How to Prioritize Healthcare Contact Center Automation

Start with high-volume, low-to-medium-complexity workflows that have clear completion criteria, reliable source data, safe handoffs to staff, and a measurable operational outcome. These conditions make it easier to automate consistent work while keeping human judgment in the workflow.

The strongest candidates help patients reach a clear next step, give staff a defined role when an exception occurs, and let the health system verify whether the workflow succeeded. Use the five factors below to compare potential workflows before selecting a first deployment.

Which healthcare contact center workflows should you automate first?

Evaluate each candidate against the same five factors. A workflow does not need to be perfect in every category, but unclear ownership, unreliable data, or an undefined escalation path should be resolved before automation begins.

High volume alone is not enough. A strong first workflow combines meaningful demand with manageable complexity, dependable data, limited judgment, and a result the health system can measure.

Why prioritization matters in healthcare contact centers

The front door to a healthcare system is often a phone call, yet many contact centers operate with technology and approaches that have not fundamentally changed in decades. Healthcare organizations must manage high call volume while helping patients reach the right answer, team, or next step.

Healthcare contact centers often struggle with several critical issues:

• Black box on caller experience: Spam calls, abandoned calls, and staff burnout are common challenges. There's often no visibility into what callers are calling about or their experience. On average, health systems resolve only ~41% of calls, meaning nearly 6 in 10 callers are left with a poor patient experience.
• Scaling issues: Healthcare call centers cannot scale up and down to meet fluctuating demand. This results in either overstaffing, which leads to higher cost, or understaffing, which leads to missed appointments and unresolved issues. Call centers experience churn that varies from 20% to over 100% every year.
• Inconsistent quality: Even when calls are resolved, quality can vary widely. With the ability to audit only 1-2 calls per agent monthly, understanding true performance is difficult. Agents can't be expected to speak every patient's language or be available 24/7.
• Reactive with undone work: Many tasks never even get started. Teams are so busy staying above water that there's no opportunity to proactively reach out to patients who need the most support.

Healthcare organizations operate at a complex intersection of patients, vendors, and a large employee base. Millions of touchpoints, mostly handled by people, can lead to poor experiences, burnout, and inefficiency. This complexity makes workflow selection central to a responsible AI implementation.

AI agents can add capacity to patient communications. The practical question is where they should begin, not everywhere they could eventually be used.

Choose the investment model: Augment, Delegate, or Expand

Before implementing AI agents in your healthcare call center, determine your strategic approach to automation. Your investment choice will impact implementation timelines, training needs, and expected ROI. Healthcare organizations typically pursue one of three strategies with AI agents, each offering different benefits:

• Augment: AI agents support staff with their work.
• Delegate: AI agents take on a subset of staff work.
• Expand: AI agents do work that was not possible or didn't make sense for staff to do.

Examples of AI agent workflows for each investment model and user group:

Augment workflows might seem attractive initially because you already have staff in place and want to make them more productive. As we'll see in the next section, however, the business case may not make sense compared to Delegate and Expand workflows.

Map value to a specific workflow outcome

The report evaluates value through care gap closures, revenue gains, and cost savings:

• Care gap closures: The number of tasks that have an opportunity to generate a care gap closure (e.g., CT lung screening), the share that result in that closure, and the value of that closure (e.g., DALYs, QALYs) or reimbursement, expressed in dollars to compare against investment.
• Revenue gains: The number of tasks that can generate revenue (e.g., calls about open orders), the share that result in a revenue-generating event (e.g., appointments booked), and the value of that event in dollars (e.g., revenue per appointment).
• Cost savings: The number of tasks deflected from staff, the number of equivalent tasks staff complete over time, total staff time saved, and the dollar value of that time using benchmark pay rates (usually $75k per year fully loaded).

As you analyze the value and cost to buy and deploy the solution, you'll notice different dynamics across the three approaches:

• Augment: You might automate suggestions to agents in real-time, similar to how a manager sits shoulder-to-shoulder with a staff member during onboarding. It doesn't decrease cost because the agent still does the work and you pay for the solution. It requires retraining and adoption, adds cost with minimal return, and usually doesn't deliver positive ROI.
• Delegate: The business case relies on reducing costs or reallocating resources to higher-value tasks while AI agents handle repetitive, automatable work. It works best when workflow effort and prevalence are well understood, offers clear cost and variability reduction, and delivers clear ROI from additional AI agent capacity.
• Expand: Because this is net-new work, it usually requires a new revenue case or process redesign. Examples include automated outbound calling to patients with open orders, patients the health system couldn't reach with text or portal messages and didn't have the human capacity to call given that 70% of calls go to voicemail. Greenfield opportunities are possible with sharply lower costs and very high ROI for properly chosen use cases.

Evaluate workflow readiness

After defining the investment model and intended value, prioritize and sequence individual healthcare workflows. Apply the five-factor scorecard consistently so each candidate can be compared on the same basis.

• Complexity is driven by integrations with systems of record like EHRs and CRMs (tip: if possible, use flat file exchange via SFTP to avoid system integration where a real-time connection isn't required) and the number of steps in the workflow. Increasing process complexity makes it harder for both AI agents to execute tasks correctly and for patients to complete required actions.
• Volume is driven by the number of tasks that can be automated over a year and the average duration of each task. Longer tasks create more value when automated.
• Integration dependency reflects whether the workflow can access reliable data at the point when it is needed.
• Need for human judgment identifies decisions that should remain with clinical staff, patient access teams, or other designated owners.
• Measurable value connects the workflow to a defined patient or operational result and a baseline that can be reviewed after launch.

What should a health system automate first?

Starting with lower-complexity, fast-to-implement workflows gives the organization a bounded way to test operating ownership, patient handoffs, measurement, and staff readiness before taking on more complex EHR integrations.

Delegate and Expand workflows can provide practical starting points when they combine high volume with low-to-medium complexity and a clear cost, capacity, access, or revenue outcome. Two examples identified in the existing report are:

• Delegate – Hospital call routing: AI agents answer the phone at hospitals and handle all the "switchboard work" of connecting callers to the desired department, service line, or provider office. They also manage lower-volume, low-complexity workflows like password resets and answering health-system questions from the website or a curated FAQ set. This can start as small as a single hospital and requires no EHR integration. The system deflects over 75% of incoming calls, including spam, freeing operators for higher-value work.
• Expand – Outbound calling for patients with open orders: AI agents automatically call patients with open orders and encourage them to schedule (e.g., mammography screenings or specialist visits), transferring qualified patients to the scheduling team. This can start as small as a single service line and avoids lengthy EHR integration by exchanging flat files over SFTP. Instead of wasting 85% of calls on uninterested patients, your team spends 100% of their time with patients who actively want appointments.

Readers evaluating an inbound use case can see how ActiumHealth applies AI agents to healthcare contact center automation.

What should a health system avoid automating first?

Defer workflows until the operating prerequisites are clear when they have:

• Unclear ownership: No team owns exceptions, corrections, or changes to the workflow.
• Unreliable source data: The agent cannot consistently access the information required to complete the work.
• High dependence on human judgment: Clinical, policy, or operational decisions cannot be reduced to bounded rules and safe escalation.
• Weak handoff paths: Staff cannot receive the patient, context, and next action when the agent should stop.
• No defined success measure: The health system cannot tell whether the patient or operational result improved.

Deferral is not a rejection of automation. It creates a clear list of prerequisites the workflow owner can address before implementation.

How should a health system measure the first deployment?

Choose measures that reflect the intended job of the workflow. Establish the baseline before launch, review the same measures after launch, and keep unlike workflows separate.

• Completion or resolution: Did the workflow complete the defined task or leave the patient with a clear next step?
• Accuracy: Did the agent provide the right information and follow the approved workflow?
• Escalation and handoff quality: Did exceptions reach the right person with enough context to continue?
• Staff capacity: Did the workflow reduce repetitive work or move staff time to higher-value tasks?
• Answer rate and abandonment: Did more patients reach the workflow, and did fewer leave before receiving help?
• Patient experience: Did patients encounter less repetition, confusion, or unresolved work?

Use the metric that matches the workflow's intended outcome. A single blended automation rate can hide important differences between routing, scheduling, outreach, and clinical-adjacent workflows.