On target: Healthcare compliance

This article is part of a series detailing meaningful proposed tax law changes. Read the previous article on key individual tax changes. 

The "Big Beautiful Bill" introduces a new savings vehicle for American families called the Trump Account. This novel provision has largely been overshadowed by other headline items including the SALT cap—and perhaps understandably so. This article will explain what these accounts are, how they would work, and their tax implications, so that if the legislation passes, you can be informed on whether they fit into your family's financial future.

What are Trump Accounts? 

Trump Accounts are specialized savings accounts designed for parents to invest in the futures of their children. 

• Account requirements: These accounts must be established as either a trust or custodial account before the child (the beneficiary) turns eight years old. 
• Contributions: Contributions are limited to cash only, with an annual cap of $5,000 per child (indexed for inflation), and can continue until the beneficiary reaches age 18. 
• Account limit: Each child is allowed only one Trump Account. If multiple accounts are created for the same beneficiary, only the first one qualifies as legitimate; any additional accounts are subject to a steep 100% excise tax on any income they generate. 
• Investment options: Funds can only be invested in stock of a regulated investment company that tracks a "well-established index" or a portfolio composed exclusively of US equities. While this restriction aims to promote long-term, stable growth through proven market indexes, it may limit flexibility compared to other savings options. 

Tax implications and distributions 

The rules governing distributions from Trump Accounts are somewhat intricate, but the overall tax benefits appear limited.

• Return of investment: Any portion of a distribution that represents a return of the original contributions is not subject to tax, which aligns with the fact that contributions are made with after-tax dollars and are not deductible. 
• Earnings: Any earnings or investment gains within the account are taxable to the beneficiary, regardless of how the funds are used. 
• Qualified purposes: If the funds are used for qualified purposes (defined as higher education expenses, a small business or farm loan taken out by the beneficiary, or a first-time home purchase), then the resulting gains are taxed at capital gains rates rather than as ordinary income. 
• Penalties: There is an additional 10% penalty on distributions to beneficiaries under the age of 31 which are not attributable to qualified expenses. 

Encouraging participation: The federal credit 

To encourage participation, the legislation includes a one-time federal credit of $1,000 for beneficiaries born between 2025 and 2028. 

• Automatic deposit: This credit is automatically deposited into a Trump Account unless the taxpayer opts out on their tax return. 
• IRS establishment: If no account has been created and no election out has been indicated, the IRS will establish an account on the beneficiary's behalf, following the processing of the parent's tax return. This automatic enrollment feature could jumpstart savings for many families, particularly those who might not otherwise take the initiative to open an account. 

Comparing Trump Accounts to other savings options 

While Trump Accounts offer some advantages, they have limitations when compared to existing savings plans like 529 plans. 

• Advantages: Trump Accounts offer potential rate arbitrage, tax deferral, and a degree of investment security due to regulatory constraints. They also expand the definition of qualified expenses to include small business loans and first-time home purchases. 
• Disadvantages: They fall short of the full tax-free growth and withdrawal benefits associated with 529 plans. Additionally, they lack some of the flexibility of 529 plans, such as the ability to repay student loans or roll over unused funds into a Roth IRA. The annual contribution cap of $5,000, even when adjusted for inflation, may also limit the long-term impact of these accounts compared to the more generous limits available under 529 plans. 

For families considering how best to invest in their children’s futures, it is important to weigh the novelty of Trump Accounts against the proven advantages of existing plans. If you have questions about your unique situation or would like more information on how these accounts would fit into your family’s financial picture, please do not hesitate to reach out to the BerryDunn Tax Team. We are here to help you navigate these important decisions and ensure a bright financial future for your loved ones.

The Medicare Payment Advisory Commission (MedPAC) recently released its June 2025 Report to Congress, highlighting critical developments in Medicare Advantage (MA). Several key insights emerged with important implications for home health and supplemental benefit policy. 

Home health utilization patterns 

While overall home health use was similar between Medicare Advantage (8.3%) and Fee-for-Service (FFS) beneficiaries (8.6%), MA enrollees were 3.2 percentage points more likely to use home health following hospital discharge. This trend suggests that MA plans may be encouraging the use of home health as a lower-cost alternative to skilled nursing facility (SNF) care. 

Even more notably, MA enrollees received fewer visits on average—approximately 18 visits per year compared to 20 visits for FFS beneficiaries, representing an 11% reduction in service intensity. This occurred even when care was delivered by the same home health agencies, indicating plan-driven differences in utilization management. 

Supplemental benefits and transparency gaps 

Supplemental benefits—such as transportation, groceries, and fitness programs—remain a major draw for MA enrollment. In 2025, MA plans are projected to receive approximately $86 billion in Medicare rebates to fund these benefits, a dramatic rise from $21 billion in 2018. 

However, despite the scale of these investments, critical gaps remain in transparency and accountability. There is limited data on: 

• How frequently these benefits are used 
• How much plans actually spend per benefit  
• Whether they improve health outcomes 

As scrutiny over MA payment intensifies, the lack of reliable data prevents a meaningful assessment of value and impact. Without improved oversight and reporting, policymakers and stakeholders cannot determine whether MA plans are delivering better care or simply redistributing federal funds without measurable benefit. 

Implications for home health 

In the home health sector, providers continue to struggle with constrained reimbursement from MA plans, even as demand for post-acute care alternatives rises. To ensure that MA spending leads to meaningful improvements in care, greater transparency is urgently needed—both in how dollars are allocated and in whether they drive measurable health outcomes. Only with clearer insight into MA spending can we ensure investments are made in areas that deliver true value to patients and the healthcare system. 

BerryDunn’s home health and hospice team is comprised of respected industry leaders and professionals who have dedicated their careers to advancing patient care and navigating core challenges. We partner with clients on a variety of financial, outsourced, and consulting services. Learn more about our team and services.    

As artificial intelligence (AI) becomes increasingly woven into nonprofit operations, boards are stepping into a new and critical role. Traditionally focused on mission oversight and fiscal responsibility, today's boards must also shape how AI is introduced, governed, and aligned with the organization’s values. Below are the seven most important actions a board can take to ensure responsible and strategic AI implementation. 

1. Build board-level AI fluency  

To offer meaningful oversight, board members must first understand the terrain. That means going beyond buzzwords to grasp AI’s potential and pitfalls in a nonprofit context—especially its ethical implications, financial impact, and accessibility concerns. 

Boards should: 

• Encourage ongoing education through trainings, industry briefings, or podcasts 
• Appoint an AI lead or champion to coordinate learning 
• Create space for dialogue between board and staff on emerging AI use cases 

2. Articulate a mission-driven AI vision  

Boards help define the “why” behind AI adoption. A strong vision ensures that tech decisions enhance the mission—not distract from it. 

This vision should: 

• Align AI use with organizational values and goals 
• Clearly state which uses are appropriate or off-limits 
• Address equity, inclusion, and accessibility for staff and stakeholders 

3. Establish policies and oversight structures  

Governance must evolve alongside innovation. Whether through an AI subcommittee or embedded into existing ones, boards should define oversight mechanisms early. 

Key actions: 

• Develop policies that address privacy, accountability, and ethical standards 
• Work with leadership to implement those policies organization-wide 
• Determine how and when AI performance and risks are reported to the board 

4. Invest in readiness across the organization  

AI implementation requires buy-in, training, and trust. Boards can champion a culture of learning that empowers both staff and leadership. 

That includes: 

• Encouraging staff-wide AI literacy, not just executive-level understanding 
• Supporting leaders in preparing their teams for workflow changes 
• Framing AI as a tool for empowerment, not displacement 

5. Prioritize responsible resource allocation  

AI can be expensive and time-consuming to deploy. Boards with financial oversight should evaluate whether investments are sustainable and impact-driven. 

Questions to ask: 

• What specific problems will this AI tool help solve? 
• How will outcomes be measured? 
• Are there grant opportunities or partnerships to offset costs? 

6. Promote transparency and communication  

Successful AI implementation thrives on trust. Boards can support transparency by encouraging open communication with internal teams and external stakeholders. 

Consider: 

• Creating dashboards or reports that track AI performance and risks 
• Soliciting feedback from staff and community members 
• Sharing learnings and ethical commitments publicly, when appropriate 

7. Extend impact to the community  

Nonprofits don't just implement technology—they model inclusive access to it. Boards can advocate for ways AI can serve not only the organization but the broader population. 

Ideas include: 

• Supporting community-based AI training or literacy initiatives 
• Partnering with peer nonprofits to share resources or lessons learned 
• Ensuring AI solutions serve marginalized and underrepresented groups 

Looking forward  

AI implementation is a journey, not a quick fix. Nonprofit boards play a critical role in making sure this journey is rooted in strategy, equity, and mission. With the right vision and structure, AI can become a powerful ally in expanding impact—and the board can be the compass that keeps it on course. 

BerryDunn’s nonprofit tax team works exclusively with tax-exempt organizations throughout New England and beyond. We understand and embrace the unique challenges faced by nonprofits—and recognize the vital importance of putting the mission first. Our team has deep expertise in partnering with nonprofits to develop strategies for success. Learn more about our team and services. 

Newly appointed to lead BerryDunn’s Healthcare Practice Group, Lisa Trundy-Whitten is closely attuned to the healthcare industry. From challenges faced by healthcare organizations to the solutions BerryDunn’s experts can provide, Lisa shares her vision for the team as she takes the helm, as well as thoughtful insights for today’s healthcare leaders. 

Today’s healthcare leaders face historic challenges that require innovative strategies to successfully navigate. From the impact of the proposed $880 billion cuts to Medicaid to redefining the model for employing primary care providers to be more sustainable for health systems, there is a path forward.  

It’s not surprising that BerryDunn’s fastest-growing services are those being shaped by economic uncertainty, regulatory compliance, financial strategy, IT strategy, and sustainability initiatives. As I consider the healthcare landscape today, there are a couple of key areas I’d like to highlight. First, the regulatory changes at the federal level are putting tremendous pressure on healthcare organizations, and second, quickly advancing technology is forcing the industry to evolve rapidly to remain sustainable.  

Impacts of federal regulatory changes 

Proposed shifts in federal policies and laws are creating uncertainty around reimbursement and regulatory compliance. At the top of the list are the Medicaid cuts in the "One Big Beautiful Bill Act,” which passed the US House of Representatives in May and is now up for consideration in the Senate, and the impact of possible changes to provider taxes.  

Major consequences of these possible Medicaid cuts include reimbursement and financial sustainability for organizations. My team and I know you have concerns. You’re wondering how you will make up for the potential lost reimbursement and whether entire programs will need to be eliminated.  

One way BerryDunn is staying ahead of the regulatory curve to support you is by closely monitoring regulatory changes, determining possible impacts, and developing strategies to inform and support our clients.  

Embracing and adapting to new technology 

One challenge with technology is determining how to best leverage it to improve accuracy and efficiency. Uncovering ways to align new tools with existing resources, the ethical use of technology, and governance models all come to mind when I think of the effects of technology on the healthcare industry. AI is one such tool that is constantly emerging and is prompting organizations to seek ways to advantageously employ it.  

Healthcare organizations like yours are working through how to supplement the workforce with technology to create a positive outcome. Technology needs to be integrated in a way that reduces the burden instead of adding to it.  

Our team has professionals skilled at strategic IT analysis and change management. We can assist with IT consulting to guide you on technology planning, EHR, and other system selections for your organization. We have expert advisors who can collaborate with you on the latest technology to help optimize your operations, including AI. 

Our Healthcare Practice Group  

My vision for our Healthcare Practice Group is to continually elevate our team and how we work with—and for—our valued clients. We want to help you integrate financial strategy and innovation to support operations and thrive in a climate of rapid change. To quote Tammy Brunetti, my predecessor in this role, "We are Better Together.”  

BerryDunn has an incredibly full breadth of services, and our team works across healthcare practices to provide a full complement of services. Considering our firm’s early roots in healthcare, we take tremendous pride in being large enough to provide a depth of resources but small and personal enough that we can provide services that fit your unique needs.  

I urge you to learn more about our services and team. We look forward to working with you to create the innovative solutions you require in today’s ever-changing climate. 

Best, 

Lisa Trundy-Whitten 

Read this if you are a healthcare financial leader, such as a CFO, revenue cycle executive, HIM director, or compliance professional.

Healthcare providers face increasing administrative and financial pressure due to the high volume and complexity of payer claim denials. Artificial Intelligence (AI) offers healthcare finance professionals powerful tools to shift denial management from reactive to proactive, significantly enhancing operational efficiency and financial performance. This article explores the use of AI technologies in preventing and managing denials, outlines an implementation strategy, presents case studies, and discusses challenges and future trends.  

Leveraging AI for claim denial management 

Claim denials continue to erode hospital and physician practice margins. According to HFMA and the AMA, denial rates can exceed 10% of submitted claims, with manual rework costing upwards of $25 per denial. The transition to value-based care and payer policy complexity make it difficult to maintain clean claims without significant investments in administrative labor. 

AI, including Machine Learning (ML), Natural Language Processing (NLP), Robotic Process Automation (RPA), and predictive analytics, offers finance leaders scalable, data-driven solutions to mitigate these risks. Leveraging AI across the revenue cycle enables better first-pass yield, reduces days in A/R, and drives sustainable improvements in net revenue. 

Denial landscape and financial implications  

Denials can be broadly placed into two categories:  

• Hard denials are typically irreversible and often result from issues such as contractual non-coverage. 

• Soft denials are potentially recoverable through resubmission or appeals.  

Understanding the nature of these denials is critical for devising effective mitigation strategies. 

What to consider for denials prevention: 

• A lack of prior authorization 

• Medical necessity disputes 

• Eligibility or benefit mismatches 

• Documentation or coding errors 

• Timely filing issues 

These denials often result in a 3–5% reduction in potential revenue—a significant financial impact for large health systems. Moreover, the manual effort required to rework and resubmit denied claims increases the cost-to-collect and diverts valuable resources from more strategic tasks. The resulting financial strain and workflow inefficiencies ultimately affect patient satisfaction and organizational sustainability. 

AI in revenue cycle management 

AI technologies enable healthcare organizations to optimize their revenue cycle operations through automation and intelligence.  

• Machine learning models: Trained on historical denial data, they can predict the likelihood of future denials and suggest interventions to avoid them. By proactively identifying high-risk claims, organizations can reduce rejection rates before claims are even submitted. An example is a decision tree classifier used to predict whether a hospital claim will be denied or approved. 

• Natural Language Processing tools: Play a pivotal role in understanding and extracting value from unstructured data sources such as EHRs, claim notes, and Explanations of Benefits (EOBs). They can identify missing or inconsistent information, automate appeal letter generation, and improve overall documentation quality. An example of NLP is a tool that extracts diagnoses, medications, and procedures from unstructured clinical notes to support accurate coding and streamline billing workflows. 

• Robotic Process Automation: Complements AI by handling repetitive, rule-based tasks such as eligibility verification, claim submission, and payer portal interactions. This frees up human resources for more complex and judgment-based activities. An example of an RPA is a bot that automatically retrieves claim status updates from payer portals and inputs the results into the billing system. 

• Predictive analytics tools: Offer powerful dashboards and forecasting capabilities, helping revenue cycle leaders identify trends, prioritize improvement initiatives, and continuously monitor performance metrics. An example of this tool is a model that analyzes historical claim data to forecast which submitted claims are most likely to be denied. 

Strategic benefits for finance executives 

AI adoption offers a multifaceted return on investment for healthcare finance executives. One of the most direct benefits is revenue enhancement through reduced denial-related leakage. By identifying and addressing risks before claims are submitted, organizations can significantly increase their clean claim rates. 

Operational productivity is also improved. Staff previously tasked with manual denial follow-up can be reallocated to higher-value roles, such as analytics or payer negotiation. This shift not only improves morale but also increases efficiency. 

In terms of compliance, AI helps organizations stay audit-ready by flagging inconsistencies in documentation and coding that may trigger payer audits or regulatory scrutiny. Furthermore, fewer denials and faster resolution cycles contribute to improved cash flow and reduced accounts receivable aging—key metrics for any finance leader. 

Implementation roadmap  

A successful AI implementation begins with defining clear, ROI-based goals. Finance leaders should align projects with measurable KPIs such as denial rate reduction, net revenue uplift, or staffing efficiency improvements. These goals serve as the foundation for all subsequent decision-making. 

Data readiness is a crucial prerequisite. Effective AI models require clean, structured, and integrated clinical and financial data. Organizations must assess their data infrastructure and invest in necessary improvements to ensure a successful deployment. Piloting the AI solution in specific payer segments or service lines allows for early value demonstration and helps build internal support. Positive results from these pilots can then inform a broader rollout strategy. 

Vendor selection should be driven by a thorough evaluation process, focusing on healthcare-specific experience, integration capabilities, and the vendor’s ability to maintain a comprehensive payer rule library. Equally important is preparing the organization for change. Successful adoption depends on cross-functional buy-in, robust training programs, and transparent communication about the benefits of AI. 

Case examples 

Several healthcare organizations have demonstrated the transformative potential of AI in denial management. Here are a few examples: 

• A 900-bed hospital implemented AI-based denial prediction models integrated with its Epic system. The result was a 40% reduction in manual claim rework and an increase in the clean claim rate to 94%. 

• A 400-provider medical services organization deployed NLP-enhanced Clinical Documentation Improvement (CDI) tools. This led to better capture of Hierarchical Condition Categories (HCCs) and a marked decrease in documentation-related payer inquiries. 

• A large, multi-state health system leveraged predictive analytics to identify and address root causes of denials across departments. By retraining staff based on data-driven insights, the system achieved a 33% year-over-year decrease in denials and gained an $8 million boost in net revenue. 

Challenges and mitigation strategies 

Despite the promise of AI, implementation comes with challenges. One major obstacle is the presence of data silos that limit the effectiveness of AI models. Integrating clinical, financial, and administrative systems is essential to create a unified view of the patient and claim lifecycle. 

Another concern is model bias and accuracy. AI tools must be regularly validated and adjusted to ensure their predictions remain reliable and do not inadvertently reinforce systemic issues. Overfitting and underfitting can both lead to misleading outputs if not properly managed. 

Regulatory compliance must also be prioritized. Organizations should only engage with HIPAA-compliant vendors who implement strong data protection measures. Moreover, staff should be trained on the appropriate use of AI outputs to prevent misuse or misinterpretation. 

Cultural resistance can slow or derail implementation. It is important to position AI not as a replacement for human expertise but as a tool that augments and enhances decision-making. Early wins, peer testimonials, and leadership support can help build momentum and buy-in. 

The future: AI as an RCM standard 

The future of revenue cycle management lies in the widespread adoption of AI tools as standard practice. Emerging technologies such as Explainable AI (XAI) will provide transparency into how decisions are made, making it easier to comply with audits and build trust with clinicians and payers. 

Federated learning is another promising development, enabling healthcare organizations to train AI models collaboratively without sharing sensitive patient data. This approach enhances model performance while preserving privacy. 

Real-time denial adjudication engines represent the next frontier, offering the ability to detect and resolve issues as claims are being prepared before submission. Such capabilities will transform denial management from a reactive function into a proactive, dynamic process embedded across the revenue cycle. 

AI: A strategic imperative 

AI adoption is no longer experimental—it's essential. Finance leaders must lead cross-functional efforts to deploy AI solutions that streamline operations, protect margins, and improve payer-provider collaboration. When implemented strategically, AI transforms denial management from a reactive cost center into a predictive, revenue-generating function. 

The future success of healthcare organizations depends on their ability to adapt to evolving reimbursement models, manage cost pressures, and improve data governance. AI serves as a strategic asset in achieving these objectives. As the industry embraces more digital health tools, those who proactively integrate AI into their revenue cycle operations will emerge as leaders, better equipped to deliver financial stability and enhance patient-centered care. In the end, the organizations that view AI not just as a technology but as a business imperative will be best positioned to thrive in the next era of healthcare delivery. 

BerryDunn’s revenue cycle consultants engage with your healthcare organization to objectively review existing processes and develop actionable strategies for short- and long-term performance improvement. Learn more about our team and services.