Measuring the Impact of AI Call Routing on Call Center KPIs Such as Handle Time, Call Transfers, and Patient Satisfaction Scores in Healthcare

Healthcare groups across the United States are always looking for ways to improve how they talk to patients and take care of them. They also want to keep costs down and make sure things run smoothly. One area that has changed a lot is the use of AI call routing systems in healthcare call centers. These systems use artificial intelligence to handle patient questions, direct calls quickly, and automate common tasks. For medical office managers, owners, and IT teams, it’s important to understand how AI call routing affects key measurements like average handle time, call transfers, and patient satisfaction. This helps them decide if investing in this technology is a good idea.

This article looks at how AI call routing changes healthcare call center results in the U.S. It helps improve patient care and changes phone systems with data-driven automation.

What is AI Call Routing in Healthcare?

AI call routing uses smart software that looks at caller information and what they need, then sends the call to the best agent or service option. Traditional call menus use preset choices like “Press 1 for billing.” AI routing uses analytics and natural language processing (NLP) to understand what callers want and shorten wait times.

In healthcare, this means patients get their questions about appointments, prescriptions, or advice sent quickly to the right team. This lowers frustration and helps patients get care faster. For example, during busy times like flu season, AI systems can prioritize urgent calls about medicine or appointment changes. This helps human workers manage the extra calls.

Verizon Health and CVS Health show real examples of AI call routing cutting wait times and reducing call backlogs. Research shows these systems guess call reasons right about 80% of the time. This leads to faster help and fewer call transfers, which helps healthcare providers.

Impact on Average Handle Time (AHT)

Average Handle Time (AHT) is how long agents spend on each call. In healthcare call centers, controlling AHT is important because long calls create backlogs. That means patients wait longer and are less happy.

AI call routing affects AHT in two ways:

• Initial Increase in Handle Time Per Call
  Agents might spend more time on the first call to fully answer the patient’s question, especially if it is about complex topics like appointments or medical advice. This takes more time at first but stops patients from calling again.
• Overall Reduction in Total Time Spent per Issue
  By sending calls to the right agents and cutting down on transfers or callbacks, AI lowers the total time agents spend per patient problem. So even if the first call is longer, the overall time is shorter.

For example, a telecom company using AI routing cut call times by 20% after adding automation. Healthcare groups also found that agents spend more time on the first call, but total time spent per patient drops because repeat calls go down.

Lower AHT helps healthcare practices by reducing agent tiredness, using staff better, and serving patients faster without lowering the quality of care.

Reduction in Call Transfers

Call transfers happen when a caller is passed from one agent to another because the first one cannot solve the issue. High transfer rates mean poor call routing and make patients frustrated. This leads to longer times to fix the problem and lower satisfaction.

AI routing cuts call transfers by:

• Using predictive analytics to find agents with the right skills for each caller.
• Using NLP to understand caller questions instead of fixed menu options.
• Giving agents patient history and call details before or during the call to solve problems faster.

Verizon’s AI helped cut call transfers by removing extra routing steps. Another big telecom company cut call transfers by 45% after using AI routing and saw an 18-point rise in their Net Promoter Score (NPS).

Healthcare call centers have seen similar results. One provider increased their First Call Resolution (FCR) from 62% to 81% by using AI routing and automation. This cut call transfers and helped avoid rules violations. For medical office managers and IT staff, this shows AI routing not only improves patient experience but also helps meet rules by cutting down on wrong or mishandled calls.

First Call Resolution (FCR) and Patient Satisfaction

First Call Resolution (FCR) is an important measure in healthcare call centers. It shows how many questions are fully answered in the first call without needing follow-ups. Higher FCR means better efficiency, more patient trust, and lower costs.

Research finds a clear link between FCR and patient satisfaction. According to the Service Quality Measurement Group, every 1% increase in FCR leads to 1% higher patient satisfaction scores and 1% drop in costs.

Healthcare groups using AI routing have shown this in practice. For example, one provider saw FCR rise from 62% to 81% after using AI call automation. Patient satisfaction scores increased by 22%. This shows how patients respond well when their questions get quick and correct answers.

In U.S. medical practices, especially during busy call times in seasons, raising FCR with AI can help keep patients and build loyalty. Patients who get quick and clear answers are 2.4 times more likely to stay with their providers.

Scalability During Peak Periods in Healthcare

Healthcare practices often get more calls during times like flu season, vaccination drives, or emergencies. AI call routing is designed to handle more calls during these busy times. It keeps calls moving fast and takes pressure off human agents.

CVS Health uses AI during flu season to quickly handle prescription calls. This shortens patient wait times and lets pharmacy and nursing staff focus on harder tasks. This flexibility is important for medical managers running busy clinics or systems with many locations. Adding more staff quickly can be hard and costly.

Using AI call routing helps healthcare groups manage large call increases without lowering service quality. This makes the patient experience better and lowers risks like missed appointments or late medication refills. These things are key to managing the health of many people.

AI and Workflow Automation in Healthcare Call Centers

Using AI in call routing often goes hand in hand with workflow automation. These systems make front-office tasks easier by:

• Handling routine questions automatically, like office hours, appointment confirmations, or billing questions without needing agents.
• Connecting with Electronic Health Records (EHR) and Customer Relationship Management (CRM) systems. AI pulls patient data and past interactions fast to give agents important info before they answer.
• Using AI chatbots so patients can book appointments or ask for medication refills at any time. This lowers call volume during working hours.
• Using NLP to understand spoken or typed questions. This skips old menu trees and sends calls quickly to the right place, helping both operations and patient satisfaction.

Workflow automation allows agents to focus on harder patient needs or urgent questions by freeing them from basic tasks. For healthcare IT teams, linking these systems to practice software improves data and makes communication smoother.

It is important to use best practices during setup to get the most from these technologies. Good data, input from frontline staff, thorough testing, and watching over the system are key. Bad data or rushing can cause wrong call routing, upset patients, and low use by staff.

Measuring Success: KPIs to Track

Healthcare leaders and IT managers can check how well AI call routing works by tracking these KPIs:

• Average Handle Time (AHT): Checks if AI lowers or balances call time without hurting quality.
• First Call Resolution (FCR): Shows the percent of patient questions solved in the first call; a key service quality measure.
• Call Transfer Rate: Counts how often calls are passed between agents due to poor routing.
• Patient Satisfaction Scores: Surveys taken after calls that measure how patients feel about the call center.
• Agent Productivity: Looks at how AI changes agent work and call handling.
• Compliance Rates: Checks that rules are followed by limiting bad call routing and errors in records.

Watching these numbers regularly helps healthcare groups improve AI routing, train agents better, and serve patients well.

Real-World Examples Relevant to U.S. Healthcare Providers

Several healthcare groups in the U.S. show how AI call routing works:

• CVS Health uses AI call routing in busy seasons to cut wait times for prescription refill and appointment calls. This helps patients get timely support.
• One healthcare provider raised their FCR from 62% to 81% after using AI. This cut compliance problems and raised patient satisfaction by 22%.
• Big companies like Verizon and American Express use AI routing to save many customers by cutting call queues and improving problem solving. These lessons apply to healthcare call centers where keeping patients is very important.

These examples show how AI call routing helps manage complex and busy call centers while focusing on patient and operation needs.

A Few Final Thoughts

Medical office managers, owners, and IT staff in the U.S. should consider AI call routing and workflow automation as key tools to improve call center work. Lower handle times, fewer call transfers, and higher patient satisfaction give clear benefits. This helps practices offer patient-focused care while keeping costs down and using staff well.

By using these tools and tracking important measures, healthcare groups can better handle patient calls throughout care. They can keep up with patient expectations for connected, real-time service in today’s healthcare world.