Healthcare AI: Beyond the Hype—How U.S. Leaders Can Implement for Real Impact

What Executives Need to Know from the Front Lines with Rick Moreland, MBA, CEO, Modality Global Advisors

In the last decade, Artificial Intelligence (AI) has transitioned from a tech buzzword to a boardroom necessity for healthcare organizations striving to stay competitive. But even as headlines tout AI’s promise, leaders are asking: What are the real, bottom-line benefits? How do we separate AI’s transformative potential from the marketing noise? And—crucially—what are the practical steps to implementation that safeguard patient privacy, optimize financial performance, and actually improve outcomes?

In this episode recap, we translate a candid conversation with Rick Moreland, MBA, CEO at Modality Global Advisors, into clear, actionable insights. Drawing on Moreland’s 25+ years in healthcare leadership—from the operating room to the C-suite—this article breaks down why AI matters now, where it delivers true value, the pitfalls to avoid, and what it really takes to make AI work for your hospital or health system.

Whether you’re a CEO, CFO, clinical leader, or innovation executive, you’ll find this guide strips away the hype, puts the patient at the center, and delivers the must-knows for AI strategy in U.S. healthcare today.

How Does AI Actually Benefit Healthcare Organizations?

Short Answer: AI delivers three core benefits: personalized care, operational optimization, and improved patient engagement. The real value comes from practical, targeted deployments—not generic tech promises.

The promise of AI in healthcare is real, but it’s easy to get lost in the sea of exaggerated claims. According to Rick Moreland, MBA, who has spent decades leading and optimizing clinical and operational teams, the actual benefits are already here if you know where to look and how to leverage them.

Moreland’s perspective:

“We’ve been using AI in several spaces already. At the most granular level, AI can personalize treatment plans by analyzing patient data in the EHR and generating customized care pathways tailored to each individual.”

Top three value areas include:

• Personalized Treatment: AI analyzes vast datasets (EHR, lab results, genomics) to tailor care.
• Drug Discovery and Decision Support: Especially in cancer care, AI speeds up identification of optimal drug regimens by parsing clinical data and biosimilar options.
• Patient Engagement: Tools like chatbots and virtual assistants now offer 24/7 support for patient questions, manage appointments, and increase overall satisfaction, as Moreland notes.

This isn’t hype: it’s already improving both patient outcomes and the provider experience.

Is Healthcare AI All Hype? How to Separate Reality from Marketing

Short Answer: Most AI promises are inflated, but practical gains exist—if you address readiness, data quality, and implement with intention.

Skepticism among health system executives is not only warranted, it’s necessary. “A lot of people are scared that it’s going to take jobs,” Moreland acknowledges, “but we’ve been using it in several of the spaces that we’ll cover today.” The reality? AI is a tool—not a replacement for clinical expertise.

What’s real and what’s hype?

• Real: AI as an “always-on helper to our analysts”—automating low-level data aggregation and surfacing optimization suggestions.
• Hype: “AI will replace physicians.” Not happening. Instead, “It is a support to the physician, not a replacement. It can help physicians detect things—like cancers—earlier, but it doesn’t make the diagnosis for them.”

Executives should focus on use cases with tangible ROI. As Moreland puts it, “AI can help optimize financial performance and even make projections, but it’s only as valuable as the data and workflows you prepare for it.”

What Keeps CFOs Up at Night? The Financial Realities of AI in U.S. Healthcare

Short Answer: Cost, data quality, and unclear ROI are primary financial concerns. Successful AI deployment starts with data readiness and a defined business case.

With memories of costly EHR implementations still fresh, many CFOs and finance leaders worry about sinking millions into AI with little to show. Moreland is direct about these concerns:

“A lot of our CFOs are especially worried—are we going to sink millions into AI and then have it backfire on us?”

Here’s how leaders should approach the financial side of AI:

1. Data First: “You have to be prepared and have your data prepared to implement [AI] correctly.”
2. AI as an Analyst Multiplier: For teams short on analysts, AI is a force multiplier—“It helps them keep up and even optimize their work.”
3. Financial Operations: AI can aggregate financial data, optimize billing and coding, and make actionable projections in real-time.

Key Takeaway: Don’t invest in AI before you clean up your data and define your business need.

How Should Leaders Address HIPAA, Privacy, and Security in AI?

Short Answer: Keep AI systems—and sensitive data—internal whenever possible. Rigorously vet third-party solutions for security protocols and HIPAA compliance.

Data privacy remains the defining risk of healthcare AI. Moreland’s approach is unequivocal:

“We make sure that wherever AI is running, it’s done within the organization—if it’s their cloud, it’s their cloud; if it’s their servers, it’s only on their servers. If it’s third parties, what are their guardrails? How well are they working?”

Three rules for protecting patient data in AI initiatives:

• Prefer Internal Deployments: Use internal clouds or servers for any AI handling PHI.
• Demand Security Proofs: Third-party vendors must show HIPAA and security certifications.
• Proactive Risk Assessment: “We try and keep AI working for the organization, within the organization, to help prevent breaches.”

Remember, the reputational and financial cost of a data breach can far outweigh any initial savings from external or “cheaper” AI services.

What AI Use Cases Deliver the Highest ROI in U.S. Healthcare?

Short Answer: Imaging, early disease detection, staffing optimization, and revenue cycle analytics are delivering measurable gains now.

Not all AI investments are created equal. According to Moreland, the highest-impact use cases right now include:

1. Imaging and Diagnostics:
“Having AI that works in the background to optimize imaging reads and help the physician—this does not replace them, but it can help with earlier detection, like lung cancer screening.”

2. Robotic and Minimally Invasive Surgery:
AI is “improving the precision of procedures—total joints are a great example—ultimately helping patient outcomes and reducing cost.”

3. Staffing Optimization:
Staffing ratios and shortages plague the industry. Moreland describes AI’s real-time impact:

“AI will help supervisors managing staffing, especially in real time—highlighting where there are opportunities, not just showing a dashboard, but actually predicting needs as they emerge.”

4. Revenue Cycle and Finance:
Predictive analytics are transforming claims, denials management, and regulatory compliance. “If your dashboard isn’t already using AI, it will be soon,” Moreland says.

Summary Table of High-ROI Use Cases:

How Do You Actually Implement AI in a Complex Health System?

Short Answer: Effective implementation requires deep discovery, stakeholder engagement, targeted use cases, and expert guidance—not just technology procurement.

Moreland is blunt: “It’s not as easy as signing up for a subscription on the internet.” The challenge lies in integrating new tools into existing workflows, data sources, and—most of all—people’s habits.

Best Practices for Implementation:

1. Begin with Stakeholder Discovery:
   “We spend time with key stakeholders—physicians, leaders, frontline staff—to understand what they’re trying to accomplish, their pain points, and goals.”
2. Customized Solutions, Not One-Size-Fits-All:
   Every organization’s needs and data are unique.
   “We create customized solutions—whether it’s quality, finance, or operations.”
3. Elbow-to-Elbow Support:
   Consultants should work closely with teams, ensuring solutions fit real workflows and deliver sustainable value.
4. Prioritize Change Management:
   AI adoption requires cultural buy-in and education, not just tech rollout. “Change is hard, but it’s easier when clinicians, process professionals, and business experts all work together.”

What About Data Interoperability, Access, and Regulatory Hurdles?

Short Answer: Addressing interoperability, data access, and regulatory compliance requires a multidisciplinary team—clinicians, process experts, and business strategists—plus persistent, organization-specific problem solving.

Interoperability is where most AI projects stall. Moreland’s firm tackles it with “a team of clinical experts and business experts to marry all of that together,” performing detailed process mapping and technology gap analysis.

Critical actions for overcoming these hurdles:

• Assess Existing Flows: Map out where data lives and how it moves through your system.
• Identify Gaps and Bottlenecks: Sometimes the fix is tech, other times it’s process or policy.
• Leverage Real-World Experience: “We help organizations who maybe lack resources, or have never done it before, or just haven’t had the opportunity to change.”

The takeaway: Multidisciplinary teams and stakeholder alignment are essential for making real progress on interoperability.

Why Do You Need Outside Experts for AI? Can’t Our IT Team Do This?

Short Answer: Outside experts provide cross-disciplinary knowledge, unbiased assessment, and deep implementation experience—factors most internal teams lack for complex, enterprise-wide AI deployments.

When asked why organizations need external partners like Modality Global Advisors, Moreland responds,

“We don’t know what we don’t know. That’s where we come in and help teams understand how AI can help them. We assess on the ground, create customized solutions, and work alongside staff to make it work.”

Advantages of outside experts:

• Unbiased evaluation and benchmarking
• Access to best practices and latest regulatory requirements
• Experience with technical, clinical, and financial integration
• Ability to scale and support change management

Simply put, the complexity and stakes are too high for most health systems to “DIY” their AI transformation.

What’s the First Step for Hospital Administrators Ready to Explore AI?

Short Answer: Begin with deep stakeholder engagement—listen before you buy or build.

“We spend time with key stakeholders to understand what they’re trying to accomplish and dive deep into their pain points. Without that understanding, it’s hard to analyze or make suggestions.”

Practical steps:

1. Gather Stakeholders: Include clinical, operational, financial, and IT leaders (and don’t forget frontline staff).
2. Define Problems, Not Just Wish Lists: What specific outcomes or bottlenecks matter most?
3. Start Small and Targeted: Pilot in one area before scaling across the enterprise.

This engagement often sets the stage for a more successful, less expensive AI journey.

What’s Next for Healthcare AI in the U.S.? Where Should Leaders Focus?

Short Answer: AI will become more regulated, more pervasive, and more strategic—requiring specialized leadership and investment to stay competitive.

Looking ahead, Moreland predicts three trends:

1. AI Isn’t Going Anywhere:
   “People like myself and our firm help educate healthcare facilities, take the fear out of AI, and give education and purpose to it.”
2. Growth and Pervasiveness:
   “AI is only going to grow in more and more areas—finance, operations, clinical care.”
3. Regulation and Scrutiny:
   “In the next two years, we’ll see more regulations and guardrails on how we use AI—especially in IT.”

The reality is, leading health systems—especially in highly competitive markets—are already investing heavily, sometimes even hiring C-suite leaders dedicated to AI strategy.

Final Takeaway: Don’t Wait, But Don’t Rush—Targeted AI Delivers the Advantage

The bottom line for U.S. healthcare leaders? AI isn’t a silver bullet, but it’s no longer optional for competitive, high-performing organizations. The winning strategy is deliberate: Start with stakeholder-driven problem definition, prioritize high-impact use cases (imaging, finance, staffing), safeguard data, and engage outside experts to guide implementation.

As Rick Moreland, MBA, CEO of Modality Global Advisors, reminds us, “AI is a helper—not a replacement—for our best people. When you target solutions, engage stakeholders, and commit to real change, the ROI and patient benefits are tangible.” The time for exploratory pilots and surface-level deployments is over. Make your move, but make it count.