Your Guide to AI in Medical Billing: How to Improve Human Oversight
The American Medical Association (AMA) has officially integrated AI-specific descriptors into the CPT code set. This signals a permanent shift in how we process patient data. In fact, a survey from Eliciting Insights revealed that 75% of health systems now use or plan to use at least one AI application.
“In 2026, health systems are embracing AI to address both workforce constraints and financial pressures," said Trish Rivard, Eliciting Insights CEO.
For medical practices, the key challenge of integrating Artificial Intelligence (AI) into medical billing is not just adopting new technology, but doing so without losing the essential human touch. In this article, understand what AI can do for medical billing and cultivate that human element thoughtfully, ensuring it remains strong even as practices navigate today’s increasingly tech-driven healthcare landscape.
Key Takeaways
Medical billing is the process of generating healthcare claims to submit to insurance companies to obtain payment for medical services rendered.
AI refers to the simulation of human cognitive functions, such as learning, reasoning, and problem-solving, by machines.
AI automation tackles the biggest headaches of traditional medical billing head-on.
In medical billing, we need human reviewers to monitor AI tools.
What Is Medical Billing?
AAPC defines medical billing as “the process of generating healthcare claims to submit to insurance companies for the purpose of obtaining payment for medical services rendered by providers and provider organizations.”
Once a claim is submitted, a medical biller must closely track it to ensure your practice is actually paid for the work your provider performed. Unfortunately, most claims get denied; often for reasons like:
Administrative/technical errors
Incomplete or inaccurate patient information
Incorrect or missing insurance or payer details
Duplicate claims or resubmitted claims for the same service on the same date
Coding and documentation problems
Incorrect or mismatched diagnosis and procedure codes
Missing or insufficient documentation
Use of expired or non‑covered codes
Authorization and coverage issues
Missing prior authorization or referral when required by the payer
Services deemed not medically necessary
Non‑covered services relative to the plan’s benefit design
Reappealing these claims can be a tiresome back-and-forth, increasing the administrative burden and costs (from about $25 up to roughly $120 per appeal). AI offers a smarter way to support overworked billers and coders. Here’s a closer look at AI’s role in medical billing.
What can AI do for medical billing?
AI refers to the simulation of human cognitive functions, such as learning, reasoning, and problem-solving, by machines. In healthcare, AI-driven machine learning models are increasingly used to automate billing processes, optimize medical coding, and enhance administrative efficiency.
A digitalized system has many advantages in medical billing since it can:
Analyze electronic health records (EHRs).
Detect coding inconsistencies.
Predict claim rejections.
Streamline reimbursement workflows.
Reduce human error and financial loss.
AI automation tackles the biggest headaches of traditional medical billing head-on, like how manual processes are so labor-intensive, error-prone, and time-consuming. The table below shows the difference between traditional and automated medical billing.
Traditional Medical Billing vs. Automated Medical Billing
Notably, a time-driven activity-based costing study found that medical billing takes an average of 75 minutes for ambulatory procedures and 100 minutes for inpatient procedures, with physicians spending approximately 15 minutes per case, equating to $50 per procedure.
Reich et al. addressed these inefficiencies by implementing an automated point-of-care electronic charge voucher system in an academic anesthesiology practice, resulting in a 3% increase in annual revenue and a 10-day reduction in accounts receivable.
Despite the boom in AI implementation in healthcare, it’s still important that we don’t miss this crucial element: the human touch.
Why is human oversight important in AI medical billing?
Richard Frank, MD, PharmD, a member of the AMA-convened Current Procedural Terminology (CPT®) Editorial Panel, said, “These autonomous devices are still going to be under the judgment of the prudent physician making those decisions for their individual patient.”
Remember, technology alone cannot fully interpret clinical nuance. We are the ones who can understand changing regulations or ethical context. In medical billing, we need human reviewers to monitor for patterns of up‑coding, down‑coding, or fraud indicators, helping the organization stay in compliance with local frameworks and avoid fines or recoupments.
Here are ways you can empower your clinical staff to work well with technology:
Educate them with the “why” of AI. Explain how each AI tool (EHR‑integrated AI, billing engines, chatbots, etc.) can help them reduce their burden or improve care.
Design workflows with them, not for them. Map where AI fits into their existing processes (e.g., auto‑coding drafts, prior‑auth nudges, reminder messages, lab TAT alerts).
Define clear escalation paths. Ensure that they know when to override, pause, or double‑check AI‑generated billing codes, orders, or patient messages.
Offer structured upskilling. Encourage learning about data literacy, AI fundamentals, and tool‑specific competencies.
Be AI-Powered While Human-Reliant with Synapse
Human oversight does not replace AI; rather, it anchors it in clinical reality, compliance, and ethics. What we want is an AI‑driven medical billing that is safer, more accurate, and sustainable for your practice.
About Us
Synapse Revenue Cycle Management handles all aspects of the revenue cycle with a focus on transparency, accuracy, and partnership. We believe that healthcare providers should always feel prepared, informed, and in control of their billing operations. That’s why full visibility is at the core of everything we do, from coding and claims submission to follow-ups and reimbursement tracking.
Source:
Alhejaily, A.-M. (2024). Artificial intelligence in healthcare (Review). Biomedical Reports, 22(1).
https://doi.org/10.3892/br.2024.1889
CPT codes offer the language to report AI-enabled health services. (2025, January 2). American Medical Association.
https://www.ama-assn.org/practice-management/cpt/cpt-codes-offer-language-report-ai-enabled-health-services
Gleeson, C. (2026, March 24). Health system AI adoption surges in 2026 with execs reporting increased ROI: survey. Fierce Healthcare.
https://www.fiercehealthcare.com/ai-and-machine-learning/75-us-healthcare-systems-use-plan-use-ai-platform-2026
Manual vs Automated Invoice Processing: A Cost Comparison - Gotbilled. (2025). Gotbilled.com.
https://www.gotbilled.com/blog/manual-vs-automated-invoice-processing-a-cost-comparison
Measuring the Cost of Denials and the Impact of Prevention. (n.d.). Www.os-Healthcare.com.
https://www.os-healthcare.com/news-and-blog/measuring-the-cost-of-denials-and-impact-of-prevention
Nasser, L. K. (2025). The Evolution of Automated Medical Billing With Artificial Intelligence: A Review With a Global and Saudi Perspective. Cureus.
https://doi.org/10.7759/cureus.96464
Reich, D. L., et al. (2006). Development of a Module for Point-of-care Charge Capture and Submission Using an Anesthesia Information Management System. Anesthesiology, 105(1), 179–186.
https://doi.org/10.1097/00000542-200607000-00028
What is Medical Billing? (2025). Aapc.com.
https://www.aapc.com/resources/what-is-medical-billing?srsltid=AfmBOopXLTQOUwccNxmyT4sc_q-CPZHH6whEOKXFxCCkBMuiR5vsvzwQ
Williams, J. (2024, March 28). Battle of the Bots: As payers use AI to drive denials higher, providers fight back. HFMA.
https://www.hfma.org/revenue-cycle/denials-management/health-systems-start-to-fight-back-against-ai-powered-robots-driving-denial-rates-higher/
Tseng, P., et al. (2018). Administrative Costs Associated With Physician Billing and Insurance-Related Activities at an Academic Health Care System. JAMA, 319(7), 691.
https://doi.org/10.1001/jama.2017.19148