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Every step of the billable journey through a health system generates data to be analyzed and optimized, from net collection rates to denial rates and more. At a time where financial pressures on health systems continue to increase, maintaining a set of robust and actionable analytics is absolutely necessary for RCM leaders.

We last covered revenue intelligence about a year ago (and it’s still worth a read). This year, we’re revisiting the space with a closer look at the range of approaches to tracking those revenue cycle metrics and what’s new in the space, including AI agents and several more specialized AI-forward solutions. 

(Note: As in our previous coverage, we’re not focusing on either end-to-end RCM systems or point solutions that may include analytics; for us, revenue intelligence refers to intelligence-first solutions.)

How RCM leaders measure and understand performance

The way revenue cycle metrics are categorized mirrors the way the revenue cycle itself is typically understood: 

• Front-end: Many denials originate before the patient encounter due to prior auth issues or incorrect insurance verification. Metrics include authorization approval rates, pre-registration rates, and insurance verification accuracy.
  

• Mid-cycle: Operational speed and accuracy are key, especially DNFB (days in discharged not final billed) and coding accuracy. These metrics highlight billing delays and cash flow bottlenecks.
  

• Back-end: Includes clean claim rates, initial denial rates, appeal success, and net collection rates.
  

Historically, many of these metrics have been siloed across disparate systems or tracked manually, delaying insight and reducing agility. Best-in-class results require a data infrastructure that aggregates and visualizes information across platforms. But dashboarding alone isn’t enough; what makes revenue intelligence valuable is the ability to surface not just what changed, but why.

Differing Approaches

In general, teams really have four options for approaching  revenue intelligence.

1. Rely on the EHR built-in reporting: Epic, Oracle Cerner, and athenahealth all offer some level of analytics or reporting built-in. This may be the best option when consistency, ease-of-use, rapid implementation, and standard reporting without needing significant customization are the priority.
   

2. Build in-house: Many health systems opt to export data from their EHR and billing systems and build custom dashboards and reporting with traditional business intelligence platforms like PowerBI or Tableau. For those with strong internal data teams, this may be a viable option.
   

3. Mix-and-match with point solutions: Most RCM vendors, from prior authorization to billing and collection, will offer some level of reporting. The challenge is the lack of integration across sources. Each solution will provide deep analytics on their portion of the workflow, but will not typically cover the end-to-end.
   

4. Revenue intelligence specialized solutions: These specialized solutions generally allow for integration, standard dashboards and reporting, and an additional analytics layer on-top, allowing for predictive analytics or improved forecasting. As covered last year, at their best, these solutions give deep recommendations on top of reporting.
   

Innovation in revenue intelligence

In the past year, the most meaningful evolution in revenue intelligence has been the emergence of downstream AI agents that act on the insights surfaced by these platforms. For example:

• Adonis extended its core analytics and denials management platform with Adonis AI Agents, intended to recommend and take action on identified opportunities.
  

• VisiQuate launched Ana, a configurable agent designed to proactively identify metrics, anomalies, and red flags tailored to the user’s role. Then, if desired, the broader agent suite can automatically take next actions on the most critical items. 
  

This marks an important shift. Historically, while even the most advanced intelligence solutions might be better at predictive analytics or proactive surfacing of insights, the end user still needed to interpret and take action. With agents, those steps become automated or semi-automated, accelerating resolution and impact.

We’re also seeing the emergence of adjacent financial intelligence tools, solutions that don’t focus as directly on traditional revenue metrics, but instead use the same AI-forward approaches to a wider set of pain points:

• ArcheHealth’s first product targets the healthcare supply chain, using AI to identify excess spend and optimize procurement decisions.
  

• Celery proactively flags payroll and finance errors, aiming to prevent leakage outside the core RCM domain.
  

As RCM continues to be a leading area for enterprise AI adoption, the question for health system leaders is no longer whether to invest in revenue intelligence, but how to ensure those investments drive measurable improvement. Whether through internal builds, dedicated platforms, or embedded agents, the next wave of value will come from turning insight into timely, targeted action.