The Impact of Generative AI on Call Center Productivity in Healthcare: A Case Study Approach

Call centers within healthcare organizations play an important role in managing patient calls, scheduling appointments, handling insurance questions, and supporting administrative tasks.
Because healthcare services are in higher demand and insurance processes are complex, many hospitals and medical offices in the United States are using generative artificial intelligence (AI) to improve call center productivity and reduce paperwork.
This article looks at how generative AI is changing call center operations in healthcare. It focuses on revenue-cycle management (RCM), improving workflows, and examples from health systems.

Adoption of AI in Healthcare Revenue-Cycle Management

Revenue-cycle management is a key part of healthcare. It includes tasks from patient registration to billing and collecting payments.
These tasks need to be fast and accurate, especially when dealing with insurance claims, coding, and denials.
Recently, nearly 46% of hospitals and health systems in the U.S. have started using AI in their RCM jobs.
Also, about 74% of healthcare groups are using some type of automation in revenue-cycle processes.
This shows that more places are realizing AI can help speed up work and reduce errors.

One main reason for using AI is to lessen the paperwork for healthcare workers.
Tasks like coding claims, checking insurance coverage, handling denials, and managing patient bills take a lot of time.
AI tools use natural language processing (NLP) and machine learning to automate many of these steps.
For example, AI can automatically assign billing codes from clinical notes. Before, coders had to review these carefully, which took a lot of time.
These efficiencies allow staff to focus on more important tasks, like helping patients coordinate their care.

Generative AI’s Role in Healthcare Call Centers

Generative AI is a type of AI that can produce human-like answers and handle complex conversations.
It has become a useful tool in healthcare call centers.
Medical office leaders and IT managers across the U.S. said call centers using generative AI improved productivity by 15% to 30%.
This is because AI can handle routine patient questions, arrange appointments, and help with insurance details.

Traditional automated phone systems rely on fixed scripts and need human input for many requests.
In contrast, generative AI understands more kinds of patient questions and gives accurate, timely answers.
This cuts down call wait times and lets human agents focus on tougher problems.
Also, generative AI can study caller information and guess patient needs. This helps make communication more personal and call handling faster.

Case Studies: AI Transformations in Healthcare Organizations

• Auburn Community Hospital, New York: Auburn used robotic process automation (RPA), NLP, and machine learning for revenue-cycle management.
  They cut discharged-not-final-billed cases by 50% and improved coder productivity by over 40%.
  This led to better billing accuracy and financial results, raising the case mix index by 4.6%.
  While Auburn focused on coding and billing, fewer billing issues also made call centers run more smoothly since fewer problems were passed on to agents.
• Banner Health: Banner uses an AI bot to automate insurance coverage checking and connects customer insurance data across several financial systems.
  This helps front-office staff spend less time verifying coverage and handling insurance requests.
  Banner’s AI also creates appeal letters for denied claims automatically, making the appeals process smoother without adding work for call center staff.
• Community Health Care Network, Fresno, California: They use AI tools to review claims before sending them, marking claims that might get denied.
  This led to a 22% drop in prior-authorization denials from commercial payers and an 18% decrease in denials for uncovered services.
  These savings let call center and revenue teams reduce time spent on appeals by an estimated 30 to 35 hours each week.

These examples show AI not only improves billing accuracy but also lowers call volumes about payment and insurance, making call centers more productive.

Enhancing Patient Experience Through AI

Healthcare call centers help keep communication open with patients, which affects how happy patients are and if they stay with the provider.
Generative AI can help patients with common requests like scheduling appointments, refilling prescriptions, and asking about insurance coverage.
AI-powered systems answer these questions quickly and correctly, cutting down on patient frustration when dealing with healthcare.

AI can also make payment plans personalized by looking at each patient’s financial situation.
This helps reduce calls about payment problems and improves how much money the provider can collect.

AI and Workflow Automation: Streamlining Front-Office Operations

AI helps call centers not just by answering calls but also by making the front-office work smoother and faster.

• Automation of Eligibility and Authorization: AI can automatically check if a patient is eligible for coverage and manage prior authorizations better than people do.
  This early check stops many claim denials caused by missing or wrong authorizations.
  As a result, fewer patients or providers need to call about authorization status, lowering call volume.
• Claim Review and Denial Prevention: AI systems review claims before submission to catch common errors.
  This means fewer denied claims and less time spent on appeals and re-submissions for call center and billing teams.
  More correct claims also mean fewer calls to clear up billing problems.
• Coding and Documentation Accuracy: Using natural language processing, AI reads clinical notes to find needed information and assign the right billing codes.
  This lowers coding mistakes and speeds up billing.
  Fewer errors also mean fewer patient or insurance calls about denials.
• Fraud Detection and Compliance: AI helps protect data and ensures rules are followed by spotting possible fraud or wrong coding.
  Finding these issues early stops call centers from being overwhelmed by fraud or audit investigations.
• Integration of Multi-System Workflows: AI bots connect different financial and patient data systems so front-office and call center staff have full access to correct information.
  This makes workflows easier and cuts the time needed to answer patient or insurance questions.

These improvements save staff time and cut costs.
For example, the Fresno network saved about 30 to 35 hours each week by reducing appeals work.
Using staff time more efficiently leads to faster patient responses and better financial results for providers.

Challenges in Generative AI Adoption

Even with its benefits, generative AI in healthcare call centers faces some problems.

• Bias and Accuracy: AI must be trained carefully to avoid bias that could cause unfair treatment of patients.
  Wrong or biased answers could hurt patient trust and care.
  AI results need to be checked and sensitive data protected.
• Complexity of Healthcare Data: Healthcare has many different kinds of data.
  AI systems must correctly understand this varied information, which is hard because of differences in documents, payer rules, and local laws.
• Regulatory and Privacy Concerns: Healthcare providers must make sure AI follows rules like HIPAA.
  Keeping patient data safe during AI use and securing automated workflows are top priorities.
• Long-Term Integration: Right now, generative AI mostly handles simple tasks.
  It will take years for advanced AI to be fully used.
  Call centers will also need new staff roles to manage and watch AI tools to keep quality and accountability.

By working through these issues, healthcare organizations can slowly add generative AI to improve patient service and operations.

Specific Implications for U.S. Healthcare Providers

For medical office leaders and IT managers in the U.S., using generative AI in healthcare call centers brings clear benefits.
Reducing denials and automating prior authorizations help improve finances, which is important in a system with tight reimbursement.
Call centers become more efficient and can handle more calls without needing many more staff.

Also, connecting AI with analytics tools like Amazon QuickSight helps track call center performance, find call trends, and spot where improvement is needed.
QuickSight’s real-time data helps managers improve workflows while keeping service quality high.

Healthcare providers working with complex U.S. insurance and Medicare/Medicaid rules benefit a lot from AI automation and proactive claim checking.
Automating insurance checks and claim reviews cut errors and denials caused by payer rules, lowering administrative work.

Finally, since U.S. patients come from many backgrounds, AI’s ability to make communication personal helps improve patient satisfaction.
This is especially helpful in areas where people rely a lot on phone communication.