Call Center Cost Reduction: 7 Ways AI Can Cut Healthcare Contact Center Costs

Introduction

Healthcare contact centers are among the most expensive to run in any industry. Industry cost benchmarks put the average cost per call in healthcare at $17 to $35 or more, compared to $6 to $15 in industries like retail and technology. Labor makes up 60 to 75 percent of total contact center spend, which means most of that cost difference comes down to one thing: how much time agents spend on each interaction.

For healthcare leaders under pressure to control costs without sacrificing patient experience, call center cost reduction has become a top priority. The good news is that cutting costs doesn't have to mean cutting corners on care. It means understanding exactly where time and money are being lost, then applying the right mix of automation and intelligence to close those gaps.

What Drives Healthcare Contact Center Costs?

• Before fixing the cost problem, it helps to understand where the money actually goes. A handful of operational patterns quietly inflate the cost of every healthcare interaction.

• High average handle time. Healthcare conversations tend to run longer than a typical retail or telecom call because agents have to verify identity, pull up records across multiple systems, and explain clinical or billing details clearly. Every extra minute of average handle time multiplies across thousands of calls a month.

• After-call work. Once the call ends, agents still have to document the interaction, update the patient record, and close out the ticket. This after-call work rarely shows up in call duration reports, but it consumes real agent capacity.

• Agent knowledge and search time. Healthcare agents often work across insurance portals, EHR systems, scheduling tools, and internal knowledge bases just to answer one question. The time spent searching for the right answer is one of the least visible but most expensive parts of the call.

• Transfers and escalations. When an agent can't resolve an issue, the call gets passed to another team, another location, or a specialist. Each transfer adds hold time for the patient and duplicate handling time for the organization, and a rising escalation rate is often a sign of deeper workflow gaps.

• Repeat calls. If a patient's issue isn't fully resolved the first time, they call back, sometimes more than once. Every repeat contact is a call the organization pays for twice without solving anything new.

• Manual QA. Traditional quality assurance samples a small fraction of calls, usually 2 to 5 percent, which means most conversations go unreviewed. Coaching gaps and compliance risks can go unnoticed for months.

• Inefficient workflows. Outdated scripts, disconnected systems, and unclear escalation paths force agents to take longer routes to reach the same outcome.

• Staffing requirements. All of the above adds up to more headcount than would otherwise be needed, plus higher overtime and higher hiring and training costs when agent turnover follows.

• For healthcare organizations that still rely heavily on human agents, reducing the time and effort required to handle each interaction can be just as important as automating calls. AI-powered agent assist, conversation intelligence, and automated quality assurance can help organizations reduce these operational costs without removing humans from complex patient interactions.

7 Ways AI Can Reduce Healthcare Contact Center Costs

1. Automate Routine Patient Interactions

A meaningful share of healthcare call volume is made up of routine, low-complexity requests: scheduling, rescheduling, appointment confirmations, benefits FAQs, and simple administrative requests like address or insurance updates. Conversational AI and virtual agents built for healthcare can handle these interactions end to end, including authentication and compliance checks, without a live agent ever picking up the call.

One global healthcare provider used Level AI's virtual agent to automate 45 percent of prescription refill calls while also speeding up transfers for the calls that still needed a human agent. That's a direct reduction in the number of interactions that require paid agent time, and it scales without adding headcount.

Cost impact: fewer calls reach the agent queue in the first place, which lowers total labor hours before any other optimization is applied.

While automation can remove some calls from the agent queue, many healthcare interactions still require human judgment, whether that's a complex billing dispute, a clinical question, or a distressed patient. That's where AI can help reduce the cost of the calls that agents continue to handle.

2. Reduce Average Handle Time With AI-Powered Agent Assist

This is one of the most direct ways AI reduces cost in a healthcare contact center, because it targets the exact bottleneck agents face on every call.

Without AI assistance, a typical call looks like this: the patient explains their issue, the agent listens, then stops to search a knowledge base or navigate three or four different systems, finds the relevant answer, and finally resolves the call. Each of those search steps adds seconds or minutes that add up across thousands of interactions.

With real-time agent assist, the flow changes. The patient explains their issue, AI listens to the conversation as it happens, surfaces the relevant policy, script, or account detail automatically, and suggests the next best action. The agent resolves the call using guidance that's already in front of them instead of hunting for it.

Level AI's agent assist works this way in live conversations, surfacing relevant information, guidance, and recommended actions so agents spend less time searching and navigating between systems and more time actually resolving the patient's issue. According to customer insight data, this kind of contextual assist can save an agent 1 to 1.5 minutes of handle time per call by reducing manual data entry and lookups.

The cost mechanism is simple: lower average handle time means each agent can handle more interactions in the same shift, which increases overall team capacity and lowers the cost per interaction, without adding a single new hire.

3. Reduce After-Call Work With AI-Powered Summaries

Once a healthcare call ends, the agent's job isn't done. They still have to write a summary, apply a disposition code, log key topics discussed, note any follow-up actions, and update the patient's file. This after-call work often adds another minute or more per interaction, and it's time that doesn't touch a single patient.

AI can generate automatic call summaries, dispositions, key topics, action items, and follow-up notes the moment a call ends, based on what was actually said in the conversation. Instead of writing notes from memory, agents can review and confirm an AI-generated summary in a fraction of the time.

With AI-generated summaries and automated post-call workflows, agents can spend less time documenting interactions and more time helping patients. This matters for compliance too, since consistent, complete after-call work documentation reduces the risk of gaps during an audit.

Cost mechanism: less after-call work per interaction means more agent capacity across the day, which lowers the operational cost of running the contact center without changing staffing levels.

4. Reduce Transfers and Repeat Calls

Every transfer and every repeat call is a cost the organization pays twice for the same issue. AI can reduce both by giving agents the full context they need to resolve a call the first time, including prior interaction history, account status, and relevant policy details, so they aren't forced to pass the patient along simply because the information wasn't in front of them.

Beyond helping individual agents, conversation intelligence can analyze patterns across thousands of calls to answer questions that are much harder to see one call at a time:

• Why calls are being transferred, and to which teams

• Where patients consistently get stuck in a workflow

• Which issues generate the most repeat contacts

• Which processes create unnecessary escalations

This shifts the conversation from "AI makes agents faster" to a more useful question for cost reduction: why are these calls expensive in the first place? A high first-call resolution rate is often the clearest signal that this kind of root-cause work is paying off, since it means fewer patients are calling back for the same issue.

5. Automate Quality Assurance Across Patient Conversations

Traditional QA in most healthcare contact centers looks the same everywhere: managers manually sample a small number of calls, review them, score them against a rubric, and schedule coaching sessions. Because of the time this takes, only a small fraction of conversations, often in the low single digits, ever get reviewed.

AI-powered QA changes the coverage entirely. AI can analyze conversations at scale, identify issues automatically, score interactions against a scorecard, surface trends across teams, and flag specific coaching opportunities, all without a manager listening to a single call.

This applies directly to areas that matter most in healthcare: automated conversation scoring, compliance monitoring, script and disclosure adherence, agent performance tracking, patient experience signals, and risk detection for calls that need immediate review. According to customer insight data, one organization reached 75 percent automation of its custom QA scorecard, saving an estimated 10,000 hours of manual review work across half a million conversations.

Cost mechanism: automated QA means less manual review time, faster and more targeted coaching, and fewer costly errors that go unnoticed until they become compliance issues. Level AI's quality assurance software is built to review 100 percent of conversations rather than a small sample, which is what makes this level of coverage possible. If your team is still relying on manual sampling, it's worth seeing how automated QA works on your own call volume.

6. Identify Where Your Contact Center Is Losing Money

Reducing handle time and automating QA make individual calls cheaper. But the bigger cost reduction opportunity is often structural: finding out why certain calls are expensive in the first place.

Suppose a healthcare contact center discovers that 15 percent of transfers originate from the billing team. On its own, that number doesn't explain much. But conversation analysis can dig into what patients are actually asking about, why agents are choosing to transfer rather than resolve, and which specific issues repeatedly require escalation to a specialist. Once that pattern is visible, the organization can fix the underlying workflow, whether that's a knowledge gap, a missing system integration, or an unclear policy, instead of continuing to pay for the same transfer every day.

Level AI's analytics and conversation intelligence can help surface exactly this kind of pattern across a healthcare organization's full conversation volume, including:

• High-cost call types and what's driving them

• Frequent escalation drivers

• Transfer patterns by team or issue type

• Repeat-contact drivers

• Agent performance gaps

• Knowledge gaps in existing documentation

• Process inefficiencies that add unnecessary steps to a call

This is the point where AI stops being just an agent-assist tool and becomes an operational intelligence layer for the entire contact center, one that tells leadership where the money is actually going.

7. Optimize Agent Performance and Workforce Capacity

Conversation data can also point to specific, actionable improvements in how the workforce operates. AI can identify which agents need coaching on specific skills, which workflows consistently cause delays, which call types consume the most time relative to their complexity, which agent behaviors correlate with longer calls, and where additional training would have the biggest impact.

It's worth being direct about what this is for. The goal isn't necessarily to reduce the number of agents. It's to increase the amount of work each agent can handle effectively. That distinction matters in healthcare, where patient trust and care quality depend on having enough skilled people available, not fewer of them. Used this way, AI-driven call center efficiency becomes a capacity question rather than a headcount question, which is a safer and more sustainable position for any healthcare organization to take.

How Level AI Helps Healthcare Contact Centers Reduce Costs?

• Level AI helps healthcare contact centers reduce operational costs across three connected areas, rather than treating cost reduction as a single feature.

• Make agents more efficient. Real-time agent assist, knowledge retrieval, next-best-action guidance, and in-call conversation guidance reduce the time it takes to resolve each patient interaction.

• Reduce manual work. Automated call summaries, automated QA across every conversation, and large-scale conversation analysis take repetitive documentation and review work off agents' and managers' plates.

• Find operational inefficiencies. Transfer analysis, repeat-contact analysis, root-cause identification, and agent performance insights show leadership exactly where cost is being created, not just where it's being spent.

• Many healthcare contact centers already know their cost per call is too high. What's harder is knowing exactly which of the seven levers above will move the needle fastest for their specific call mix, staffing model, and patient population.

Reduce Insurance Contact Center Costs with Level AI

See how Level AI helps insurance contact centers reduce operational costs, improve agent efficiency, and deliver better policyholder experiences.

Schedule a demo

Reduce Insurance Contact Center Costs with Level AI

See how Level AI helps insurance contact centers reduce operational costs, improve agent efficiency, and deliver better policyholder experiences.

Schedule a demo