How AI Is Helping to Improve Patient Care in the Healthcare Industry

Introduction

About 67% of patients said they were at least somewhat likely to switch providers after poor communication before their care, according to a RevSpring survey of 1,000 U.S. patients from 2023. This is third-party research from a patient communication vendor, and it measures stated intent rather than actual switching. Still, it points to something healthcare leaders see every day: patients judge their care by how well the organization communicates, not only by the clinical result.

That is where AI in patient care is making a quiet but real difference. Alongside tools that support diagnosis and treatment, healthcare organizations are using AI (software that learns patterns from large amounts of data) to understand what patients need, reduce the friction of scheduling and billing, support the people who answer patient questions, and spot service problems before they grow.

Better patient care depends on more than clinical decisions. It depends on whether a patient understands their test results, can reach someone to reschedule, knows what to do before a procedure, and feels heard when they call with a worry. Every call, chat, and email is a chance to get that right or wrong. This article explains how AI helps across the patient journey, where human judgment must stay in charge, and how to measure the results. If you want a starting point, voice of the customer insights show what patients are telling you in their own words.

Improving Patient Experience Through Conversation Intelligence

Conversation intelligence is software that listens to (or reads) every patient interaction, turns it into text, and sorts it by topic, outcome, and tone. Instead of relying on a few recordings picked by hand, a team sees patterns across thousands of conversations.

Understand patient needs through real interactions

Patients rarely fill out a survey to say that a billing letter confused them. They call and ask about it. AI can analyze calls, chats, and emails to find recurring concerns, common questions, and service issues, and group them into themes that a team can act on.

This matters because surveys and isolated feedback have blind spots. Only a small share of patients respond to surveys, and those who do tend to be at the extremes, very happy or very unhappy. Conversations include everyone who contacted you, in their own words. Our look at what is hiding in the 97% of conversations you do not analyze explains how much insight sits unread in most organizations.

Patient interaction data can show where confusion clusters, for example:

• Procedures: patients asking what to eat, drink, or stop taking before a test

• Test results: patients calling because a result appeared in the portal without an explanation

• Billing: patients unsure why they received a bill or what their share is

• Insurance: patients asking whether a visit is covered, or why a claim was denied

Identify patient frustration and service gaps earlier

Conversation analysis can surface repeated complaints, negative sentiment (how a patient feels, based on their words and tone), and points of friction. Sentiment analysis tools add a measure of how patients feel about each topic, not only how often it comes up.

Teams can use these signals to change how they communicate. If many patients sound frustrated when asking about a referral status, the fix may be a clearer text message or a better script, rather than hiring more people. Acting on the pattern early keeps a small annoyance from turning into a formal complaint or a lost patient.

Turn patient feedback into operational improvements

When insights from many conversations are grouped together, healthcare leaders can see which workflow problems come up most often and rank them. A spike in calls about a new pre-visit form tells you something very different from one angry caller.

These findings can feed service reviews and operating decisions. Leaders can bring the top five themes to a monthly meeting, assign an owner to each, and check later whether the calls on that topic went down. The Level AI analytics module is one example of how dashboards turn conversation data into views that teams can share. For a broader look at the tools in this space, see this guide to conversation analytics software.

Customer story: a health and wellness company. A large health and wellness company set out to cut operating costs without lowering support quality. Its director of operations described the goal in a Level AI case study: "We think Level AI can shave off 2 to 3 minutes of the call. That's substantial for us, 20 to 25% of the call time." That was an expected result at the time, not a measured outcome, but it shows how a team can use conversation data to find where time and effort are lost.

Helping Healthcare Staff Deliver More Empathetic Patient Support

Patients do not remember the software. They remember whether the person on the phone was calm, clear, and kind. AI helps most when it takes away the parts of the job that get in the way of that.

Give agents faster access to relevant information

Contact center agents often juggle several screens while a patient waits. AI-powered knowledge assistance listens to the conversation as it happens and shows the agent the most relevant answer, policy, or next step. Agent Assist is the kind of tool built for this.

Guided responses reduce the things patients hate most: being transferred, repeating themselves, and hearing "let me find out" followed by a long silence. Our guide to real-time agent assist explains how live prompts work during a call.

Support consistent and compliant communication

Healthcare staff must follow approved workflows and communication policies, such as how to verify a caller's identity or what can and cannot be said about a clinical question. Real-time guidance can remind agents of the right steps at the right moment, so the patient gets the same answer no matter who picks up.

Three safeguards matter here:

• Verified information: answers should come from approved sources, not guesses

• Privacy protection: patient details should be shared only with people who need them

• Escalation to clinical staff: any question about symptoms, medication, or treatment should go to a qualified clinician, not be answered by an agent or by software

Give staff more time to focus on patients

Much of an agent's day goes to repetitive work: typing notes after a call, searching for the same policy, filling in the same fields. When AI writes a call summary or finds an answer in seconds, the agent can spend that time understanding what the patient is worried about.

AI should support empathy, not replace it. A patient calling about a frightening result wants a person who listens. The goal is to remove the paperwork around that conversation, not the conversation itself.

Customer story: a global healthcare provider. A global healthcare provider used a virtual agent to handle 45% of prescription refill calls without agent help. Calls that did need a person moved to a live agent in under 30 seconds, down from 90 seconds. Routine requests were handled automatically, which left human agents available for patients who needed more attention.

Improving Quality Assurance Across Patient Interactions

Quality assurance (QA) is the process of reviewing conversations to check that staff communicated clearly, followed policy, and treated patients well.

Move beyond manual sampling to broader conversation coverage

Most teams review only a small sample of calls, often a few per agent per month. That sample may miss recurring issues entirely, and it can leave staff feeling that only their hardest calls are picked.

AI can score a much larger share of interactions, potentially all of them. Broader coverage gives leaders a more accurate view of service quality and of the issues that repeat. Our guide on how to audit 100% of healthcare calls walks through what that looks like in practice.

Customer story: HTC (not a healthcare company, but a clear example). HTC, a telecom cooperative, reviewed about 1% of calls by hand before moving to 100% automated scoring with Level AI. One team member said the analytics "uncovers trends automatically versus me trying to find them manually." The same shift, from a thin sample to full coverage, applies to patient-facing teams.

Identify communication, empathy, and compliance patterns

Automated quality evaluations can flag possible policy violations, inconsistent information, and communication problems, such as an agent who skipped identity verification or gave two different answers about the same billing rule.

Flagged interactions may still need human review. A flag is a signal, not a verdict. Complex or sensitive cases, such as a distressed patient or a clinical question, need a person to read the context and decide. Good quality assurance for contact centers keeps people in the loop and lets them correct the scoring.

Turn quality reviews into actionable coaching

QA is only worth the effort if it improves how people work. AI can spot recurring development needs, such as agents who struggle to explain insurance terms, and point to specific calls as examples. This is the idea behind how AI identifies coaching opportunities in contact centers.

Faster feedback helps. A supervisor who can say "here is the call from yesterday, and here is what worked" gets better results than one who reviews a call from last month. Tools for agent coaching connect review findings to coaching sessions, so improvement is tracked and not left to memory.

Identifying Problems in Patient Care Before They Escalate

Most service problems start small. A confusing instruction generates a few calls, then dozens. AI can spot these recurring problems across large volumes of patient interactions while they are still small.

Common examples include:

• Unclear preparation instructions: patients keep asking the same question before a procedure

• Scheduling difficulties: patients call several times to book or move one appointment

• Billing confusion: patients ask what a charge means or why a statement changed

• Insurance friction: patients are unsure about coverage, prior approvals, or denials

• Repeated follow-up requests: patients call back because the first call did not resolve the issue

Treating each complaint as a one-off hides the pattern. When leaders trace recurring issues back to a policy or workflow, one fix can remove hundreds of calls.

Earlier identification supports targeted improvements, so teams fix the few things that cause the most calls first. Our research on repeat contact satisfaction shows how strongly repeat calls affect how people feel about a service.

Improving Healthcare Operations With AI-Driven Insights

Patient insights are also operating insights. The same data that shows what patients are confused about shows where the organization spends time and money.

Reduce avoidable contacts and repetitive work

When teams know the most common inquiry types, they can improve self-service resources (such as web pages and portal messages), clarify communication, and route calls to the right place the first time. If a large share of calls ask for the same document, putting it where patients can find it removes those calls and the manual work behind them.

Improve resolution and service consistency

Conversation insights and agent guidance help teams answer patient questions the same way each time. These are the numbers worth watching:

A shorter call is not always better. A patient with a complex worry may need more time, so handling time should be read together with resolution and patient feedback.

Connect patient experience with operational decisions

Insights from patient conversations can inform staffing, intake, scheduling, and process changes. If calls spike on Mondays after a weekend of unanswered portal messages, that is a scheduling and staffing question as much as a service question. Recurring themes help teams decide which operational changes to make first, and Level AI's healthcare solutions are built around that kind of work.

Strengthening Compliance and Reducing Patient Service Risks

Healthcare conversations carry sensitive information, so compliance (following the laws and internal rules that govern how patient data and communication are handled) is part of patient care.

AI can help identify potential compliance issues and deviations from approved communication workflows, such as a missed identity check or a disclosure that was left out. Broader coverage also improves risk visibility. With manual sampling, a compliance problem that appears in only a small share of calls can go unseen for months. Reviewing many more conversations makes such problems easier to find. See how regulatory compliance monitoring works across contact center conversations.

Four controls should sit around any AI tool that touches patient conversations:

• Access controls: only the right people can see recordings and transcripts

• Data privacy: personal details are protected or removed, in line with rules such as HIPAA, the US law that protects patient health information (see our guide to HIPAA-compliant AI voice agents)

• Audit trails: a record of who reviewed, changed, or overrode a score

• Human review: people confirm flagged cases before action is taken

Automated flags support risk detection. They do not guarantee compliance or remove the need for oversight. Compliance teams still own the decisions.

How Healthcare Organizations Can Measure AI's Impact on Patient Care

You cannot tell whether AI is helping unless you measure it. Start by recording a baseline, which means the starting numbers before you change anything, then compare against actual results over time.

Healthcare organizations should validate improvements against actual results, not against vendor promises or early estimates. Our guide to healthcare voice AI KPIs covers how to build a measurement plan, and the ROI of AI in healthcare call centers explains how to connect those numbers to cost and revenue.

How Level AI Helps Healthcare Organizations Improve Patient Experiences

Level AI helps healthcare contact centers understand patient interactions at scale. Its conversation intelligence and quality assurance capabilities analyze calls, chats, and emails, so teams can see recurring patient concerns, service friction, and chances to improve communication, rather than relying on small samples or survey replies.

Automated QA and coaching workflows help teams evaluate service quality and support agent development, with people able to review and override the results. Conversational insights can then inform changes across patient-facing workflows, from scheduling and intake to billing and follow-up. Level AI is a tool for improving contact center performance and patient service experiences. It does not replace clinical diagnosis or treatment.

See every patient conversation, not just the few you review today.

Level AI helps healthcare organizations gain visibility into patient interactions and contact center quality, so teams can find problems earlier and coach with real examples. Explore how it could work with your phone system, chat tools, and patient records.

Schedule a demo

See every patient conversation, not just the few you review today.

Level AI helps healthcare organizations gain visibility into patient interactions and contact center quality, so teams can find problems earlier and coach with real examples. Explore how it could work with your phone system, chat tools, and patient records.

Schedule a demo