Key takeaways
AI voice agent QA automatically evaluates every healthcare call (human agent and AI voice agent) against your own scorecard, so you no longer depend on a 1% to 5% sample
In 100% of calls, audit five things: patient verification and compliance, accuracy of healthcare information, script and process adherence, patient experience, and call outcome.
You don't need to start from scratch. Existing scorecards can be converted into automated evaluation criteria, validated against human scores, and expanded gradually to full coverage.
Introduction
Most contact centers review only 1% to 3% of their interactions for quality. For a healthcare contact center, that means the vast majority of patient conversations about appointments, prescriptions, referrals, billing, and benefits are never checked by anyone.
That gap matters more in healthcare than in almost any other industry. A missed identity check can expose protected health information. An incorrect answer about a prescription refill or prior authorization can delay care. A confusing call can push a patient to call back two or three times, adding volume and cost to an already stretched team.
Manual sampling was never designed for this level of risk. A QA analyst can only listen to a handful of calls per day, and a random sample rarely includes the calls that actually went wrong. Problems surface weeks later through complaints, audits, or patient churn, long after the moment to fix them has passed.
AI-powered QA changes the math. Instead of reviewing a small sample, healthcare contact centers can evaluate every call, whether it was handled by a human agent or by an AI voice agent for healthcare, and send only the calls that need attention to human reviewers.
This guide covers what AI voice agent QA is, what to audit in every healthcare call, how to move from manual QA to automated QA, how accurate AI scoring really is, how to protect patient data, and what you can learn once you see 100% of your conversations.
What Is AI Voice Agent QA?
AI voice agent QA is the practice of using AI to automatically review and score every patient conversation in a contact center. It applies to calls handled by human agents and to calls handled by AI voice agents, using the same quality standards for both.
What AI-powered QA means?
Traditional QA depends on people listening to recorded calls and filling out a scorecard. AI-powered QA does the listening and first-pass scoring automatically. Every call is transcribed, analyzed, and scored against your evaluation criteria, so QA teams start from a complete picture instead of a small sample.
How AI analyzes conversations?
The AI reads the full transcript of a call in context. It understands who said what, when it was said, and what the conversation was about. That means it can tell the difference between an agent who asked for a date of birth and one who only mentioned it, or between a patient who said "that's fine" politely and one who said it in frustration after being transferred twice.
How automated scoring works?
Each question on your scorecard becomes an evaluation criterion. For example: "Did the agent verify the patient's identity using two identifiers before discussing account details?" The AI checks the conversation, answers the question, and records the reason for its answer along with the exact quote and timestamp that support it. Scores roll up by section, by agent, by team, and by call type.
AI-assisted QA vs. traditional manual QA
Traditional manual QA | AI-assisted QA | |
|---|---|---|
Call coverage | 1% to 5% of calls | Up to 100% of calls |
How calls are selected | Random or supervisor-picked | Every call is scored; risky calls are flagged automatically |
Time per evaluation | Often 20 to 30 minutes | Minutes, with the AI doing the first pass |
Consistency | Varies by reviewer | Same criteria applied the same way on every call |
Evidence | Reviewer notes | Quotes, timestamps, and reasoning for each score |
Speed of detection | Weeks after the call | Shortly after the call |
Compliance visibility | Partial | Every call checked against compliance criteria |
Coaching input | Based on a few calls | Based on patterns across all of an agent's calls |
The role of human-in-the-loop QA
Automation does not remove people from QA. It changes what they spend time on. In a human-in-the-loop model, the AI scores every call and QA specialists review the calls that matter most: flagged compliance risks, low scores, escalations, disputed evaluations, and a regular calibration sample. Human reviewers also refine criteria when the AI and the team disagree, which keeps scoring aligned with how your organization defines quality.
What Should You Audit in 100% of Healthcare Calls?
Full coverage is only useful if you are measuring the right things. Healthcare QA scorecards typically cover five areas.
Compliance and Patient Verification
This is the highest-risk area and the strongest reason to audit every call. Check whether the agent or AI voice agent:
Verified patient identity before sharing any account or health information
Confirmed authorization before speaking with a caregiver or family member
Shared only the minimum necessary information
Read required disclosures (for example, recording notices or billing disclosures)
Avoided discussing PHI with unverified callers
Automated regulatory compliance monitoring makes it possible to flag every call where a required step was missed, instead of hoping one shows up in the sample.
Accuracy of Healthcare Information
Patients act on what they are told. Audit whether information about appointments, office hours, prescription refill status, referral steps, coverage, copays, and next steps was correct and consistent with your policies. For AI voice agents, this also means checking that the agent did not invent answers or go beyond approved workflows.
Script and Process Adherence
Check whether required workflows were followed: appointment scheduling steps, refill request intake, prior authorization routing, triage protocols, and escalation rules for urgent symptoms. Process gaps often explain downstream problems like missed appointments and repeat calls.
Patient Experience
Evaluate how the patient was treated, not just what was completed. Common criteria include:
Did the agent acknowledge the patient's concern?
Was the patient asked to repeat information?
Was hold time explained?
Did the agent show empathy when the patient was anxious or upset?
Was the explanation clear and free of internal jargon?
Resolution and Call Outcomes
Finally, check whether the call accomplished its purpose. Was the appointment booked, the refill submitted, or the billing question answered? Was the patient transferred, and was the transfer necessary? Did the patient leave the call with a clear next step?
Audit area | Example criteria | Why it matters in healthcare |
|---|---|---|
Compliance and verification | Two-identifier verification, authorized caller check | Reduces PHI exposure and regulatory risk |
Information accuracy | Correct refill status, coverage details, next steps | Prevents care delays and patient confusion |
Process adherence | Scheduling, refill, prior auth, and escalation workflows | Keeps operations consistent across teams |
Patient experience | Empathy, clarity, repetition, hold explanation | Drives satisfaction and trust |
Resolution and outcome | Task completed, transfer needed, next step clear | Reduces repeat calls and call volume |
How to Transition From Manual QA to Automated QA
Moving to 100% coverage works best as a staged rollout, not a switch that flips overnight.
Document your existing QA process
Start by writing down how QA works today: which scorecards you use, how calls are selected, who reviews them, how disputes are handled, and how results feed into coaching. This becomes the baseline you will compare against later.
Identify what can be automated
Go through each scorecard question and sort it into three groups:
Clearly observable in the conversation (for example, "Did the agent verify date of birth?"). These are easy to automate.
Requires judgment but is visible in the conversation (for example, "Did the agent show empathy?"). These can usually be automated with well-written criteria.
Requires data outside the call (for example, "Was the correct billing code entered in the system?"). These may need a system integration or screen recording, or may stay manual.
Convert existing scorecards into AI evaluation criteria
Rewrite vague questions so they describe what "good" looks like. "Was the agent professional?" is hard for anyone to score consistently. "Did the agent avoid interrupting the patient and maintain a calm tone, even when the patient was frustrated?" is much easier for both humans and AI.
Validate AI scores against human reviews
Before relying on automated scores, run the AI and your QA team on the same set of calls. Compare results question by question. Where they disagree, review the evidence, then adjust the criteria or retrain your reviewers. Tools that help you calibrate QA evaluations make this step much faster.
Establish a human-in-the-loop process
Decide which calls always go to a human: compliance failures, escalations, very low scores, and agent disputes. Set up a regular calibration sample so the team keeps checking AI accuracy over time.
Expand from sample-based QA to 100% coverage
Begin with one call type or one department, such as appointment scheduling. Once scores are validated and trusted, add more call types and scorecards until every call is covered. Many teams keep their manual reviews during this period and gradually shift reviewer time toward coaching and targeted audits.
Stage | What happens | Human role |
|---|---|---|
1. Baseline | Document current process and scorecards | Define the standard |
2. Pilot | Automate one scorecard on one call type | Review every AI score |
3. Validate | Compare AI and human scores | Resolve disagreements, refine criteria |
4. Expand | Add call types and departments | Review flagged and sampled calls |
5. Full coverage | 100% of calls scored | Focus on coaching, calibration, and edge cases |
For a deeper look at the mechanics, see this guide to call center quality assurance automation.
Can AI Use Your Existing QA Scorecards?
Yes. Most healthcare contact centers already have years of work built into their scorecards, and a good AI QA platform should use them rather than replace them.
Existing scorecards and rubrics
Your current questions, sections, weightings, and pass or fail rules can be entered as they are. The AI evaluates calls against the same agent performance scorecard your reviewers already use, so scores stay comparable before and after automation.
Custom evaluation criteria
You can add new criteria at any time, such as a question about a new refill policy or a new disclosure requirement, without rebuilding the entire scorecard.
Different scorecards for different call types
A scheduling call and a billing dispute should not be graded the same way. Different scorecards can be applied automatically based on call type, queue, or topic. A single call can also be scored against more than one scorecard, for example a general quality rubric and a compliance rubric.
Department-specific requirements
Patient access, pharmacy, billing, member services, and nurse lines each have their own standards. Each team can maintain its own criteria while leadership still sees a consistent view across the organization.
Compliance-specific criteria
Compliance questions can be kept in a separate scorecard with stricter rules, such as automatic failure if identity verification is missed, so they are always visible and never averaged away inside an overall quality score.
Supporting different healthcare interactions
Interaction type | Example scorecard focus |
|---|---|
Appointment scheduling | Verification, correct provider and location, confirmation of date and time |
Prescription refills | Verification, medication and pharmacy confirmation, refill status accuracy |
Billing and payments | Disclosures, balance accuracy, payment plan explanation |
Insurance and benefits | Coverage accuracy, prior authorization steps, next steps |
Clinical triage or nurse lines | Symptom escalation rules, protocol adherence |
AI voice agent calls | Workflow accuracy, correct handoff to a human, no unsupported answers |
How Accurate Is AI-Powered Call Scoring?
Accuracy is the question that determines whether a QA team will trust automated scores. The answer depends on how the criteria are written and how the scores are validated.
How AI scoring works
The AI evaluates the whole conversation rather than searching for keywords. That matters in healthcare, where the same words can mean different things. "I already gave you that" might be a sign of a repeated verification step, not a compliance success.
How AI evaluations are validated
Scores should be tested against expert human reviews before and after launch. Strong platforms benchmark each scorecard against real calls on a regular schedule and show accuracy for each question, so you can see exactly which criteria are reliable and which need work. Level AI, for example, benchmarks Auto-QA rubrics against customer calls every month.
Measuring AI vs. human scoring
The most useful measure is agreement rate: how often the AI and an expert reviewer reach the same answer on the same question. Track it per question, not only per scorecard, because one poorly worded question can hide inside an otherwise accurate rubric. It also helps to measure agreement between your human reviewers, since people often disagree with each other more than teams expect.
Validation step | What to measure | Target outcome |
|---|---|---|
Side-by-side review | AI answer vs. expert answer per question | High agreement on each question |
Reviewer calibration | Agreement between human reviewers | Consistent human baseline |
Ongoing benchmarking | Accuracy trends over time | Stable or improving accuracy |
Dispute tracking | Share of scores agents challenge | Low and falling dispute rate |
Handling subjective criteria such as empathy and communication
Subjective criteria can be scored reliably when they are defined clearly. Instead of "Was the agent empathetic?", describe the behavior: "When the patient expressed worry or frustration, did the agent acknowledge the feeling before moving to the next step?" The AI can then point to the exact moment the patient expressed concern and how the agent responded. Borderline cases can be routed to a human reviewer.
Why evidence behind the score matters
A score without evidence is hard to trust and hard to coach from. Every automated answer should include the reasoning, a quote from the conversation, and a timestamp. This lets a QA specialist confirm a score in seconds instead of listening to the whole call, and it lets agents see exactly why a question was marked the way it was.
What Can You Learn From 100% Call Analysis?
Scoring every call is only the starting point. Once every conversation is analyzed, patterns appear that a 2% sample can never show. As explored in what's hiding in the 97% of conversations you don't analyze, the calls you never review usually hold the answers you need.
With full coverage and contact center analytics, healthcare teams can answer questions like:
Why are patients calling? See the real mix of scheduling, refill, billing, referral, and coverage calls, and how that mix changes week to week.
Why are patients dissatisfied? Find the specific moments, such as long holds, repeated verification, or unclear billing explanations, that lead to negative sentiment.
Why are repeat calls happening? Identify which call types and which answers lead to a second or third call.
Which issues cause escalations? Spot the topics and phrases that most often lead to supervisor requests or complaints.
Where are agents struggling? See which scorecard questions each agent and team misses most often.
Which compliance issues occur repeatedly? Track missed verification or disclosure steps by team, site, or call type.
What separates successful calls from unsuccessful ones? Compare the behaviors in calls that end with a resolved issue against those that end with a transfer or callback.
Key concept: QA score → Pattern → Root cause → Action
A single low score tells you one call went wrong. A pattern tells you something is broken.
Step | Example |
|---|---|
QA score | Refill calls score low on "Patient given a clear next step" |
Pattern | The drop is concentrated in calls about refills that need provider approval |
Root cause | Agents don't have a clear answer for how long provider approval takes |
Action | Update the knowledge base, add a standard response, and coach agents on it |
Pairing QA with voice of the customer insights takes this further, connecting what patients say with how calls were handled.
How 100% Call Auditing Improves Agent Coaching
When coaching is based on two or three calls a month, feedback often turns into general advice like "work on empathy." Full coverage makes coaching specific and measurable.
Identify specific coaching opportunities
With every call scored, you can see exactly which behaviors each agent misses, and how often. Here's more on how AI identifies coaching opportunities in contact centers.
Prioritize the most important behaviors
Not every missed question matters equally. Compliance and accuracy gaps should be coached before minor script deviations. Full data shows which gaps are frequent and which are rare.
Give managers evidence-based coaching insights
Managers can open real calls with the exact moment highlighted, rather than asking an agent to remember a conversation from three weeks ago. Agents are more likely to accept feedback when they can hear the moment for themselves.
Measure whether coaching improves performance
Because every call is scored, you can track whether an agent's scores on the coached behavior actually improve over the following weeks. Agent coaching tools with built-in plans and progress tracking make this visible for both agents and managers.
Move from reactive to proactive coaching
Instead of coaching after a complaint, managers can spot a dip in scores within days and step in before it becomes a pattern.
Sample-based coaching | Coaching with 100% call auditing | |
|---|---|---|
Basis for feedback | A few random calls | Patterns across every call |
Feedback style | General | Specific behavior, specific moment |
Timing | Weeks later | Days later |
Proof of improvement | Hard to measure | Tracked on every call |
Manager prep time | High | Lower, with calls and evidence already selected |
For more practical guidance, see this overview of call center coaching.
How 100% Call Auditing Can Help Reduce Repeat Calls and Call Volume
Repeat calls are one of the biggest hidden costs in healthcare contact centers. Full call analysis helps you find out why they happen. Level AI's research on repeat contacts and satisfaction shows how closely the two are linked.
Identify common reasons for calls
Once every call is categorized, you can see which reasons drive the most volume and which could be handled by self-service or an AI voice agent.
Find repeat-call drivers
Link calls from the same patient over a short period and look at what the first call was about. Often a small number of topics, such as refill status or billing statements, drive most repeat calls.
Detect unresolved issues
Calls that end without a clear next step, or where the patient says "I'll call back," are strong signals of future volume.
Identify confusing processes
If many patients ask the same question about a letter, a portal step, or a bill, the problem is often the process, not the agent. Fixing it can remove calls entirely.
Surface agent knowledge gaps
When certain agents give inconsistent answers on the same topic, the issue is usually training or missing knowledge base content.
Identify unnecessary transfers
Transfers frustrate patients and add handling time. Full analysis shows which call types are transferred most often and whether those transfers were needed.
Reducing avoidable calls is one of the most direct ways to reduce healthcare contact center costs.
How to Protect Patient Data During AI Call Analysis
Healthcare calls contain some of the most sensitive data any organization handles. Any AI QA program has to be built around that.
HIPAA considerations
Any vendor that processes recordings or transcripts containing PHI is a business associate and must sign a Business Associate Agreement (BAA). Confirm the BAA covers every part of the service, including transcription and analytics. This guide to HIPAA-compliant AI voice agents covers what healthcare buyers should verify.
PHI handling
Sensitive details such as names, dates of birth, member IDs, card numbers, and Social Security numbers should be redacted wherever they are not needed. Automated PII redaction software can remove this data from transcripts and recordings before analysis.
Data access controls
Use role-based access so QA reviewers, supervisors, and analysts only see the calls and fields they need. Single sign-on, multi-factor authentication, and automated user provisioning help keep access tight as teams change.
Data retention
Set clear retention periods for recordings and transcripts that match your policies and state requirements, and confirm the vendor can delete data on schedule.
Security requirements
Look for encryption at rest and in transit, independent audits such as SOC 2 Type II, and detailed audit logs of who accessed what and when.
Vendor compliance
Ask whether the vendor or any third party it uses can retain, train on, or reuse your data. Ask for a current subprocessor list and confirm customer data is isolated from other customers.
Human access to sensitive conversations
Limit who can listen to full recordings, log every access, and make sure reviewers only open sensitive calls when there is a clear reason.
Area | What to ask your vendor |
|---|---|
BAA | Will you sign a BAA that covers all services, including transcription? |
Redaction | Is PHI and PII redacted before analysis? |
Access | Do you support role-based access, SSO, and MFA? |
Retention | Can we set and enforce our own retention periods? |
Certifications | Do you hold SOC 2 Type II, ISO 27001, or HITRUST? |
Data use | Is our data ever used to train models for other customers? |
Audit trail | Is every access and AI decision logged? |
You can review Level AI's certifications and controls on the Level AI security page.
The Future of Healthcare Call QA: From Sampling to 100% Coverage
Healthcare QA is shifting from a process built around what a small team can review to one built around every conversation.
Traditional QA
Sample calls → Manually review → Score → Coach
AI-powered QA
100% calls → Automatically evaluate → Identify risks → Find patterns → Coach → Improve
The difference is not only speed. Traditional QA answers the question "How did this agent do on these few calls?" AI-powered QA answers "What is happening across all of our patient conversations, and what should we fix first?"
This shift also matters as more healthcare organizations deploy AI voice agents. Those agents need the same oversight as human agents, and ideally on the same platform, since separating your AI agent and your QA tool creates blind spots. Level AI's approach to auditing virtual agents with VA Pulse applies QA standards to AI voice agent calls the same way they apply to human calls.
Level AI helps healthcare contact centers analyze conversations at scale and turn that data into QA scores, coaching plans, and operational insights. Its AI-powered quality assurance scores 100% of conversations using your existing scorecards, automates more than 80% of a typical scorecard, and reaches over 90% agreement with expert human QA reviewers. Every score comes with reasoning, quotes, and timestamps, so QA teams can verify results quickly and coach from real evidence.
How Level AI Helps Healthcare Contact Centers Audit Every Call
You can't manage the quality of conversations you can't see. When only a small sample of calls is reviewed, compliance gaps, inaccurate answers, and frustrating patient experiences stay hidden until they show up as complaints, audit findings, or repeat calls.
Level AI gives healthcare contact centers visibility into every conversation, handled by human agents or AI voice agents. Teams can bring their existing scorecards, validate automated scores against their own reviewers, and keep humans in the loop for the calls that matter most. Customers have used Level AI to cut manual QA effort by up to 90%, give QA auditors a 6x productivity boost, and move from scoring 1% to 2% of calls to scoring 100%. All of this runs on a platform built with HIPAA compliance, BAA availability, SOC 2 Type II certification, and automatic PII redaction.
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1. How do you transition from manual QA to automated QA?
Start by documenting your current QA process and scorecards. Sort each scorecard question by whether it can be judged from the conversation alone, rewrite vague questions into clear criteria, and run automated scoring alongside human reviews on the same calls. Once agreement is high, expand from one call type to others until all calls are covered. This guide to improving quality assurance in a call center walks through the fundamentals
2. Can you use your existing scorecards and rubrics?
Yes. Your current questions, sections, and weightings can be entered as they are, so scores remain comparable to your historical data. You can refine or add criteria over time without rebuilding the scorecard from scratch.
3. How do you handle different types of calls?
Different scorecards can be applied automatically based on call type, queue, or topic, so scheduling, refill, billing, benefits, and clinical triage calls are each graded on the criteria that matter to them. A single call can also be scored against multiple scorecards, such as a quality rubric and a separate compliance rubric.
4. What is the role of our human QA team after automation?
Your QA team shifts from listening to random calls to higher-value work: reviewing flagged compliance risks and escalations, resolving agent disputes, calibrating automated scores, refining criteria, and coaching. Their expertise defines what good looks like, and the AI applies that standard across every call. Here's more on the traits of a strong call center QA manager.
5. Can you use your existing scorecards and rubrics?
Yes. Your current questions, sections, and weightings can be entered as they are, so scores remain comparable to your historical data. You can refine or add criteria over time without rebuilding the scorecard from scratch.
6. How do you handle different types of calls?
Different scorecards can be applied automatically based on call type, queue, or topic, so scheduling, refill, billing, benefits, and clinical triage calls are each graded on the criteria that matter to them. A single call can also be scored against multiple scorecards, such as a quality rubric and a separate compliance rubric.
7. What is the role of our human QA team after automation?
Your QA team shifts from listening to random calls to higher-value work: reviewing flagged compliance risks and escalations, resolving agent disputes, calibrating automated scores, refining criteria, and coaching. Their expertise defines what good looks like, and the AI applies that standard across every call. Here's more on the traits of a strong call center QA manager.
8. How accurate is the AI in scoring calls?
Accuracy should be measured against expert human reviews, question by question. Level AI typically automates more than 80% of a scorecard and reaches over 90% agreement with expert human QA, and rubrics are benchmarked against real calls every month. Each score includes reasoning, quotes, and timestamps so reviewers can check it quickly.
9. How does the AI handle complex or subjective criteria?
Subjective criteria like empathy work best when they describe a specific behavior, such as acknowledging a patient's concern before moving on. The AI evaluates the full conversation in context, points to the exact moment it used for its answer, and borderline calls can be sent to a human reviewer.
10. What kind of insights can we get from 100% call analysis?
Beyond scores, full analysis shows why patients call, what drives dissatisfaction and escalations, which topics cause repeat calls, where agents struggle, and which compliance issues keep recurring. Tools for AI call analytics help trace those patterns back to root causes you can fix.
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