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Health plans with home and host capabilities have set the standard for healthcare excellence since 1929—but mounting industry pressures constantly challenge these market leaders.

Decreasing margins, evolving regulatory requirements, and the shift toward value-based care demand continuous innovation. To remain competitive and stay ahead of industry shifts, health plans need data-driven solutions that simplify administrative processes while driving strategic cost management.

Health plan leaders are turning to next-generation core administrative processing systems (CAPS) to overcome complex healthcare challenges. See how payers are leveraging the integrated HealthRules® Payer CAPS to improve home and host plans to improve operational efficiency, boost member satisfaction, and achieve regulatory excellence.

1. Navigating Complex Regulatory Compliance

Health plans that manage government programs face a constant stream of state and federal regulatory changes. Non-compliance can result in severe financial sanctions or operational disruptions. Core administration systems must enable rapid, reliable changes to underlying rules without requiring significant IT interventions.

HealthRules Payer provides payers with the agility necessary to maintain compliance across Medicare, Medicaid, and Dual-Eligible lines of business as regulations evolve. By leveraging proactive health management tools and producing auditable, highly accurate reporting, your organization can deliver high-quality care while controlling costs.

2. Driving Operational Efficiency and Automation

Manual claims processing drains crucial resources and increases the risk of costly errors. Efficiency cannot come at the expense of accuracy when you need to maintain positive relationships with providers and members. Home and host plans require a system that delivers accurate claims auto-adjudication across categories.

HealthRules Payer combines advanced automation with financial accuracy. One health plan leveraged the CAPS solution to increase claims auto-adjudication volume by 800%. Paired with a 98% configuration accuracy rate, this level of operational efficiency directly supports strategic cost management and reduces member and provider abrasion.

3. Adapting to New Business Models Swiftly

The healthcare market increasingly relies on complex value-based reimbursement models. Outdated legacy systems often struggle to accommodate these varied group and benefit packages. This limitation leads to slow, error-prone configurations that impede your market agility.

The patented, English-like HealthRules Language forms the backbone of the HealthRules Solution Suite. It offers unmatched flexibility for payers to define benefit plans and provider contracts quickly. Business analysts can perform configuration updates in hours instead of days or weeks. This technical flexibility allows users to seamlessly integrate new products and value-based care models without relying on programmers and IT teams.

4. Improving Member Satisfaction and Outcomes

Integrated data is vital for enabling efficient operations and building strong provider relationships. Legacy platforms frequently lack real-time data exchange capabilities, leading to fragmented care and delayed claim resolutions. Health plans require integrated ecosystems that deliver precise information to support care teams and improve overall member health.

Through robust integration layers like HealthRules Connector and the analytical power of HealthRules Answers, HealthEdge delivers seamless integration across your entire digital ecosystem. This single source of truth helps payers achieve up to a 90% first-call resolution rate. When customer service representatives have immediate access to accurate data, member satisfaction naturally rises.

Unlock the Full Potential of Your Health Plan

Thriving in the current healthcare landscape requires technology that acts as a catalyst for growth and resilience. By optimizing core administrative processes, you can significantly reduce manual work, lower administrative overhead, and unlock per member per month (PMPM) savings.

Ready to explore how the right core administrative processing system can transform your operations?

Discover detailed insights, performance metrics, and the proven ROI that HealthEdge delivers to industry-leading health plans. Download the brochure: How Plans With Home And Host Capabilities Lead In Value-Based Care.

 

Risk adjustment programs are entering a new phase of maturity. Historically, health plans approached Medicare Advantage risk adjustment retroactively, reviewing charts after encounters occurred, and identifying missed diagnoses later in the year.

Retrospective risk adjustment remains an important part of a health plan’s risk adjustment program. But regulatory and documentation guidelines are accelerating the shift toward proactive strategies that combine retrospective review with real-time documentation validation and prospective risk adjustment.

Risk Adjustment & Regulatory Pressures

The scale of risk adjustment is really why this shift matters. Medicare Advantage payments exceeded $450 billion in 2024, with risk scores playing a central role in determining payers’ risk adjustment payment levels. As a result, even small documentation gaps can translate into significant financial and compliance implications.

Recent policy changes and audit activity are reinforcing this trend. The expansion of the Risk Adjustment Data Validation (RADV) program from The Centers for Medicare and Medicaid Services (CMS) has increased pressure on participating organizations to ensure diagnoses are fully supported by documentation. Research from the Kaiser Family Foundation also showed that chart reviews play a significant role in payer risk adjustment operations, with more than 60% of Medicare Advantage members associated with at least one chart review in recent years.

Rather than waiting until year-end reviews to identify documentation gaps, payers are increasingly building programs that continuously monitor risk capture, provide earlier feedback to providers, and support documentation improvement throughout the care cycle.

While most industry attention focuses on Medicare Advantage, many organizations are applying similar strategies across Medicaid and commercial programs—where accurate documentation and coding also influence reimbursement, quality measurement, and program sustainability.

This shift is giving rise to a new operating model: real-time risk adjustment.

What Real-Time Risk Adjustment Means in Practice

Real-time risk adjustment does not replace retrospective chart reviews. Instead, it helps shorten the feedback loop by enabling health plans to identify documentation opportunities earlier, strengthen provider engagement, and maintain continuous visibility into risk capture performance.

In traditional retrospective models, coding teams and analytics groups often identify documentation gaps months after a patient visit occurs. By that point, the clinical context may be difficult to reconstruct, making follow-up more challenging for both providers and risk adjustment teams.

A real-time approach addresses this gap by introducing continuous monitoring across the health plan risk adjustment program. Clinical documentation patterns can be evaluated throughout the year, allowing teams to detect emerging trends earlier and take corrective action while the information is still relevant.

This also means that risk adjustment insights appear closer to the point of care. Coders can prioritize the most impactful charts for review, provider engagement teams can deliver targeted documentation guidance, and analytics teams can gain earlier visibility into how risk adjustment factors are evolving throughout the year.

The result is a program that operates continuously rather than episodically.

Technology That Enables Modern Risk Adjustment Documentation and Coding

The shift toward real-time operations is largely enabled by advances in analytics and clinical data integration.

5 Key capabilities for modernizing payer risk adjustment programs:

1. Live electronic health record (EHR) integrations that allow encounter data and clinical notes to flow directly into risk adjustment analytics environments
2. Natural language processing (NLP) tools that analyze clinical documentation and highlight potential diagnosis gaps or coding opportunities, as referenced in a recent Cornell University study
3. AI-assisted triage models that prioritize charts most likely to contain high-impact documentation opportunities
4. Clinical decision support tools that surface documentation prompts during provider encounters
5. Data and analytics platforms that consolidate encounter data, chart review activity, and risk score performance metrics

An important note: It is imperative for health plans to minimize overcoding. Payers can utilize OIG regulations and evaluate data to reduce or eliminate overcoding and RADV audit risk.

These technologies are increasingly used to support Hierarchical Condition Categories (HCC) risk adjustment coding, helping organizations identify undocumented conditions earlier and strengthen the accuracy of risk adjustment submissions. Emerging research also supports the growing role of artificial intelligence in documentation analysis.

Importantly, these technologies are not designed to replace coding expertise or clinical judgment. Their primary value lies in helping risk adjustment teams focus attention on the records and member populations where documentation improvements can have the greatest impact.

Building the Operational Capabilities for Real-Time Risk Programs

Technology alone cannot transform risk adjustment operations. Organizational alignment and well-designed workflows are equally important.

Leading health plans are establishing risk operations teams responsible for coordinating analytics, coding workflows, and provider engagement initiatives. These teams serve as the connective layer between data insights and operational action within the broader risk adjustment program.

Within these programs:

• Risk operations leaders monitor documentation trends and coordinate chart review priorities.
• Coding teams focus on validating diagnoses and ensuring documentation integrity.
• Provider engagement teams work directly with clinicians to reinforce documentation best practices and strengthen collaboration across the payer risk adjustment ecosystem.

Strong feedback loops are critical to making these programs effective. When documentation patterns reveal potential gaps, those insights must be shared with providers in a constructive and timely way. Successful programs position documentation guidance as part of broader clinical documentation improvement efforts, helping providers understand how accurate documentation supports both population health management and reimbursement accuracy.

Measuring the Impact of Real-Time Risk Adjustment

Within any payer risk adjustment program, operational metrics help organizations determine whether risk adjustment factors accurately reflect the clinical complexity of their member population.

Traditional program metrics, such as overall risk score performance, remain important. However, many organizations now track additional operational indicators that provide deeper insight into how effectively their risk adjustment programs function throughout the year.

Common examples include:

• Timeliness of chart review completion
• Coder productivity and throughput
• Speed of documentation gap identification
• Rate of suspected condition closure
• Variability in risk scores across reporting periods

Monitoring these indicators provides a more dynamic view of risk adjustment performance. It also allows organizations to identify operational bottlenecks earlier and make course corrections long before final submissions are due.

Another way of measuring the impact of risk adjustment is by eliminating the waste associated with unnecessary chart reviews, thus realizing cost savings. Excluding members without HCC or risk-adjustable conditions from review pipelines immediately reduces heavy administrative expenses. This data-driven solution maximizes operational efficiency, saving significant costs and allowing staff to focus strictly on high-yield interventions and improved health outcomes.

These operational insights are increasingly important as organizations manage multiple programs simultaneously, including risk adjustment for Medicare, Medicaid, and commercial initiatives.

The Future of Risk Adjustment Operations

Looking ahead, advances in analytics, artificial intelligence, and workflow automation will continue shaping how risk adjustment programs operate.

Predictive models are beginning to identify members whose clinical histories suggest undocumented conditions. AI-driven analytics platforms can highlight documentation patterns across large provider networks. Automated workflow tools can prioritize chart reviews and route documentation questions to the appropriate teams.

Together, these capabilities are helping organizations move beyond reactive chart review cycles toward more proactive documentation management that complements, but doesn’t replace, the human coder.

Moving Toward a More Proactive Risk Adjustment Strategy

Real-time risk adjustment represents a natural evolution in how organizations manage risk adjustment documentation and coding, improve payment accuracy, and strengthen risk program performance.

Retrospective programs will remain essential for validating diagnoses and recovering missed conditions from prior encounters. However, when combined with prospective documentation improvement initiatives and real-time analytics, they become part of a more comprehensive strategy for managing risk adjustment performance.

Many health plans are now exploring integrated approaches that combine retrospective chart review, prospective documentation improvement, and real-time analytics. Modern risk adjustment solutions and services, such as those provided by HealthEdge®, are designed to support this evolving model by helping organizations strengthen documentation validation, provider collaboration, and analytics-driven risk operations.

Learn more about how HealthEdge is empowering health plans to build a successful, sustainable risk adjustment program, download our White Paper: Getting Risk Adjustment Right – A Guide for Modern Health Plans.

This is part 1 of a blog series on Ethical AI.

This content was adapted from an internal learning and development session developed by HealthEdge’s AI team, focused on educating our organization on ethical AI. At HealthEdge, we believe that safe and responsible AI is of the utmost importance. This principle shapes both how we use AI internally to accelerate our own efficiency and how we build AI-powered solutions for our customers.

These materials reflect how our AI team thinks about these problems day to day. Ethical AI isn’t something we address at the end of a project or check off during a compliance review. Rather, it’s a lens we apply from the earliest stages of design through deployment and beyond and sharing these principles openly — with our own teams and with the broader community — is part of how we hold ourselves accountable.

Ethical AI Starts with Privacy and Security

As artificial intelligence becomes more widely adopted across healthcare technology platforms, protecting sensitive data has become a critical responsibility for organizations that build and deploy AI solutions. Many users rarely think about where their inputs go or how they may be stored until something goes wrong.

In healthcare environments, where protected health information (PHI) is involved, the stakes are particularly high; privacy failures can lead to regulatory consequences, loss of trust, and real harm. For organizations developing AI-powered tools, privacy and security must be designed into every decision, from tool selection to system architecture.

Privacy Isn’t Just a Policy — It’s a Design Problem

At its core, privacy is about ensuring that personal data is collected, used, and retained appropriately, and that people maintain control over their information. Simple in theory. AI makes it complicated in practice.

Large language models can memorize training data and spit back Personally Identifiable Information (PII) in unexpected contexts. People paste sensitive information into third-party tools without thinking about retention. Data gathered for one purpose quietly gets repurposed for another. And “anonymized” datasets? Often not as anonymous as advertised, as re-identification is a well-documented risk. For those of us in healthcare, this extends to Protected Health Information (PHI), meaning privacy failures aren’t just bad practice — they’re compliance violations.

If you’re a user, know what you’re feeding into these tools. Assume your inputs may be stored. Don’t paste in someone else’s personal data without authorization. And understand what your organization actually allows as input.

If you’re evaluating tools, ask the uncomfortable questions. Where does the data go? How long is it kept? Does the free tier use your inputs for model training, evaluation, or monitoring? (Many do.) Where does data physically reside? If a vendor can’t give you straight answers about data handling, that tells you what you need to know.

If you’re building, design for privacy from day one. Collect the minimum data you need. Be upfront about how you use it. Build in deletion and user control. And don’t lean on the LLM itself for access control, that’s not what it’s for. Assume that any data used to train the model could end up being model output; curate training datasets carefully.

Security: The Threat Surface You Might Be Underestimating

Security means protecting systems and data from unauthorized access, manipulation, and exploitation. With AI, the attack surface has grown in ways that catch teams off guard.

• Prompt injection lets bad actors manipulate model behavior through crafted inputs.
• Model inversion can extract training data from responses.
• Adversarial inputs slip past safety controls.
• Indirect prompt injection, poisoned content embedded in documents or data sources, is particularly stealthy if LLM guardrails don’t scrutinize the content before processing that information as instructions, causing unexplained or undetected malicious agent behavior.

Add API key exposure, credential leaks, and supply chain vulnerabilities, and there’s a lot to account for.

If you’re a user, never hand API keys, tokens, or credentials to an AI tool. Think twice before running AI-generated code. Double-check any output that touches security decisions or access controls. If something looks off, report it. When testing new tools, use sandboxed accounts with limited permissions.

If you’re evaluating tools, look for real security hygiene, such as documented incident response and documented guardrails. Ideally, these include published metrics, SOC 2 or ISO 27001 certification, clear credential management, and evidence of pen testing or red teaming. No security docs? Vague authentication story? Third-party integrations without defined boundaries? Walk away.

If you’re building, assume every input is hostile. Rate-limit and validate aggressively. Give your AI components their own credentials with the minimum necessary permissions. Never let the model make authorization decisions. Set up guardrails and monitor for prompt injection and data exfiltration patterns. Stay on top of dependency updates and run your systems against the OWASP Top 10 for LLMs.

This Isn’t a One-and-Done Conversation

Privacy and security are not one-time considerations addressed during an architecture review. They are ongoing disciplines that influence every stage of AI development and deployment, from the engineer designing prompts and guardrails to the product leader evaluating vendors and integrations. Organizations that embed these principles into their AI strategies will do more than reduce risk. They will build the level of trust that responsible AI adoption ultimately depends on.

For more information about HealthEdge’s approach to AI, visit www.healthedge.com.

When health plans modernize their core administrative systems, the conversation often centers on technology. Implementation timelines, configuration decisions, and integrations dominate planning discussions as organizations prepare for go-live.

Yet one of the biggest factors determining whether a new platform delivers meaningful operational improvements has little to do with the technology itself. According to research from McKinsey & Company, successful transformations are more than three times more likely when organizations provide dedicated training to help employees master new solutions.

It comes down to whether the people responsible for running the system understand how to use it.

Leveraging Education Services for Product Training

For health plans implementing HealthRules® Payer, training is critical to ensuring the platform operates as intended and that operational teams can fully leverage its automation capabilities. Without that foundation, even the most advanced technology can struggle to deliver the expected improvements in efficiency and accuracy. In fact, any team member who supports implementation, serves as an in-house instructor, or uses the system on a day-to-day basis should receive training.

That is why HealthEdge® Global Professional Services provides dedicated Education Services designed to help health plans build the expertise needed to successfully implement and optimize HealthRules Payer.

HealthEdge Education Services Overview

HealthEdge offers a comprehensive suite of training programs, tailored to every stage of implementation and beyond. The programs support both technical and business teams and are designed to empower a health plan’s in-house training team to confidently educate those responsible for implementing and using the system every day. There’s even an opportunity for implementation partners to participate in structured certification programs that ensure consistent expertise, best-practice alignment, and a higher standard of delivery across the ecosystem.

The Real Costs of Insufficient Training: Internal Misalignment

Health plans are often under strict time constraints when implementing a new administrative platform. Internal leaders participate in discovery workshops, design sessions, and configuration reviews while continuing to manage their day-to-day responsibilities.

In this environment, training can easily be deprioritized—but the consequences typically appear quickly once the system is in production, where small misunderstandings can create operational friction.

For example, terminology differences between legacy systems and HealthRules Payer can cause confusion early in the transition. A “provider” label in one system may appear as “supplier” in another. While the difference may seem minor, misalignments like these can interfere with understanding system data and configuring workflows.

Unnecessary Manual Intervention

Training gaps can also lead to complications like improper or incomplete user setup, which can result in increased manual intervention during claims processing and lower automation levels across the platform. Teams may also overlook powerful capabilities and miss out on opportunities to simplify operations.

For example, if a health plan experiences a retroactive member termination, the system should trigger automatic claim reprocessing. If users don’t know HealthRules Payer can automate this process, they may spend unnecessary time on manual review.

Without proper attention to product training, users may rely on workarounds or manual processes that the platform was designed to eliminate.

Building Knowledge Throughout the Implementation Journey

HealthEdge Education Services are designed to enable business and technical users who support the implementation throughout the HealthRules Payer deployment lifecycle.

Rather than treating training as a one-time activity, programs align with the natural phases of implementation—starting with foundational education of key concepts and terminology. Training becomes more hands-on as the project progresses into design and build phases, allowing internal subject matter experts to gradually build knowledge while applying what they learn directly to the configuration of their system.

Self-guided learning modules also provide an introduction to core platform capabilities. Instructor-led training sessions then give teams the opportunity to work directly within the system, ask questions, and explore real-world scenarios that reflect their organization’s operational workflows. Coaching sessions are also available to help reinforce learning by allowing participants to bring questions that come after working in the system.

Preparing End Users for Day-to-Day Operations

One of the most important aspects of system training occurs as organizations prepare for go-live.

In many implementations, the people configuring the platform are not the same ones who will use it every day. Claims processors, enrollment teams, and finance staff may interact with the system differently from the implementation team that helped design it. To address this challenge, Education Services leads End User Enablement workshops to assist in-house trainers as they develop their own internal programs.

During these dedicated workshops, HealthEdge trainers work closely with a health plan’s in-house instructors to develop training materials tailored to the organization’s configuration and operational workflows. Templates and guidance from the HealthEdge team help health plan teams build the training materials for their operational staff.

This approach helps ensure end users learn workflows as they exist in their unique environment. It also helps operational teams understand how their day-to-day processes will evolve as they transition from legacy workflows to the automated capabilities available in HealthRules Payer.

Why Training Matters Long After Go-Live

The importance of training does not end once the system launches.

As health plans expand their use of HealthRules Payer, introduce new benefit designs, or refine operational workflows, new training needs often emerge. Organizations frequently return for focused refresher training that target areas where teams request additional support.

Education Services works closely with customers to understand where knowledge gaps exist. The training team may collaborate with delivery managers or implementation consultants to understand the payer’s unique challenges to tailor the sessions.

This collaborative approach allows health plans to strengthen internal expertise, improve operational efficiency, and reduce reliance on external consulting resources over time.

Modernizing Training With AI-Powered Tools

The Education Services team is also evolving how it creates and delivers training content.

Traditional software training programs often rely heavily on written documentation. While comprehensive, these materials can be time-consuming for busy operational teams to work through.

To improve the learning experience, the team is transforming existing training materials, such as written documentation, presentations, and recorded trainings into dynamic video-based learning modules using AI-powered tools. This approach significantly accelerates the development of training content, giving subject matter experts a more accessible way to review training materials.

Many professionals prefer to learn using video-based training. Short, focused modules allow users to quickly revisit topics and understand exactly how workflows are performed within the system. This initiative turns hundreds of training modules into interactive learning experiences that make it easier for HealthRules Payer users to build and maintain system expertise.

Empowering Health Plan Teams for Long-Term Success

Successful system implementations depend as much on people as they do on technology.

HealthRules Payer provides health plans with powerful automation capabilities, operational flexibility, and the ability to manage complex benefit structures with precision. However, realizing full value requires that users understand how to configure, manage, and optimize their organization’s use of the solution.

Education Services helps health plans build that expertise from the earliest stages of implementation and continue developing it over time.

When organizations invest in training early, they accelerate implementation timelines, improve operational outcomes, and empower their teams to take full advantage of the capabilities within HealthRules Payer.

Discover additional ways that HealthEdge Global Professional Services can help your health plan get more value from your investment in HealthRules Payer with custom code services. Read the data sheet.

Part 2 of a 2-part series, where HealthEdge® Vice President of Product Development Bobby Sherwood discusses how a new operating model can transform care operations for health plans.

In the first installment, Sherwood defined what an ecosystem operating model is, and how it can enable payers to improve care delivery, streamline administrative processes, and improve member outcomes.

Read part one here: Preparing for the Future of Care Management with an Integrated Operating Model.

Continue reading to learn more about measuring success with AI-powered tools, steps to achieving operational transformation, and seamless data sharing.

Investing in Member Engagement and Access

Health plans need to reach and engage members to drive outcomes. Where is HealthEdge investing to make that happen?

One thing that is absolutely critical to driving outcomes is activating your member population. If health plans can’t move the needle on how members are progressing through their health journey, the plan is going to struggle to demonstrate meaningful impact.

We’re significantly expanding our HealthEdge Wellframe™ solution, moving it beyond purely an app experience to include web, text-based, and email-based engagement, which is what members have come to expect in today’s digital age. We’re opening more front doors, more channels for members to come in and engage with their care team and become active participants in their own care plan.

Beyond that, we’re also focused on helping our customers and their clinical teams do more with their existing resources by targeting specific, high-impact use cases. In care management, we’ve taken a build-and-partner approach—developing features that drive operational efficiency while also forming strategic partnerships that extend capabilities. For care management, we view artificial intelligence (AI) as an augmenter and enhancer, empowering teams to work smarter rather than replacing their expertise. But there’s still opportunity for AI to take on tasks and work fairly autonomously while still keeping a clinician in the loop.

Annual health assessment completion is a prime example of how AI-powered solutions can securely and transparently enhance member outreach at scale. Today, advanced AI tools can conduct telephonic conversations with members, always making clear that the interaction is assisted by AI, and ensuring privacy and data security remain paramount. This approach not only increases efficiency and reach but also builds member trust by keeping interactions transparent while enabling health plans to engage large populations effectively—far beyond what traditional person-based methods allow.

Ultimately, the effectiveness of these investments hinges on our ability to help health plans engage more members in meaningful ways. Expanding our reach is crucial; without it, achieving measurable improvements in outcomes remains out of reach for both our clients and those they serve.

Measuring Success: Just Start

Health plans are also under intense pressure to prove ROI. How should leaders think about measuring success when adopting Care Solutions within an ecosystem operating model?

My answer is simple: just get started.

Evaluate a use case you’re already familiar with. Work with a partner that brings strong technology, services, and a skilled, comprehensive team to the table. Let them demonstrate how they can deliver better results than your current approach.

Define what “better” looks like for your organization—it could mean higher engagement on your portal, more completed assessments, closing more care gaps, or any of the performance metrics health plans typically track, such as NCQA, HEDIS, or STAR ratings. Focus on one area and work with a partner to deliver measurable value. Once you see proven results, you can expand the program with confidence.

Success measurements should be tailored to what matters most to each health plan’s business strategy. The key advantage of our integrated ecosystem operating model is our ability to contractually guarantee outcomes, because we have control over the entire technology stack and operational process—not just isolated components.

The Future Operating Model

If we’re having this conversation three years from now, what will the most successful health plans be doing differently as a result of embracing an integrated ecosystem operating model?

We’re looking at a complete shift in how health plans operate. The traditional model relies on additional staff or incremental efficiencies—which doesn’t allow for true transformation. In the future, our integrated ecosystem operating model reduces the administrative operating burden, adds expertise and allows health plan staff to focus on high-value work.

Over time, payers continue to see improved outcomes and cost reductions and gain the ability to focus on bigger strategic goals. Partnering with organizations that track and deliver measurable outcome metrics frees up their time, budget, and headspace. They are able to really focus on how they want to compete, how they want to differentiate, and how they want to win. They can leverage resources and talent on their unique strategic priorities, whether that’s specific member populations, clinical specialties, or market differentiation.

The most successful health plans will have a fundamentally different operating model. These health plans will deploy resources toward activities that move the needle on their strategic objectives, rather than getting bogged down by operational tasks that can be performed more efficiently through specialized partners and advanced technology.

And I want to emphasize—this isn’t about eliminating jobs, downsizing or taking away opportunities. This is about removing the work people find most tedious and giving nurses and care managers their passion back for why they got into this profession in the first place.

Steps to Operational Transformation

For plans just starting this journey, what’s the best first step toward moving from point solutions to a true integrated ecosystem operating model?

Our Advisory Services team works with health plans to deliver a total cost of ownership assessment and to determine their cost drivers and areas for improvement. This exercise helps health plans consider a specific line of business use case as a starting point to demonstrate clear ROI and realize immediate administrative efficiencies.

Post-discharge follow-up, member enrollment, or assessment completion are great examples because the intervention-to-outcome relationship is well established and measurable.

Once you prove the value of the model with one line of business, expansion becomes much easier to justify and implement.

Leverage Seamless Data Sharing & Transparency with GuidingCare

What else do you want health plan leaders to know about this transformation?

First, interoperability and seamless data sharing set us apart in the industry. Our entire GuidingCare platform suite shares a unified data model and API framework enabling direct, real-time integration with health plan systems. Unlike solutions that rely on piecemeal acquisitions or fragile partnerships, HealthEdge delivers true interoperability out of the box. We can work directly in systems as the direct source of truth, with direct documentation and all the automations and intelligence built in. That greatly simplifies how these interactions and services are delivered.

Second, embracing an ecosystem operating model is not just an operational shift—it’s a true business model innovation. Health plans that adopt this approach are positioning themselves for sustained success in an increasingly competitive market and evolving contracting frameworks. By moving beyond traditional software acquisitions, health plans equip themselves with a differentiated, future-oriented model that accelerates sustainable growth and delivers long-term value.

Future-Proofing Health Plan Operations with AI-Powered Solutions

The market is changing rapidly. Existing cost and regulatory pressures aren’t going away, and new challenges emerge every day. The question for health plan leaders is whether they want to keep doing things the way they’ve always been done, or whether they’re ready to move beyond the familiar and proactively shape the future of their organization’s success.

Learn more about how your health plan can leverage AI-powered tools to enable strategic, scalable, and intelligent automation. Read the data sheet, Transforming Utilization Management with an AI-Powered Decision Intelligence Ecosystem.

About Bobby Sherwood

Bobby Sherwood is VP of Product Development at HealthEdge, where he leads strategic direction for the company’s cloud-based care management solutions and Business Process as a Service offerings. With deep expertise in healthcare technology and payer operations, Bobby works with health plans to transform care delivery models and drive measurable outcomes.

 

Part 1 of a 2-part series, where HealthEdge® Vice President of Product Development Bobby Sherwood discusses how a new operating model can transform care operations for health plans.

In this installment, we cover:

• What is an ecosystem operating model?
• Addressing workforce shortages with AI
• Redesigning care delivery to improve outcomes

In part 2, Sherwood shares more about measuring success with AI-powered tools, steps to achieving operational transformation, and seamless data sharing.

Nobody goes to nursing school to push paper.

What if care managers could reclaim time currently lost to authorization reviews and compliance chases? Imagine redirecting those hours from administration and back to what really matters: engaging members, improving outcomes, and transforming care through meaningful relationships.

That’s the fundamental shift happening in healthcare now. Health plans are under unprecedented pressure from rising medical costs, regulatory demands, and workforce shortages. And the traditional approach—like layering on additional software and hiring more staff—isn’t sustainable. Something has to change.

At HealthEdge®, we’re delivering a different approach: an ecosystem operating model. We combine next-generation, AI-enabled technology with services designed to lower total costs—and we build contractual accountability into the model.

We sat down with Bobby Sherwood, Vice President of Product Development, to explore how this operating model goes beyond traditional software, and why measurable outcomes are raising the bar.

Defining the New Standard: What is an Ecosystem Operating Model?

How do you define an ecosystem operating model? And how is it different from traditional business process outsourcing?

Traditional business process outsourcing is essentially labor cost arbitrage—taking existing processes and doing them cheaper, often through offshoring or team scaling. You’re doing the same work, just with different people.

Our ecosystem operating model is fundamentally different. We’re reimagining the entire process through technology to deliver transformational outcomes, not just cost savings. It’s about operational transformation, not just labor efficiency.

Here’s what makes our approach unique: we combine HealthEdge’s integrated solution suite with clinical expertise and AI-driven automation. But most importantly, we take accountability for actual care outcomes. We’re not just providing technology. We’re building toward taking responsibility for moving the needle on medical cost trends, member satisfaction, and clinical quality measures.

That accountability changes everything. We succeed only if you succeed. The partnership dynamic is completely different from a traditional vendor relationship.

Supporting a Strategic Shift for Health Plans

HealthEdge is already delivering and optimizing an ecosystem operating model for health plan customers. What does that shift really mean for health plans, and why is now the right moment?

This evolution gives health plans unprecedented flexibility and choice within a full-stack, integrated ecosystem. Health plans can leverage delivery options to focus on which business metrics matter most for their specific strategies and do more with existing capabilities and resources.

Now is the right moment because the traditional vendor model has created fundamentally misaligned incentives. Historically, health plans license software through contracts that aren’t directly tied to member outcomes or total cost performance. While these tools can enable improvement, the commercial model itself isn’t structured around shared accountability for results.

Our ecosystem operating model reshapes legacy systems, and HealthEdge moves beyond vendor status to become a performance-aligned partner. This means our success is directly tied to delivering the specific clinical and financial outcomes that matter to each health plan. We’re accountable for results, not just software uptime. And frankly, the market is ready for this. Health plans are facing a perfect storm of pressures that’s making them more open to different arrangements.

Addressing Workforce Shortages with AI

Speaking of those pressures—rising medical costs, regulatory demands, workforce shortages—which one is forcing the biggest operational change right now?

If I had to pick one, it’s workforce shortages. Health plans can’t hire clinical staff fast enough to keep pace with member growth, stringent regulatory requirements, and the increasing complexity of care management.

This shortage is driving urgent interest in our approach. When you can’t solve the operational bottleneck by adding more people, you have to fundamentally reimagine how work gets done.

This is where AI technology within HealthEdge GuidingCare® becomes exceptionally powerful. Our system empowers health plans to address staffing shortages by automating routine tasks and enhancing clinical efficiency. Features like Automated Clinical Summaries and Intelligent Document Processing reduce administrative burdens, while Intelligent Care Guidance and Ambient Intelligence streamline decision-making and ensure documentation completeness. This allows health plans to redeploy clinical resources to high-impact activities like complex case management and member relationship building, improving care outcomes and operational efficiency.

Think about authorizations. Most nurses did not go into the profession to review paperwork. But authorizations take up so much time and budget because of the compliance burden. If we can demonstrate at scale that we’ll handle all of it, keep health plans compliant, guarantee the savings, and deliver the outcomes health plans need, nurses will gladly hand-off that work. Then health plans can shift those nurses to activities that truly impact the cost curve.

Redesigning Delivery for Measurable Impact

Historically, care management technology has focused on tools and workflows. How is HealthEdge’s Care Solutions approach different in terms of the outcomes it’s designed to deliver?

Traditional care management technology asks, “How can we make existing processes more efficient?” We ask, “What outcomes does the health plan need to achieve, and how do we redesign the entire care delivery model to get there?”

In traditional software models, vendors provide powerful tools that enable improvement, but responsibility for realizing the full value often rests primarily with the health plan. While renewals can reflect satisfaction over time, the commercial structure itself is typically based on access to technology rather than shared accountability for measurable outcomes.

When financial accountability is tied directly to outcomes, the dynamic changes entirely. You’re not just another vendor in their stack of dozens or hundreds of vendors. You become a true strategic partner, fully invested in your clients’ success, with mutually aligned incentives and shared accountability for real, measurable outcomes. This fundamentally transforms the engagement from a transactional relationship into a collaborative alliance built on trust, transparency, and joint achievement.

Our integrated platform doesn’t just digitize existing workflows. It reimagines them entirely. Instead of managing care through disconnected systems and manual processes, our platform enables seamless orchestration of member engagement, clinical interventions, and administrative processes. The result is measurable improvements in clinical quality, member experience, and cost management that we contractually guarantee.

Ready to learn more about reaching and engaging members, measuring the ROI of an ecosystem operating model, and how to get started?

Read part 2 of the blog here: Adopting an Ecosystem Operating Model and Measuring ROI.

About Bobby Sherwood

Bobby Sherwood is VP of Product Development at HealthEdge, where he leads strategic direction for the company’s cloud-based care management solutions and Business Process as a Service offerings. With deep expertise in healthcare technology and payer operations, Bobby works with health plans to transform care delivery models and drive measurable outcomes.