Readers Write: Why Healthcare Providers Need AI That Thinks, Not Just Repeats

Why Healthcare Providers Need AI That Thinks, Not Just Repeats
By Jaideep Tandon

Jaideep Tandon, MS is CEO of Infinx.

For years, automation has been the go-to fix for revenue cycle inefficiencies. Healthcare providers rolled out robotic process automation (RPA) to handle tedious tasks like eligibility checks, claim submissions, and payment posting. It was a game-changer — until it wasn’t.

RPA works like a hyper-efficient intern. It’s great at following instructions, but completely lost when something unexpected happens. Need to reprocess a claim after a payer changes the rules? Sorry, that’s not in the bot’s programming.

With payer guidelines constantly shifting, denials on the rise, and administrative burdens growing, healthcare providers need more than automation. They need intelligence.

Why Traditional Automation Falls Short

RPA has its place, but it’s not built for the complexity of modern revenue cycle management (RCM). Its biggest weaknesses?

• Zero adaptability. If a payer updates claim submission requirements, RPA bots don’t adjust — they just fail.
• No contextual awareness. RPA doesn’t know why a claim was denied or what’s likely to happen next. It just moves data from one place to another.
• No learning curve. AI improves over time, but RPA remains frozen in time unless someone reprograms it.
• Can’t problem-solve. RPA won’t notice payer trends, optimize claim prioritization, or proactively prevent denials.

In short, RPA does what it’s told. AI figures out what needs to be done.

AI, the Next Step in Revenue Cycle Management

AI takes automation a step further. It doesn’t just complete tasks, it makes smarter decisions. Here’s how AI is reshaping revenue cycle management:

• Accurate patient demographics. Patient name, date of birth, and insurance details must be correct from the start to prevent denials. AI-powered document capture extracts and validates this data automatically, reducing errors and ensuring that claims are submitted with accurate information.
• Smarter prior authorizations. Prior auth delays are the worst. RPA can submit requests faster, but it can’t anticipate what payers need or adjust to shifting criteria. AI detects patterns, flags missing information in advance, and even suggests the best way to avoid follow-ups.
• AI-powered coding audits. Billing rules are a moving target. AI-driven audits ensure claims are coded correctly the first time, preventing costly denials and compliance issues.
• Intelligent A/R prioritization. Most revenue cycle teams treat all outstanding claims equally or assign rules arbitrarily, but not all claims have the same likelihood of getting paid. AI predicts which claims should be prioritized based on payer behavior, patient payment history, and contract terms, helping providers maximize revenue with less effort.
• Denial prevention: catch issues before they happen. Instead of reacting to denials, AI predicts them. By analyzing payer trends and historical data, AI can flag risky claims before submission, reducing rework and accelerating reimbursements.

What Healthcare IT Leaders Should Consider

AI is only as good as its implementation. Before rolling out AI-powered RCM, healthcare CIOs should focus on:

• Seamless integration. AI should complement, not replace, existing EHR and RCM systems.
• Meaningful success metrics. AI’s impact should be measured by claim accuracy, denial reductions, and A/R improvements, not just automation rates.
• AI + human collaboration. AI isn’t here to replace revenue cycle teams. It’s here to free them from repetitive tasks so they can focus on complex problem-solving.

Final Thought: The Future is AI (But Not the AI You’re Thinking Of)

Healthcare doesn’t need AI that just automates. It needs AI that thinks.

The future of revenue cycle management won’t be about simply working faster. It will be about working smarter. AI-powered decision-making will reshape how healthcare providers manage revenue, shifting from reactive firefighting to proactive optimization.

The question isn’t whether AI will transform RCM. The question is,: will you be ahead of the curve, or struggling to catch up?