From Transactional AI To CFO-Level Intelligence

Hi and Happy Tuesday.

In our previous newsletters, we covered Q1 and the rollout of plug-in AI across the revenue cycle and using Q2 to make prior authorization, worklists, posting, and estimates operate as one flow.

This week, we are focused on Q3 and using RCM data to guide contracts, staffing, and investment decisions.

Once eligibility, scrubbing, and workflow automation are working reasonably well, the next question is simple:

Can finance leaders trust RCM data enough to steer strategy with it?

This is the level at which CFOs need enterprise-level intelligence rather than additional line-item reports.

1. System-wide denial root-cause analytics

The limitation:

Most reports stop at denial reason codes. They tell you WHAT happened, not WHY it happened or WHERE to fix it.

2-5% of net revenue lost to preventable denials

Today, many reports stop at denial reason codes. More advanced analytics connect data across scheduling, intake, documentation, coding, and billing to identify patterns such as:

• Sites with unusual failure rates for specific services

• Clinicians or service lines triggering medical-necessity denials

• Workflows where eligibility or authorization failures cluster

Q3 target 1:

Create a unified denial analytics view that ties payer codes back to operational root causes and quantifies dollars at risk by process step.

What this enables:

• Target process improvements where they'll have the biggest financial impact

• Identify training needs for specific teams or service lines

• Prioritize automation investments based on actual dollar leakage

2. Contract and payer-strategy modeling

The market reality

ACA marketplace plans have average denial rates of 19%. Commercial patient collections fell to 34.4% in 2024. Higher contract rates don't guarantee better margins.

~19% In-network denial rate for marketplace plans

34.4% Commercial collection rate drop

AI-driven scenario models are beginning to project how changes in payer mix, contract terms, and policy shifts will affect:

• Margin and net collection rate.

• Days in A/R and timing of cash.

• Staff requirements for denials and appeals.

Q3 target 2:

Build at least one contract model for an upcoming negotiation using your actual denial and collection data, not generic discount assumptions

What this enables:

• Enter contract negotiations with data on true net realization

• Model the operational cost of different payer relationships

• Make strategic decisions about which contracts to pursue or renegotiate

3. Integrated operational planning: RCM plus staffing and capacity

The disconnect

Finance plans volumes. Operations plan staffing. Revenue cycle tracks denials. These conversations happen separately until problems emerge.

71% Of hospitals use predictive AI for operations

66% Of CFOs expanding AI in the revenue cycle

The next evolution: Connect operational models to RCM metrics so staffing decisions account for expected denials, A/R aging, and write-offs.

• Projected volumes by service line linked to denial risk

• Required staffing and overtime tied to RCM workload

• Expected denial and collection performance under different scenarios

Q3 target 3:

Conduct a quarterly planning session where finance, operations, and revenue cycle review a shared model with volumes, staffing, and RCM performance integrated

What this enables:

• Make staffing decisions based on forecasted RCM workload

• Model how automation investments affect both operations and cash flow

• Align finance, operations, and RCM around common projection

In our next newsletter, we’ll give you a Q4 roadmap.

Dino Gane-Palmer