Healthcare call center metrics: the six numbers to pull and read

Your central phone report can show faster answers while one office says patients are calling back twice. Both can be true. The reports may be counting different calls or stopping the clock at different points.

Hello Patient builds AI agents for healthcare. The agents answer the calls routed to them. They book appointments, route refill requests, explain balances, and send secure payment links. Agree on six measures across central and local lines, then check the outcomes behind them. This page shows you how to read those six measures, what each one leaves out, and how to check an AI agent's effect in your own reports.

Which six healthcare call center metrics can you compare across offices?

Pull these six for each location and queue, using the same reporting period:

• Calls offered: incoming attempts delivered to the queue you're measuring.
• Calls answered: calls connected under your phone report's definition of an answer.
• Calls abandoned: callers disconnecting while they are queued.
• Average speed of answer: average queue wait for the answered calls included in that measure.
• First-call resolution: eligible initial requests confirmed resolved on the first contact, through outcome review.
• After-hours volume: attempts outside the relevant office's published hours, including holidays.

Keep the counts beside the percentages, and write down how your phone system defines each measure. NICE's campaign reporting documentation counts contacts delivered to an agent queue. Your system may count them differently.

NICE and Amazon Connect are useful examples of how vendors define these measures. Their definitions aren't universal standards.

Use one week to check what each report counts

Before you compare locations, fill in the definitions behind each number. Use the companion workbook to record them alongside one week of phone results and a separate review of patient requests. These are proposed worksheet conventions to map to your own systems.

The workbook has editable fields for each measure's unit, exclusions, source system, owner, period, numerator and denominator. Counts use N/A for the denominator. Offered, answered and abandoned totals may not reconcile. ASA covers the queue wait included in that measure.

1. Set the comparison. Choose the week, queues, locations, request types and operating hours. Compare the same mix across reports and record differences you cannot align.
2. Collect the phone records. Enter daily counts and ASA with the definitions used. Separate business-hours and after-hours observations using each location's local hours and holidays.
3. Review requests separately. Define eligible requests, the repeat-contact window and the endpoint you will count as resolved. Distinguish a completed booking from a submitted refill handoff that still needs clinical approval. Record confirmed resolution, unresolved requests and unknown outcomes. For repeat calls, use an approved deidentified request key; a matching caller number alone does not establish the same person or request.

What does abandonment rate count, and what can it hide?

Abandonment rate counts queued calls that disconnect, but one patient may create more than one abandoned call.

A report with 100 abandoned calls doesn't necessarily describe 100 patients. Some may have tried twice. Those numbers are a hypothetical example, but the distinction matters when you're estimating how many people couldn't get through.

Amazon Connect calculates abandonment rate as abandoned queued contacts divided by queued contacts, multiplied by 100. Use the same filters for both counts. Check whether the report leaves out short calls and how it treats callbacks before you compare percentages.

Look at repeat attempts too if your reporting shows them. A lower abandonment rate can still hide patients calling several times about one unfinished request. If you're estimating the cost of missed patient calls, keep call attempts separate from the number of people who called.

Where does average speed of answer stop counting the wait?

Average speed of answer only tells you about the part of the wait your report measures.

Amazon Connect's average speed of answer covers queue time for answered calls. It does not include abandoned calls, time before the queue, or waits that happen later. Write down where your own report starts and stops the timer.

Say an AI agent answers a routed call and then transfers the patient to staff. If the timer stops when the agent answers, average speed of answer can fall even though the patient still waits for a person. Track that later wait separately.

The same issue comes up when calls move between central and local teams. Keep transfer counts beside the answer-time report, and know whether the report counts calls or individual transfers.

What counts as first-call resolution at a medical practice?

A request counts as resolved when the caller gets what they called for on that first contact.

Genesys includes first-call resolution in its contact-center definitions. That is one vendor's definition, not a universal standard. Your group still needs to decide what a finished request means for each kind of call.

A booking request can be finished when the right appointment is booked and confirmed. Sending the request to someone who will book it later leaves it open.

For a refill request, routing the message can complete the submission. It does not mean the refill was approved.

For billing, explaining a balance may finish a patient's question. If the patient called to pay, the request is finished when they pay through the secure payment link the agent sends. Judge the outcome against what the patient called for instead of giving every answered call the same credit.

Hear a Hello Patient agent handle a call or request a free demo agent.

How should you count after-hours and overflow calls?

Count after-hours and overflow separately because the same call can belong to both groups.

Use each office's published hours, including holiday changes, to mark after-hours calls. An evening call may be after hours for one office while another location is still open.

Overflow tells you how the call was routed. It can happen while an office is open or after it closes. If you add after-hours and overflow totals together, some calls may get counted twice.

Compare an after-hours route with its own earlier results. If those calls used to go to voicemail, answering them changes what can happen before the office reopens.

Which call center metrics can an AI agent change?

Measure the agent’s work through the calls it answers.

Our agents answer the calls routed to them as they come in. That can change answered calls, abandonment, and recorded answer time on those routes.

Read those call results alongside appointment availability and clinical decisions. Incoming call counts describe attempts to reach the group; after-hours volume tells you when people called.

At its Georgia centralized billing office, Piedmont Urgent Care reached about 48% of billing calls resolved without staff within its first three months.

Use that result to ask what the agent could handle in your group, then measure those calls in your own reports.

How do you check a vendor's call center metric claims against your own reports?

Ask exactly which calls the claim covers, then check the same route and period in your own reports.

Ask which measure is expected to change and how unfinished requests are counted. Use the same comparison period before and after, and record changes to office hours or call routing. If a vendor makes a claim about all incoming calls, the evidence needs to cover all incoming calls. A result from one assigned route only tells you about that route.

Your group measures calls in its own phone system. Hello Patient integrates with the EHR, practice management, and CRM systems healthcare already runs on. For your security review, see Hello Patient's HIPAA compliance information.

Find out whether Hello Patient fits your practice. Book a call and bring a central report and one office's report. The call takes 15 minutes, and we'll start with the difference you want explained. Make sure to ask for a free demo agent to see how it will sound to your patients.

Frequently asked questions

What are the most important KPIs for a healthcare call center?

Start with the request you want Hello Patient to handle, then choose the KPI that shows whether it changed. Keep six measures available: calls offered, calls answered, calls abandoned, average speed of answer, first-call resolution, and after-hours volume. If you're reviewing evening coverage, look first at after-hours attempts and what happened to them. If patients keep calling about one request, look at resolution and repeat attempts. The other measures explain what changed around that problem. One better number does not tell you whether the calls you cared about were actually handled.

What is a good abandonment rate for a healthcare call center?

This review did not verify a healthcare-wide abandonment rate you could use as a target, so measure Hello Patient against your own baseline. Measure before and after with the same queues, hours, callers, short-call rules, and reporting period. Ask for the counts behind the percentage. Then look at repeat attempts. A lower abandonment rate can still hide patients calling several times about one unfinished request.

Why are patients still waiting when our medical practice meets its answer-time target?

An average can look fine while some patients still wait much longer. When you evaluate Hello Patient, look at the spread of wait times where your phone system provides it, not only the group average. Compare locations, hours, and request types, and check the share of calls that meet the service target your group chose. This review verified no numeric healthcare-wide answer-time target to recommend. If routing or coverage changes, measure wait times the same way before and after. Then you can tell whether patients' waits changed or the report is counting different calls.

How do you measure first-call resolution at a medical practice?

For a Hello Patient evaluation, use this local review method: sampled initial requests confirmed resolved on first contact ÷ all eligible initial requests reviewed × 100. Keep unresolved and unknown cases in that denominator, and report unknowns separately. In a hypothetical sample of 100 eligible requests, 70 confirmed resolved, 20 unresolved, and 10 unknown yield 70% confirmed first-call resolution, with 10 unknown outcomes. Use the same eligibility rules and follow-up window each time. NICE describes using call logs, outcome data, and surveys. A patient not calling back does not, by itself, show that the request was resolved.

Why might staff calls take longer after an AI agent starts answering a medical practice's phone?

The calls reaching staff may take longer because Hello Patient handles some routine requests before they get there. When your practice routes routine balance questions to the agent, your billing team's calls are the disputes and requests that need a closer look. Compare the same request types before judging the staff average. Also look at how many calls reached the team and how much unfinished work remained. A longer staff call, by itself, does not show that performance improved or got worse.

How does a multi-location medical group compare call center metrics across locations fairly?

Keep each office visible when you compare Hello Patient results. Use the same dates, hours, queue definitions, and call types before you combine locations. For rates, add the underlying counts first, then calculate the group percentage. Do not simply average each office's percentage. For average speed of answer, weight each location by the calls included in that location's average, which may not match its total answered calls. Record routing changes, closures, and overflow sent elsewhere. That lets you see whether the group changed or whether the result came from one office or one route.