• 47% of executives reported widespread or departmental use.
• Mid-size payers (supporting 2-10M lives) are 30% more likely than their peers to report widespread adoption of AI.
• Executive leaders report the highest confidence in AI maturity, with 31% claiming widespread adoption of AI vs. only 3% of operational and regulatory leaders.

 

Health plans are not just using AI to improve internal efficiency—they are applying it to member-facing experiences. Member engagement is tied with claims processing for the highest reported use of AI, signaling that personalization and digital interaction are central to payer strategy. Yet adoption alone does not define readiness.

Where Is AI Being Applied Most Often?

AI Adoption Is Outpacing Governance

While nearly all payers are deploying AI, only 31% report having fully defined governance models and controls in place.

This governance gap introduces strategic tension. AI is being deployed into high-impact workflows (claims adjudication, prior authorization, engagement), but oversight frameworks are still maturing.

Without governance clarity, transparency, and cross-functional alignment, even well-intentioned AI initiatives risk eroding trust or creating compliance exposure. When trust begins to erode, the effects don’t stay contained within a single workflow. Member satisfaction declines. Call center volumes increase. Complaints rise. And during open enrollment, members who feel uncertain or unsupported are more likely to explore alternatives.

Over time, that erosion shows up in retention rates, HEDIS and Star ratings, and organizational growth. AI initiatives that lack transparency or governance can unintentionally create the very friction they were meant to eliminate.

This brings us to the member side of the equation.

 

The Member Perspective: Open to AI, But Not Fully Convinced

While payer adoption is accelerating, member adoption is more measured.

According to the 2025 HealthEdge Healthcare Consumer Study:

• Only 21% of members report using AI-powered tools offered by their health plan
• 58% have not used AI tools
• 21% are unsure whether they’ve used AI tools
• Among non-users, 64% say they would be open to using AI tools in the future

Members are not rejecting AI. But they are not embracing it at the pace at which payers are deploying it.

When asked what types of AI tools they would most likely use, members expressed strong interest in:

• Chatbots and virtual assistants
• Personalized education and resource recommendations
• Cost-saving benefit and provider tools
• Health tracking and coaching tools

The demand signal is there. But so is hesitation.

The Trust Barrier: What Members Need

While 23% of members report no concerns about AI use by their health plan, the majority cite specific issues:

• 26%: Quality and accuracy
• 20%: Privacy
• 20%: Data security
• 11%: Not knowing how to use it

More revealing is what would increase consumers’ comfort, as shown in this chart. This is where the readiness gap becomes clear. Payers are accelerating deployment. Members are asking for transparency and guardrails. Adoption appears to be ahead of trust.

 

Where Payer and Member Readiness Diverge

Comparing the two surveys reveals three critical gaps.

1. Operational Readiness vs. Experiential Readiness: Payers are embedding AI into claims, care management, and engagement workflows. Members are still deciding whether AI improves or complicates their experience.

2. Efficiency vs. Confidence: Health plans prioritize efficiency, automation, and cost containment. Members evaluate AI through accuracy, fairness, privacy, and clarity.

3. Investment vs. Communication: Payers are investing heavily in AI capabilities. Members are asking to be informed when AI is used, and why. This is not a technology gap. It is a perception gap.

What Will It Take to Succeed with AI in 2026 and Beyond?

The data from both surveys point to four imperatives for health plans:

1. Strengthen governance alongside deployment. AI maturity must include oversight, ethical controls, and auditability.
2. Make AI visible and explainable. Members want transparency, not hidden automation.
3. Focus on high-friction use cases. Cost clarity, claims accuracy, and benefit navigation are where AI can immediately build trust.
4. Balance AI and human collaboration. Technology should reinforce—not replace—the human relationship between a health plan and its members.

The Bottom Line: Readiness Needs to Even Out

AI adoption across health plans is accelerating. But technology readiness and trust readiness are not the same thing.

The fact is: Members stay with plans they trust. They leave when they don’t. If AI makes it easier to understand benefits, resolve a claim, or get timely support, it strengthens that trust. If it feels confusing, inconsistent, or opaque, it creates friction, and that friction shows up during open enrollment.

The risk isn’t that health plans are moving too fast with AI. The risk is deploying it in ways that outpace governance, transparency, and operational alignment. That’s where execution matters.

At HealthEdge, we focus on embedding AI directly into core workflows — not as a bolt-on, but as part of how claims are adjudicated, care is managed, and payment integrity is enforced. That means building governance into the system, designing for auditability, and aligning automation with real operational outcomes.

Download the Full Research Reports

To explore the complete survey findings, including adoption trends, governance maturity insights, and detailed consumer sentiment, download the full report, The Great Rebalancing: Inside the New Realities Shaping Health Plan Performance, 2026 Healthcare Payer Survey Report.

In today’s rapidly evolving healthcare landscape, standing still isn’t an option. Health plans relying on legacy care management platforms, whether vendor-supported, homegrown, or heavily customized, are increasingly feeling the strain of outdated technology, limited visibility, and escalated compliance demands.

Across the industry, health plans are prioritizing operational efficiency, regulatory alignment, and data-driven care delivery as top priorities for 2026. Recent HealthEdge® research shows intensifying regulatory pressures are forcing a seismic shift in health plan priorities, with technology and IT-business alignment emerging as critical levers for managing cost and risk.

If rising costs, shrinking margins, and complex compliance requirements are weighing on your organization, you’re not alone. Many health plans are increasingly focusing on aligning IT and business strategies and leveraging technology as key approaches to address regulatory challenges and cost pressures.

Here are five unmistakable signs your legacy care management system may be holding your organization back—and why now is the perfect time to evaluate a modern alternative like HealthEdge GuidingCare® for care management.

1. Compliance Is a Constant Struggle, Not a Confidence Point

Legacy care management systems weren’t designed for today’s rapidly evolving compliance landscape.

Between federal and state regulatory changes, evolving quality metrics, and increasing reporting requirements, care management platforms must adapt quickly. Yet legacy platforms often require manual updates or custom patches just to stay compliant, creating risk and operational drag.

Modern industry outlooks call this period one of reinvention, where organizations must operate on systems that are stable yet adaptable to shifting requirements.

2. Reporting Takes Days—or Even Weeks

Legacy care management systems often store data in silos, forcing teams to extract data manually across systems, cleanse it, and stitch reports together. This results in:

• Long turnaround times for leadership reporting
• Delayed insights into utilization trends
• Reduced confidence in data accuracy

Healthcare leaders are increasingly focused on operational execution powered by data and automation. Modern platforms, like GuidingCare, provide near-real-time reporting with integrated data models, empowering users to generate actionable insights without relying on IT handoffs or manual processes.

3. Inefficient Workflows Are Draining Resources

Care managers and clinical staff play a critical role in delivering high-quality outcomes, yet many legacy systems prioritize documentation over workflow efficiency. Common pain points include:

• Duplicate entry across programs
• Complex navigation and context switching
• Manual coordination between utilization management, care management, and quality teams

Industry research shows that aging IT infrastructure not only consumes valuable resources but also limits organizations’ ability to innovate and deploy emerging capabilities such as AI and automation. GuidingCare addresses these challenges by streamlining workflows with intuitive user experiences and automation, enabling care teams to focus on what truly matters—delivering exceptional member care rather than navigating system workarounds.

4. Your System Can’t Scale with Your Growth or Strategy

Whether expanding into new lines of business, launching new care programs, or adopting value-based care models, scaling your care management platform should be straightforward, not bogged down with technical debt.

However, legacy systems often:

• Require costly customizations for new programs
• Complicate integrations with external systems
• Limit the ability to adopt new operating models

According to broader industry outlooks, systems must be agile, integrated, and data-ready to support evolving care delivery models and competitive strategies in 2026 and beyond. GuidingCare is purpose-built for growth, offering rapid configuration, extensibility, and cross-program scaling without the need for costly overhauls.

5. Member Satisfaction Is Suffering

Your care management system should ultimately drive better member outcomes and experiences. Yet, legacy platforms often fall short, offering limited visibility into engagement and lack cohesive care coordination capabilities, which can negatively impact:

• Member satisfaction scores
• Adherence and outcomes
• Trust and loyalty

As industry shifts toward adaptive care models and integrated delivery, the need for systems that support seamless engagement across care journeys has never been greater.

Care solutions like GuidingCare and HealthEdge Wellframe™ deliver integrated member experiences and advanced measurement capabilities that help care teams personalize support, drive adherence, and deliver outcomes that matter, boosting both satisfaction and retention.

The Cost of Standing Still Is Too High

In a healthcare landscape defined by cost pressures, regulatory complexity, workforce strain, and rising expectations for outcomes, health plans can no longer afford to let outdated care management hold them back.

By transitioning away from legacy platforms to a modern solution like GuidingCare, health plans can:

• Improve compliance with less operational strain
• Access faster, more actionable analytics
• Enable efficient workflows that allow your clinical staff to work at the top of their licenses
• Scale with growth and innovation as you align with your organization’s growth strategy, goals, or expansion plans
• Deliver superior member experiences and outcomes

The time to act is now.

Discover how GuidingCare helped transform Nascentia’s outdated system into a modern care management solution that improves outcomes and drives operational excellence. Read the full case study: Disjointed to Dynamic — How Nascentia Health Modernized Care with HealthEdge GuidingCare.

At a recent executive roundtable, HealthEdge® brought together health plan leaders from across the U.S. to share their experiences and see how other organizations are addressing key challenges. One recurring barrier? How to eliminate internal data siloes and leverage actionable insights.

In this article, we highlight key insights from a panel of payer executives who shared how their organizations are currently leveraging the HealthEdge ecosystem to improve data accuracy, transparency, and efficiency.

Why should payers focus on interoperability and data transparency?

Interoperability has been a major disruptor for the healthcare industry, forcing payers to address legacy technologies, siloed processes, and outdated systems. This shift has highlighted the undeniable need for digital innovation and vendor partnership.

By focusing on making data accessible and understandable, payers can streamline processes and move toward a more integrated, forward-thinking system that supports the future of interoperability.

“As we evaluated our operations, we recognized the need to take a data-centric approach to drive meaningful change,” said one panelist, the Enterprise Platform Strategy Leader at a regional health plan. “We applied the RACI model to our data, not just for identifying users and authors, but also focusing on data transformers, a critical yet often overlooked piece. Data is transformed through processes, and by understanding this, we could better align and optimize our operations to meet interoperability requirements.”

What are the practical advantages of leveraging a cloud-first, modern architecture to break down data silos?

Focusing on data as a corporate asset was key. For digital solutions like the HealthEdge ecosystem, the cloud isn’t just a storage site—it’s a foundation for everything from interoperability to analytics. For example, using the FHIR data standard in the cloud isn’t just for compliance. By making this information available in a universal format, health plans can easily repurpose it in areas like provider workflows, integrating data where it adds the most value and meeting requirements beyond regulatory mandates.

“We’ve been cloud-first for 10 of the 11 years I’ve been with the company,” said the Chief Information Officer at a New York-based health plan. “We’re as well-positioned as we could be. Because we organize our data according to FHIR, we’ve been able to use it to create endpoints that we’ve been able to integrate into provider workflows that has delivered value.”

How do health plans address the separation of “business” and “IT” to drive cross-functional collaboration?

“Separating business and IT is a false dichotomy,” said the Vice President of IT at a nationwide health plan. “In our role, we have a unique vantage point to see the silos and inefficiencies that exist across operational areas. It’s not enough to simply point these out; we need to understand the business deeply enough to propose and build meaningful use cases that deliver real value.”

Take “care-related” transactions as an example. Sometimes, the only way a provider knows a member has been discharged is through personal phone calls—a process that’s far too slow. By following the member’s journey and connecting all the data touchpoints, we can provide timely, actionable insights. For instance, tracking discharge data and delivering it to providers immediately can help prevent re-admissions and ensure better care. Our role is to connect the dots, drive accountability, and create solutions that enable business value and improve outcomes.

How can payers ensure new technologies improve efficiency instead of adding more complexity?

The process begins by evaluating what the legacy system actually does. Usually, the system is attempting to serve every need but failing to address core priorities effectively. Payer leaders must identify when a workflow faces bottlenecks, or when a cluttered system is more overwhelming than supportive.

“We really had to understand what our legacy platform did,” said the Enterprise Platform Strategy Leader. “Because we built it, it became all things to all people. But it can’t be everything to everyone, otherwise it’s nothing to nobody. Our legacy platform had more than 500 letters for member communications. By simplifying the logic and adopting a more efficient data model with HealthEdge GuidingCare®, we were able to reduce that to 17.”

With tools like GuidingCare Letters, member communications can be generated in real-time without manual effort, significantly cutting overhead and allowing care managers to focus on improving member outcomes.

With so much data available, how can payers determine what is actionable for improving care management?

Having a lot of data means nothing if you can’t trust it or act on it. The first step is to build a culture that trusts and validates available information so it can guide organizational action. It’s common for health plans to get different answers to the same question depending on where and when the data is pulled.

“I don’t think our core KPIs change, it’s the speed at which we understand the data in order to get those KPI changes materializing,” said the Vice President of IT.

Solutions like HealthEdge® Provider Data Management can automate data ingestion and validation to give payers a single source of truth, reducing workflow complexity and improving the member experience.

What are the most promising AI strategies you’re seeing in healthcare right now?

Artificial Intelligence (AI) is a hot topic, with use cases ranging from streamlining care management to enhancing customer experiences. Innovative uses include agentic AI for tasks like syncing provider data across platforms or performing ambient call center analytics.

“Trust is the fuel that goes in the rocket of AI,” said the Vice President of IT. “We’re exploring how to use AI to identify the right data, confirm its cleanliness, understand its governance and history, and then apply it effectively. The problem often isn’t the absence of data, but rather knowing which data is clean, what it means, and how to use it. By using AI to establish that foundation of trusted data, we can unlock its full potential.”

AI-driven tools can unlock new possibilities, but the costs often emerge before the economic benefits, requiring health plans to maintain careful oversight and budget management. The key to success is keeping humans in control—defining, containing, and curating the knowledge an AI agent can access while validating its outputs.

“I think it’s really important that humans have to stay in control,” said the Chief Information Officer, “We’re spending a lot more time investing in managing knowledge and making sure we’re in control of the knowledge we give AI access to.”

What are the key technology challenges and priorities for health plan executives?

A persistent roadblock for many payers is that demand for new projects consistently exceeds the available supply of resources. It’s easy to initiate technology adoption, but far more challenging to demonstrate tangible value and complete them. Promoting a culture of rapid iteration and testing is essential.

“A cultural thing we struggle with is embracing failing often enough, because we’re trying something so new—and embracing that at the engineer level is key,” said the Vice President of IT. “We’re seeing such an accelerated pace of change in technology that if we spend too much time trying to make one solution work that multiple competitive capabilities can come out in the meantime. It’s important to be able to fail, fail fast, be okay with it, and move on to new things.”

Another significant challenge is driving adoption and establishing trust among teams who may be skeptical of new technologies. It is common for users to demand explainable, compliant AI solutions before they are willing to fully integrate them into their workflows. Consequently, effective change management and complete transparency regarding the capabilities and limitations of these tools are essential for successful implementation.

Achieve Greater Value from your Digital Solutions

This panel discussion revealed that breaking down data silos is not just a technical challenge, but a strategic priority for healthcare organizations. By aligning technology with business objectives and implementing AI responsibly, health plans can leverage verified data to streamline operations and deliver superior member experiences.

Learn more about how your health plan can leverage technology to deliver an integrated and impactful member experience in the eBook, “Disjointed to Dynamic: How Nascentia Health Modernized Care with HealthEdge GuidingCare.”