5 Ways the Right AI Can Transform Your Bundled Payments Program Especially for CMS TEAM

As health systems prepare for CMS TEAM and other value-based models, leaders are facing a familiar challenge: large datasets, tight timelines, and pressure to reduce variation while protecting outcomes. Artificial intelligence can help.

Used responsibly, AI provides a clearer view of where financial and clinical opportunities exist across an episode. It simplifies complex data, predicts risk earlier, and brings powerful analytics to teams that may not have dedicated data science resources.

This guide explores five practical ways AI can strengthen a bundled payments program and support better decision-making for surgeons, care teams, and administrators.

Why AI Matters for Bundled Payments

Bundled payment success depends on understanding how patient characteristics, care decisions, and operational patterns influence cost and outcomes. Historically, hospitals have relied on analysts to piece these insights together from claims files, EHR exports, spreadsheets, and manual reviews. That approach is slow and difficult to scale.

AI offers a different path. Modern platforms can:

• Process large claims and EHR datasets in seconds
• Identify cost and outcome drivers with far greater precision
• Predict episode cost risk early in the care journey
• Flag patients likely to experience complications or readmissions
• Give non-analysts an intuitive way to explore performance questions with natural language queries

These capabilities matter even more for programs like CMS TEAM, where hospitals will take accountability for cost and quality on every Medicare surgical episode. With margins tight and variation still widespread across procedures, AI can provide the visibility leaders need to prepare.

Before diving into specific use cases, it is worth noting that not all AI tools are created equal. Hospitals will need to validate accuracy and avoid immature products that generate unreliable guidance. When implemented with caution and clinical oversight, however, AI can be a powerful force for improvement.

Let's take a look at how AI can transform your bundled payments program.

1. AI Surfaces Insights Hidden in Large and Complex Datasets

Bundled payment analytics require teams to examine claims, EHR data, supply documentation, order sets, implant lists, notes, timestamps, and post-acute care patterns. Most organizations have this information, but it lives across multiple systems and formats. Pulling it together usually demands weeks of preparation and manual work.

AI can complete these steps in seconds. By processing structured and unstructured data at once, it shows clinicians and administrators what is actually driving episode performance.

Faster Access to Root-Cause Drivers

Instead of waiting for a quarterly analyst report, care teams can see trends almost immediately. For example:

• Which surgeons have the largest variation in length of stay
• Whether specific implant combinations are tied to higher episode costs
• Which comorbidities consistently predict longer recovery or higher readmissions
• Where post-acute care patterns differ across sites

Because AI is designed to summarize patterns quickly, users can explore multiple questions during a single meeting. This allows decisions to move at the same pace as operations.

Better Visibility for Busy Leaders

Hospital executives and physician leaders often have limited time. AI gives them quick access to reliable summaries without forcing them to sort through spreadsheets or raw claims files. This clarity is especially important as systems prepare for CMS TEAM, where decisions must be informed by episode-level data rather than anecdotal experience.

2. AI Predicts Episode Cost Risk at Scheduling or Admission

One of the most valuable roles AI can play in bundled payments is early cost prediction. Traditional models rely on retrospective data, which limits proactive planning. AI models can analyze patient histories, comorbidities, functional status, social factors, and care patterns to generate a cost estimate at the start of the episode.

Identifying High-Risk Episodes Before Surgery

AI can help hospitals:

• Forecast total episode cost before the patient enters the operating room
• Assign risk profiles to guide care coordination and resource allocation
• Identify candidates who may benefit from enhanced prehabilitation or closer follow-up
• Reduce avoidable variation early in the care journey

Early prediction aligns directly with the goals of CMS TEAM. If hospitals understand expected costs before the episode begins, they can set appropriate plans, manage resources more effectively, and improve financial predictability.

Planning Support for Care Teams

When surgeons and care coordinators know a patient is likely to need additional support, they can adjust discharge planning, schedule timely therapy, and discuss expectations more clearly. Early clarity helps reduce readmissions, length of stay, and delays in recovery that often increase total cost.

3. AI Flags Patients at High Risk for Complications or Readmissions

Complications and readmissions are two of the biggest drivers of high costs within bundled payments. AI can analyze detailed variables that are often missed in manual reviews and flag patients who may require extra attention.

More Accurate Patient Segmentation

Machine learning models evaluate thousands of data points to identify patterns linked to elevated risk. These signals might include:

• Comorbidity clusters
• Prior utilization patterns
• Social determinants that influence recovery
• Medication histories
• Subtle combinations of lab results or vital sign changes

This level of detail helps teams anticipate issues that may not be obvious through traditional risk-stratification tools.

Supporting Targeted Interventions

Once high-risk patients are identified, care teams can direct resources where they matter most. Examples include:

• Scheduling proactive follow-up appointments
• Assigning a care coordinator for closer oversight
• Initiating early physical therapy
• Adjusting pain management plans
• Connecting patients with social support services

These targeted interventions help reduce complications and readmissions, which directly improves both clinical results and financial performance under CMS TEAM.

4. AI Makes Advanced Analytics Accessible to Non-Analysts

One of the most important benefits of AI is its ability to democratize data. Surgeons, nurses, administrators, and service line leaders often want to understand performance drivers but lack the time or technical training to run complex analyses.

With natural language interfaces, users can ask questions in plain English and get answers in seconds.

Examples of Natural Language Queries

Users might ask:

• "What are the top causes of high cost variation in our joint replacement episodes?"
• "Which post-acute care providers have the highest readmission rates?"
• "How do length-of-stay patterns differ between facilities?"
• "What factors predict extended recovery for shoulder arthroplasty?"

Instead of waiting for an analyst to extract and prepare a report, clinicians can explore these questions directly during committee meetings or case reviews.

Closing the Gap Between Data and Action

When teams can interact with data more easily, they make decisions faster. Service line leaders can confirm whether a problem exists before building an improvement plan. Surgeons can compare their outcomes to benchmarks and ask more informed questions. Executives can get clear summaries during strategic planning sessions without requesting multiple follow-up reports.

This accessibility is key for CMS TEAM, where hospitals will need consistent insight across all surgical episodes, not just a few high-volume service lines.

5. AI Supports Performance Improvement Across the Entire Episode

Success under bundled payments depends on many touchpoints: preoperative optimization, surgical practice patterns, perioperative processes, discharge planning, and post-acute care utilization. AI can help teams monitor each phase and identify where improvement will deliver the strongest return.

Identifying High-Value Opportunities

AI can show hospitals:

• Which procedures have the greatest variation in episode cost
• Where length-of-stay patterns differ between surgeons or facilities
• Which post-acute care pathways lead to better or worse results
• How implant choices affect total cost across different patient profiles
• Which complication types have the greatest financial impact

This level of clarity helps systems prioritize projects and allocate resources where they can make the largest difference.

Supporting Collaboration Between Hospitals and Physicians

Bundled payments require close coordination between physicians and hospital leaders. AI provides a shared source of truth that both parties can trust. Instead of debating whose data is correct, teams can focus on actionable strategies.

When combined with structured improvement processes, AI becomes a foundation for:

• Standardizing pathways
• Evaluating supply variation
• Supporting gainsharing conversations
• Guiding resource alignment across acute and post-acute settings

The result is a more collaborative environment that benefits patients, clinicians, and hospital operations.

Guarding Against Immature AI Tools

While AI has tremendous potential, hospitals must approach implementation with caution. The market is filled with new tools, many of which promise far more than they can deliver. Leaders should validate:

• Model accuracy and transparency
• Data sources used for training
• Clinical relevance of recommendations
• Ability to handle incomplete or imperfect data
• Alignment with regulatory and compliance standards

The goal is not to adopt AI for its own sake. Instead, hospitals should look for platforms that have proven results and deliver reliable insights grounded in evidence and supported by clear governance.

Bringing It All Together

AI is not a magic solution, but it is a practical tool that helps hospitals succeed in an increasingly complex environment. In bundled payments, the ability to extract insights quickly, predict risk early, and support decision-makers at every level can make a meaningful difference in both financial and clinical outcomes.

For organizations preparing for CMS TEAM, these capabilities are more than useful. They are strategic. AI gives leaders the visibility they need to reduce variation, coordinate care more effectively, and build a culture of continuous improvement.

When paired with careful validation and thoughtful implementation, AI can transform how hospitals approach bundled payments, helping teams navigate change with better information and stronger alignment.

To learn more about how Avant-garde Health's AI driven platform can support your readiness for TEAM, contact us today!