Precision Beyond Imaging

How a Texas Radiology Group Unlocked Additional $5M with Jindal Healthcare’s AI-Powered RCM

For this Texas-based multi-site radiology group, revenue cycle visibility was critical to combating inefficiencies resulting in an 18% denial rate and up to $300K in monthly revenue leaks.

Manual workflows, fragmented follow-ups, and reactive denial management had turned their collections into a guessing game—until Jindal Healthcare stepped in to give their revenue cycle the same precision their MRIs delivered every day.

With AI-powered RCM, the group identified the root causes behind their revenue leaks and re-engineered their revenue cycle to drive financial growth.

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RCM Precision with AI That Saw Through Every Claim

Inside the Case Study, You’ll Learn How Jindal Healthcare:
  • Streamlined eligibility verification, eliminating 90% of eligibility misses
  • Used contextual coding intelligence to flag errors before submission
  • Applied predictive analytics to prevent denials and revenue risks
  • Prioritized high-yield, high-impact claims for revenue realization
  • Created a self-learning RCM ecosystem that got better with each cycle
Unlock this case study to see how Jindal Healthcare leverages AI to diagnose and fix revenue cycle gaps and build lasting RCM resilience for healthcare providers.
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Impact at a Glance

36%

Increase in Monthly Collections

61%

Reduction in Denials

~15%

Improvement in Clean Claim Rate