How Quickly Can Your Hospital Call Center Adapt to AI?

Many hospital call centers face a unique set of challenges that make adapting to artificial intelligence (AI)-powered communication tools particularly difficult. These barriers generally fall into three categories: regulatory/ethical, technical/data, and organizational/cultural.

This white paper draws on Amtelco’s decades of experience in the communications industry to share helpful information about steps you can take to prepare your hospital call center staff and technology systems for AI use.

Regulatory and ethical barriers exist because healthcare is a highly regulated industry that handles incredibly personal patient information. Data privacy and security concerns are often the most critical barrier. AI tools rely on vast amounts of sensitive patient data. Strict regulations, such as HIPAA in the U.S. and GDPR in Europe, impose complex requirements for data handling, storage, and transmission. Ensuring AI systems are fully compliant poses a significant and resource-intensive hurdle.

If an AI communication tool provides incorrect or misleading information that leads to patient harm, it can be unclear who is legally responsible: the clinician, the hospital, or the software developer. This uncertainty creates a risk-averse environment that discourages adoption.

Healthcare data is often fragmented and siloed across different legacy systems. However, AI requires large volumes of high-quality, standardized data. The lack of interoperability makes it difficult to integrate AI tools or build effective models. AI solutions must integrate seamlessly with existing organizational platforms, which can be a technically challenging.

Many advanced AI models (especially deep learning) are “black boxes”—you can see the output, but the reasoning behind it is opaque. For patient care and communication, people often need to understand why the AI made a certain suggestion before they can trust it or act on it. The lack of trust people have about the AI is another hurdle.

Using AI in healthcare is a relatively new concept, and healthcare organizations understandably have a skills gap in their internal talent to properly develop, implement, and maintain AI tools. Training existing staff on AI systems can be a huge logistical and financial undertaking.

Healthcare organizations and their call centers are actively developing advanced strategies to manage the unique challenges of integrating AI, with a primary focus on minimizing disruptions and safeguarding patient data. These methods can be used when presenting a plan to leadership when thinking about integrating AI into your hospital call center:

Phased migration, also known as an incremental deployment or hybrid staged extension, is the primary strategy for integrating AI into existing workflows and legacy systems without a costly and high-risk “rip and replace” overhaul.  For example, the organization may start with a small, contained, and low-risk AI use case (e.g., an AI chatbot for patient self-scheduling) within a single department or clinic. If the system fails, only one small area is impacted, not the entire hospital.

Sidecar architecture refers to the introduction of the AI as a “sidecar” service that sits outside the main systems. In a hospital call center, the sidecar architecture pattern allows AI functionalities (like natural language processing, intelligent routing, and data analysis) to run in a separate, isolated process alongside the main, often legacy, call center system. This separates the AI logic from the core application, enabling easier updates, better scalability, and reduced risk to existing operations.

Staff resistance can be overcome using a gradual rollout and change management. Training is focused on the initial pilot group. The rollout expands department by department, allowing staff to become accustomed to the new tool and for “super-users” to emerge and champion the technology. Staff will likely see immediate, tangible benefits before the system is fully implemented, which will help build trust.

Costs can be managed using a staged investment approach. Investment is spread out over time. The initial investment is small for the pilot, with larger funding releases tied to the demonstrated success and ROI of the initial phases. The organization demonstrates the value of AI before committing to a substantial capital expenditure.

I firmly stand behind my stance that as an industry, Healthcare Contact Centers MUST embrace AI and shape it to our needs. We should use AI as our trusted companion that keeps the focus on our patients. I am learning everything I can by diving into our beta testing of Active Insights with Amtelco. I’m giving Amtelco feedback to do what I can to make sure our patients and their families, as well as potential patients, have the best possible contact with our institution. We put our patient’s needs first.

Jacqueline Pilon, Connect Call Center & Switchboard Manager for State University of New York (SUNY) Upstate Medical University

Reassuring call center agents about the use of AI is often the single most critical factor for successful AI adoption in a high-stress, high-empathy environment like a hospital call center. Below are specific, actionable mitigation strategies to address agent resistance and workflow disruption:

1. Focus on AI as an “Agent Co-Pilot,” Not a Replacement

The primary strategy must be to re-frame the AI’s role from automation to augmentation. Clearly communicate that AI is being introduced to handle “low-value, high-volume, repetitive tasks” such as, verifying insurance, looking up pharmacy hours, and simple appointment reminders.

This frees up the agent to focus on the “high-value, high-empathy, complex conversations” where their human skills are truly needed, reducing burnout and increasing job satisfaction.

2. Redesign the Workflow Around Agent Strengths

Prevent the AI from creating more work by involving veteran call center agents and supervisors in the AI’s design and testing phases. Ask agents to identify the top three most painful, repetitive tasks they deal with daily. The initial AI rollout should focus only on automating or assisting with these specific tasks to provide an immediate, tangible benefit.

Help agents view the AI as a tag-team partner by defining exactly when the AI transfers the call or chat over to an agent, and what information must be included in the handoff. The agent should receive a concise summary of the patient’s issue, their current sentiment (e.g., “Patient is frustrated about billing”), and what the AI has already attempted, thereby eliminating the need for the patient to repeat themselves.

3. Implement Targeted Training and Coaching

The agent’s job is changing, which requires a new type of training. Agents need to understand the AI’s capabilities and, crucially, its limitations. Training should not only include how to use the AI, but also how to manually override the AI when necessary. This builds confidence and maintains human oversight.

Leverage the AI’s ability to analyze calls for targeted coaching. Instead of a manager listening to random calls, use the AI to flag calls where the agent successfully de-escalated a difficult situation. This shifts coaching from focusing on negative performance to reinforcing positive “super-agent” behavior.

By prioritizing these strategies, hospital call centers can more rapidly prepare for integrating AI into their workflows and change the narrative from “AI is taking our jobs” to “AI is taking our busywork, so we can focus on our patients.”