AI Healthcare Call Center Software: Top Platforms Replacing Manual Patient Communication [2026]

Multi-practice healthcare call centers handle an average of 2,000 calls per day. The average hold time is 4.4 minutes, nearly five times longer than the Healthcare Financial Management Association's recommended benchmark of 50 seconds. Only 52% of patient issues are resolved on the first call. The financial cost of this friction is real: abandoned calls from long hold times can translate to $45,000 in daily revenue loss at a single center (Dialog Health).

That is the patient-facing side. Behind the scenes, staff spend hours on hold with insurance companies verifying benefits, checking prior authorization status, and following up on claims. Complex healthcare calls cost $8 to $15 per interaction when accounting for HIPAA compliance, clinical training, and multi-system navigation (Wantek). Agent burnout runs at crisis levels, with 74% of customer service representatives at risk and annual turnover reaching 60% (Providertech).

Patients notice. Ninety-six percent of patient complaints involve customer service, and those who experience negative phone interactions are four times more likely to switch providers (Dialog Health). The combination of high costs, staff shortages, and patient dissatisfaction has created a breaking point for health systems, specialty pharmacies, and pharmaceutical patient services teams.

The numbers compound. On average, patients make 3.5 calls for each scheduling need (Dialog Health). With an average handle time of 6.6 minutes per call, a 350-agent center handling 75 calls per agent daily incurs a daily cost of approximately $128,625. The average annual operating cost for a healthcare call center reaches $13.9 million, with 43% allocated to labor alone.

AI healthcare call center software addresses both sides of this problem. These platforms use voice AI, natural language processing, and deep EHR integration to handle patient scheduling, eligibility verification, appointment reminders, refill coordination, and payer communication.

The technology has matured beyond simple IVR menus and scripted phone trees. Modern platforms resolve patient inquiries end-to-end and make outbound calls to payers for benefit verification and authorization, escalating to human agents only for complex exceptions.

Survey data shows that healthcare call center leaders would be satisfied if AI could automate 34% of inbound calls, though some platforms report handling up to 85% (Dialog Health). At a center handling 2,000 calls daily, automating even 34% could yield daily savings of approximately $43,700.

This guide compares the 10 leading AI healthcare call center platforms, covering their capabilities, healthcare-specific features, integration ecosystems, and reported performance data. Whether you manage a health system contact center, run a specialty pharmacy operation, or oversee pharmaceutical patient services, this breakdown identifies which platforms fit your workflows and where gaps remain in the market.

What Does AI Healthcare Call Center Software Do?

AI healthcare call center software is technology that automates voice, text, and digital interactions between healthcare organizations, patients, and payers using artificial intelligence.

These platforms handle two categories of communication.

Inbound interactions. Patients call in for appointment scheduling, appointment changes, billing questions, prescription refill requests, insurance coverage questions, and general inquiries. AI agents understand the request, access relevant patient data through EHR integrations, and resolve the issue in real time.

Outbound interactions. The platform initiates calls or messages for appointment reminders, refill reminders, benefit verification calls to payers, prior authorization follow-ups, care coordination outreach, and patient education campaigns.

Most platforms operate across multiple channels: voice (phone), SMS, web chat, and email. The AI adapts its communication style to each channel while maintaining a unified patient record. A patient who starts a scheduling request by phone and follows up by text receives a consistent experience.

The top three reasons patients call healthcare centers are billing and payments (52%), insurance questions (41%), and medication-related queries (34%) (Dialog Health). AI platforms are designed to handle all three categories without requiring human intervention for standard requests.

This represents a fundamental shift from legacy Interactive Voice Response (IVR) systems. Traditional IVR forces patients through rigid phone menus ("Press 1 for appointments, Press 2 for billing") with no ability to understand natural language or resolve requests autonomously. The result: 84% of healthcare call centers still use IVR, yet transfer rates reach 19% because the systems cannot accurately route calls (Dialog Health). AI-powered systems understand what patients say, access their records, and complete the request without transfers or hold time.

The integration layer is what separates healthcare-specific platforms from general contact center tools. AI healthcare call center software connects to electronic health records (Epic, Cerner, athenahealth, eClinicalWorks), practice management systems for scheduling and billing, pharmacy management systems for prescription data, payer portals for insurance verification and authorization, and CRM systems for patient relationship tracking.

Without these integrations, an AI agent can answer the phone but cannot resolve the patient's issue.

Top AI Healthcare Call Center Platforms

Each platform below is evaluated on voice AI capabilities, healthcare-specific features, integration depth, and reported performance data.

Hyro

Hyro provides conversational AI for health systems, focusing on call center automation and digital patient engagement. The platform uses a three-pronged approach: smart routing to direct calls to the right department, end-to-end resolution for common inquiries, and SMS deflection to shift phone conversations to text-based channels.

Hyro integrates with Epic, Twilio Flex, NICE inContact, Genesys, Five9, and Cisco. This breadth of contact center integrations lets health systems layer Hyro on top of existing phone infrastructure without ripping out current systems. Their data estimates that a 100-person healthcare call center incurs $4 million in annual labor costs, with a 15% IVR misroute rate adding friction on top (Hyro).

Performance data comes primarily from health system deployments. Intermountain Health reported 85% call deflection, 99% hold time reduction, and a 64% decrease in call abandonment rate (Hyro). Contra Costa Health saw a 450% increase in goal completion rate. Hyro also reports that 67% of calls can be deflected to SMS for asynchronous resolution, turning real-time phone conversations into lower-cost text-based interactions.

Clients include Intermountain Health, Baptist Health, Summa Health, Novant Health, Montefiore Medical Center, and Bon Secours Mercy Health.

Best for: Large health systems with existing contact center infrastructure that want to reduce call volume through deflection and self-service.

EliseAI

EliseAI offers VoiceAI for healthcare, claiming to handle 95% of patient inquiries around the clock with zero hold time (EliseAI). The platform supports voice, email, text, and chat communication, providing a unified patient engagement layer.

Healthcare-specific features include smart scheduling with EHR sync, billing and payment processing, prior authorization and prescription management, and automated follow-ups. EliseAI integrates with athenahealth, ModMed, Nextech, eClinicalWorks, and AdvancedMD.

The platform reports a 66% decrease in cost-per-call for healthcare clients. EliseAI holds HIPAA and SOC 2 Type II certifications. Their healthcare product targets specific specialties including Women's Health, Dermatology, Ophthalmology, and Orthopedics.

Best for: Specialty practices and ambulatory groups looking for multi-channel patient engagement across scheduling, billing, and prescription management.

Assort Health

Assort Health describes its platform as "specialty-trained agentic AI" for patient access. The company claims training on 1.2 million clinical edge cases across 55 million patient interactions, producing workflows purpose-built for healthcare rather than adapted from general-purpose models (Assort Health).

Performance claims include a 48% increase in labor capacity, 94% patient satisfaction scores, and what the company calls industry-leading safety ratings. In a Becker's Hospital Review article, co-CEO Jon Wang cited client results: wait times reduced from over an hour to under 30 seconds, and call abandonment rates dropping from nearly 50% to below 10%.

Clients include Cleaver Medical Group, OrthoIndy, and Chesapeake Healthcare.

Best for: Multi-specialty practices and medical groups that need deep customization of scheduling rules, insurance routing, and patient communication preferences.

Commure

Commure positions itself as an AI agent platform for healthcare operations, extending beyond call centers into revenue cycle management, claims processing, and payer portal automation. Their approach uses forward-deployed engineering, embedding teams within health systems to customize AI agent configurations.

Commure's research highlights the scale of the problem: hold times exceeding 4 minutes against a 50-second benchmark, 30% of patients abandoning after a 1-minute wait, and only 50% first-call resolution in typical healthcare contact centers (Commure). They note that labor accounts for roughly 50% of total call center costs, with staffing typically running at 60% capacity.

Their AI agents target specific workflow categories: a Denials Autopilot for denial management, a Claims Processing agent, a Payer Portal agent for insurance navigation, and an Outbound Follow-Up agent for proactive patient outreach. This modular approach lets health systems deploy AI in stages rather than as a single large implementation.

In February 2026, Commure announced Commure Pro, expanding their platform capabilities for health system operations.

Best for: Health systems with complex revenue cycle challenges that want AI covering both patient-facing calls and back-office payer operations.

Infinitus

Infinitus focuses exclusively on payer-facing voice AI, calling itself "the first trusted voice AI platform for healthcare" (Infinitus). While most platforms in this category handle patient calls, Infinitus specializes in making calls to payers and providers on behalf of healthcare organizations.

The platform's AI agents call insurance companies to complete benefit verification, prior authorization inquiries, and claims follow-up. This addresses one of healthcare's most time-consuming tasks: staff spending hours on hold with payers to verify coverage or check authorization status.

The platform operates as both AI agents (fully autonomous calls) and copilots (assisting human staff during calls). This dual model lets organizations automate straightforward benefit checks entirely while keeping human oversight for complex authorization scenarios.

Infinitus has attracted major payer logos including Carelon, UnitedHealthcare, Express Scripts, Humana, OptumRx, Cigna, CVS Caremark, and Aetna. Their newer Agentic AI Member Services Suite extends capabilities to payer-side operations for member inquiries.

Best for: Specialty pharmacies and provider organizations that spend significant staff time calling insurance companies for benefit verification and prior authorization.

Notable Health

Notable Health offers AI agents for patient access, revenue cycle management, and care operations. Their voice AI handles inbound scheduling calls while the broader platform automates referral processing, order transcription, and copay collection.

Published case studies show measurable results across multiple health systems. MUSC Health reported $3.3 million in annual value through Notable Health and 15% of copays collected without staff involvement. Catholic Health achieved over $350,000 in projected annual savings with a 57% call containment rate across 25,000 calls handled. Montage Health reduced referral processing from 14 days to 3 days, automating 97% of in-scope referrals. A Florida health system automated 85% of order transcription, saving 8,000 staff hours per year and allowing 4 full-time equivalents to be upskilled into higher-value roles.

The breadth of these case studies reflects Notable's strategy: AI agents that work across access, revenue cycle, and operations rather than focusing solely on the phone channel.

Best for: Health systems seeking AI that extends beyond call center automation into broader revenue cycle and clinical operations workflows.

Providertech

Providertech provides conversational AI focused on patient communication for healthcare organizations, including providers, payers, and Federally Qualified Health Centers (FQHCs). Their platform handles inbound call management around the clock, appointment scheduling and rescheduling, appointment reminders, and medication refill requests (Providertech).

The company emphasizes accessibility, serving organizations from small physician practices to large health systems. Industry data cited on their platform shows hospitals miss 24% of inbound calls and that 60% of patients on hold too long will hang up (Providertech).

Best for: FQHCs and community health organizations that need straightforward patient communication automation with broad organizational support.

Capacity

Capacity is a conversational AI platform serving multiple industries including healthcare. The platform provides AI-powered answers, workflow automation, and agent assistance tools. In healthcare, Capacity targets patient engagement, appointment management, and predictive outreach.

CEO David Karandish describes the platform's healthcare vision: predictive algorithms that identify patients likely to miss appointments, followed by automated outreach through preferred channels, telehealth alternatives, or transportation coordination (Healthcare IT Today).

Best for: Healthcare organizations looking for a general-purpose conversational AI platform that can be configured for patient engagement alongside other operational use cases.

Talkie.ai

Talkie.ai develops AI voice agents for medical facilities, focusing on automating front-desk phone interactions. Based in Europe, the platform handles appointment scheduling, reminders, and patient inquiries through voice-first AI (Talkie.ai).

Talkie.ai positions itself as purpose-built for healthcare, with voice technology designed for medical terminology and clinical workflows. The platform targets clinics and health systems looking to reduce phone burden on front-office staff.

Best for: Medical facilities and clinics seeking a voice-first AI solution for front-desk phone automation.

Neon Health

Neon Health provides an AI workforce that automates patient access workflows across the full care journey. Unlike platforms that focus solely on patient-facing scheduling or solely on back-office automation, Neon Health's AI workers handle both payer-facing and patient-facing calls through a single platform.

On the payer side, Neon Health's AI workers make voice calls to insurance companies for benefit verification, prior authorization status checks, and claims follow-up. On the patient side, they manage outreach for financial assistance enrollment, onboarding education, refill reminders, and care coordination. This dual capability addresses a gap that most platforms in this category leave open: the administrative workflow that spans from payer verification through patient engagement.

Neon Health is HIPAA compliant, HITRUST certified, and SOC 2 certified. The platform takes a consultative implementation approach, working with teams to map existing workflows before designing AI-powered solutions. Components include voice AI, portal automation, and rules engines combined into bespoke configurations for each customer's systems and data.

Performance benchmarks include 2x faster time-to-therapy and 80% cost reduction compared to manual processes. Neon Health raised $6 million in September 2025, led by NFX, and is a Y Combinator graduate.

Best for: Specialty pharmacies, pharmaceutical patient services teams, and health systems that need AI handling both payer communication (benefit verification, prior authorization, claims) and patient engagement (onboarding, adherence, financial assistance) within a single workflow.

AI Healthcare Call Center Platform Comparison

The comparison reveals a clear market segmentation. Most platforms focus on patient-facing communication: scheduling, reminders, and billing inquiries. A smaller group (Infinitus, Commure, Neon Health) extends into payer-facing operations. Only Neon Health treats both payer-facing and patient-facing AI as co-equal capabilities within a single platform.

Use Cases by Organization Type

Different healthcare organizations face distinct call center challenges. The right platform depends on which workflows consume the most staff time and create the most patient friction.

Health systems handle the broadest mix: scheduling, triage routing, appointment reminders, referral coordination, and billing inquiries. With only 19% of healthcare call centers operating around the clock and 11% of patient calls occurring outside regular hours (Dialog Health), after-hours coverage is a recurring gap. Platforms with deep EHR integration and high call-volume capacity fit best. Hyro, EliseAI, Assort Health, and Notable Health have demonstrated health system deployments with published results. Neon Health serves health systems that also need payer-facing capabilities for benefit verification and authorization.

Specialty pharmacies spend significant staff time on payer calls for benefit verification, prior authorization, and financial assistance enrollment. A single benefit verification call can take 20 to 45 minutes when accounting for payer hold times, authentication, and data entry. Multiply that by hundreds of patients per day, and the labor cost becomes the dominant operational expense.

Infinitus and Neon Health address the payer-calling burden directly. Providertech and EliseAI support the patient communication side with scheduling, refill reminders, and adherence outreach.

Pharmaceutical patient services teams manage hub programs that coordinate patient access to specialty medications. These programs require both payer engagement (benefit verification, prior authorization, copay program enrollment) and patient outreach (onboarding, education, adherence support).

The workflow challenge is bridging these two sides: once coverage is confirmed, the patient needs to be contacted about their copay options, onboarding schedule, and first fill logistics. Neon Health's end-to-end coverage from payer verification through patient engagement makes it relevant for pharma hub operations where the workflow spans both sides.

Revenue cycle teams need eligibility verification, claims follow-up, and denial management. Denied claims cost an average of $118 to reprocess, and prior authorization delays create downstream revenue leakage. Commure and Notable Health offer AI agents for these back-office workflows. Infinitus and Neon Health handle the payer-communication component that feeds the revenue cycle.

How Should You Evaluate AI Call Center Software for Healthcare?

Evaluate AI call center software for healthcare by assessing accuracy guarantees, HIPAA compliance documentation, EHR integration depth, edge case handling protocols, and measurable ROI through cost-per-interaction benchmarks.

Healthcare introduces regulatory requirements, clinical complexity, and patient safety considerations that general-purpose contact center tools do not address. Five criteria matter most.

Accuracy and hallucination risk. This is the most critical factor. General AI models can generate plausible but incorrect information. In healthcare, an inaccurate insurance verification or a wrong appointment time has direct patient impact.

Ask vendors about their accuracy benchmarks, how they handle edge cases, and what guardrails prevent fabricated responses. Request data on error rates from production deployments, not just demo environments. Assort Health, for example, publishes its training dataset size (1.2 million edge cases) as a proxy for accuracy, while other vendors cite containment rates or first-call resolution percentages.

HIPAA compliance and BAA requirements. Every platform handling protected health information must be HIPAA compliant and willing to sign a Business Associate Agreement (BAA). Additional certifications like HITRUST and SOC 2 indicate deeper security commitments.

HITRUST requires a formal risk assessment and third-party audit. SOC 2 Type II verifies that controls operate effectively over time, not just at a single point. Neon Health holds all three certifications (HIPAA, HITRUST, SOC 2), providing the full compliance stack that risk-conscious health systems require.

Integration depth versus API-only connections. A platform with deep EHR integration that reads and writes patient records directly enables end-to-end resolution. An API-only connection may require manual steps to close the loop. Ask whether the integration is bidirectional, real-time, and whether it supports the specific EHR version your organization runs.

Edge case handling and live agent escalation. Every healthcare call center encounters situations that fall outside standard workflows: a patient with unusual insurance, a scheduling conflict with clinical requirements, or a caller who needs a human. The escalation path and how much context transfers to the human agent matters as much as the automation rate.

ROI framework. The clearest measure is cost per interaction before and after implementation. Healthcare call centers average $4.90 per call (Dialog Health). AI platforms that handle routine calls at a fraction of that cost while improving first-call resolution deliver measurable returns.

Build your ROI model around four metrics: call volume deflection (percentage of calls fully handled by AI), cost per interaction (before and after), patient satisfaction scores (HCAHPS or internal surveys), and staff retention (reduced burnout should reduce turnover from the current 60% annual rate). The most convincing vendor evaluations run a pilot on a defined call subset, measure these four metrics over 60 to 90 days, and extrapolate results to full deployment.

What Is the Difference Between Payer-Facing and Patient-Facing AI?

Payer-facing AI calls insurance companies for coverage and authorization verification, while patient-facing AI handles scheduling, reminders, and billing questions directly with patients.

Payer-facing AI navigates insurance company phone systems, holds on IVR trees, and extracts structured data from verbal responses. A benefit verification call might require the AI to dial an 800 number, navigate a phone menu, authenticate with member and provider details, ask specific coverage questions, and record the responses in a structured format. This is high-volume, repetitive work that consumes hours of staff time daily. Infinitus and Neon Health specialize in this category.

Patient-facing AI handles direct communication with patients. These calls require empathy, health literacy awareness, multi-lingual support, and careful consent management. A patient calling to reschedule an appointment may also mention a medication concern, express frustration about a bill, or need guidance on pre-procedure preparation. Patient-facing AI must navigate these contextual shifts while maintaining a positive care experience. Most platforms in this guide (Hyro, EliseAI, Assort Health, Notable Health, Providertech, Capacity, Talkie.ai) focus primarily here.

The technical requirements diverge sharply. Payer-facing AI must navigate third-party phone systems that change without notice, handle long hold times, and extract specific data points from unstructured conversations. Patient-facing AI must understand natural language variation, respond with appropriate empathy, and hand off to clinical staff when the conversation moves beyond administrative topics.

Most platforms specialize in one direction. This makes sense: the engineering challenges are different, the training data is different, and the regulatory considerations are different. Organizations that need both capabilities typically run separate solutions for each, creating integration gaps where payer verification data does not flow cleanly into patient communication workflows.

This gap creates real operational problems. When a specialty pharmacy confirms that a patient's insurance covers a medication but requires a $500 copay, someone needs to contact the patient about financial assistance options.

If the payer-facing AI and patient-facing AI are separate systems, that handoff requires manual data transfer, staff intervention, and often a 24 to 48-hour delay. For time-sensitive therapies, that delay can mean missed treatment windows.

Neon Health bridges this gap. By building AI workers that handle both payer and patient communication, the platform creates a unified workflow from insurance verification through patient engagement. A single system verifies coverage, identifies the copay amount, checks financial assistance eligibility, and contacts the patient with their options. For specialty pharmacies and pharmaceutical patient services teams, this eliminates the disconnect between confirming a patient's coverage and getting them started on therapy.

Healthcare call center AI has moved past the pilot phase. Platforms in this guide handle millions of interactions across health systems, specialty pharmacies, and pharmaceutical operations. The technology reduces cost per interaction, eliminates hold times for routine calls, and improves first-call resolution.

The market is fragmenting along specialization lines. Some platforms excel at patient scheduling for health systems. Others focus on payer communication for specialty pharmacies. A few are building end-to-end workflows that span both sides. The right choice depends on where your call center spends the most staff hours and where patient experience suffers most.

When evaluating platforms, prioritize accuracy over automation rate. A platform that handles 70% of calls correctly is more valuable than one claiming 95% automation with unverified accuracy. Look for healthcare-specific training data, production case studies with named clients, and compliance certifications that match your regulatory requirements.

Explore how Neon Health's AI workforce automates both payer and patient communication.

Frequently Asked Questions

What is AI healthcare call center software?

AI healthcare call center software uses voice AI, natural language processing, and workflow automation to handle patient and payer calls. These platforms manage scheduling, benefit verification, appointment reminders, and billing inquiries without requiring human agents for routine interactions. Most integrate with EHR systems to access and update patient records in real time.

How much does AI healthcare call center software cost?

Pricing varies by vendor and model. Traditional healthcare call centers spend $4.90 per call on average (Dialog Health). Complex healthcare calls requiring HIPAA compliance and clinical knowledge cost $8 to $15 per interaction (Wantek). AI platforms typically reduce cost per interaction by 60-90%. Most enterprise platforms use custom pricing based on call volume and workflow complexity.

Is AI healthcare call center software HIPAA compliant?

Most established platforms in this category are HIPAA compliant and offer Business Associate Agreements (BAAs). Look for additional certifications like HITRUST and SOC 2 Type II, which indicate deeper security and audit commitments. Always verify compliance documentation and ask about data handling practices before implementation.

What is the difference between payer-facing and patient-facing AI in healthcare call centers?

Patient-facing AI handles scheduling, reminders, and billing questions with empathy and plain language. Payer-facing AI navigates insurance company phone systems and portals to complete benefit verification, prior authorization, and claims follow-up. Most platforms specialize in one direction. Few, like Neon Health, handle both within a single platform.

Can AI call center software integrate with existing EHR systems?

Yes. Most platforms integrate with major EHR systems like Epic, Cerner, athenahealth, and eClinicalWorks. Integration depth varies from basic API connections to deep bidirectional sync that updates patient records in real time. Ask vendors about specific compatibility with your EHR version and whether integrations are pre-built or require custom development.

Key Takeaways

• Healthcare call centers average 4.4-minute hold times and 52% first-call resolution, costing up to $45,000 per day in lost revenue from abandoned calls at a single center.

• AI healthcare call center software handles inbound and outbound communication across voice, SMS, chat, and email, integrating with EHR and payer systems to resolve interactions end-to-end.

• Most platforms specialize in either patient-facing communication (scheduling, reminders, billing) or payer-facing communication (benefit verification, prior authorization). Few address both.

• Health systems should prioritize platforms with deep EHR integration and high-volume call handling. Specialty pharmacies and pharma patient services teams need payer-facing AI for benefit verification and prior authorization.

• When evaluating platforms, prioritize accuracy over automation rate, verify HIPAA/HITRUST/SOC 2 compliance, and test edge case handling and live agent escalation paths before signing.

• Neon Health is the only platform in this guide that addresses both payer-facing and patient-facing call center automation within a single workflow, bridging the gap between insurance verification and patient engagement.

• Cost per interaction is the clearest ROI metric: healthcare call centers average $4.90 per call, and AI platforms report reducing this by 60-90% for routine interactions.

Sources

• Dialog Health. "Healthcare Call Center Statistics." Accessed February 2026. https://www.dialoghealth.com/post/healthcare-call-center-statistics

• Wantek. "Call Center Pricing: Average Cost Per Call & Outsourcing." 2025. https://www.iwantek.com/blogs/news/call-center-pricing-average-cost-per-call-outsourcing

• Providertech. "Conversational AI for Healthcare Call Centers." 2024. https://www.providertech.com/conversational-ai-for-healthcare-call-centers/

• Forbes. "Recent Research Suggests Something Has to Change in the Contact Center Space." 2023. https://www.forbes.com/sites/adrianswinscoe/2023/07/26/recent-research-suggests-that-something-has-to-change-in-the-contact-center-space/

• Hyro. "AI Call Center for Healthcare." Accessed February 2026. https://www.hyro.ai/healthcare/call-center-ai/

• EliseAI. "HealthAI." Accessed February 2026. https://eliseai.com/healthai

• Assort Health. Self-reported data from company website. Accessed February 2026. https://www.assorthealth.com/

• Becker's Hospital Review. "How Agentic AI Is Ending Hold Music and Reinventing Patient Access." October 2025. https://www.beckershospitalreview.com/strategy/how-agentic-ai-is-ending-hold-music-and-reinventing-patient-access/

• Commure. "How AI Agents Are Transforming the Healthcare Call Center." Accessed February 2026. https://www.commure.com/blog/how-ai-agents-are-transforming-the-healthcare-call-center

• Infinitus. Self-reported data from company website. Accessed February 2026. https://www.infinitus.ai/

• Notable Health. Self-reported data from company website. Accessed February 2026. https://www.notablehealth.com/

• Healthcare IT Today. "Healthcare AI and Patients: 2026 Health IT Predictions." January 2026. https://www.healthcareittoday.com/2026/01/14/healthcare-ai-and-patients-2026-health-it-predictions/

• Talkie.ai. Self-reported data from company website. Accessed February 2026. https://talkie.ai/

• Envera Health. "Behind the Metrics." Accessed February 2026. https://enverahealth.com/resources/knowledge-center/white-paper/behind-the-metrics

• American Health Connection. "2024 Healthcare Call Center Customer Service." 2024. https://americanhealthconnection.com/2024-healthcare-call-center-customer-service/