Top 8 AI Triage Nurses in 2025
Nov 10, 2025
The healthcare industry faces unprecedented challenges in 2025, with patient volumes soaring and medical staff stretched beyond capacity. Emergency departments worldwide report wait times exceeding four weeks for routine appointments, while triage nurses struggle to manage overwhelming call volumes with limited resources. This is why more and more healthcare organization are adopting AI triage nurses. They are automation systems that are revolutionizing how healthcare organizations assess patient symptoms, prioritize care needs, and route individuals to appropriate medical services.
These AI agents leverage machine learning algorithms and LLMs to conduct preliminary symptom assessments with remarkable accuracy. Recent studies demonstrate that machine learning models can achieve up to 75.7% accuracy in predicting Emergency Severity Index acuity assignments, significantly outperforming traditional manual triage methods that average around 59.8% accuracy. Moreover, these systems operate continuously without fatigue, handling millions of patient interactions annually while maintaining consistent clinical standards.
In this comprehensive guide, we'll explore the nine leading AI triage nurse platforms transforming patient care in 2025, examining their unique capabilities, integration features, and real-world impact on healthcare delivery.
What Is an AI Triage Nurse?
An AI triage nurse represents advanced artificial intelligence technology designed to automate and enhance the patient assessment process, traditionally performed by human nurses. These systems analyze reported symptoms, medical histories, and risk factors through conversational interfaces, whether voice, text, or chat, to determine the urgency of each patient's condition and recommend appropriate care pathways.
Unlike simple symptom checkers, AI triage nurses employ probabilistic modeling and machine learning algorithms trained on vast datasets of clinical outcomes. They ask clinically relevant follow-up questions, identify patterns across multiple symptoms, and provide evidence-based recommendations aligned with established medical protocols such as Schmitt-Thompson guidelines. The technology has proven particularly valuable in reducing unnecessary emergency department visits, optimizing resource allocation, and ensuring patients receive timely access to the right level of care.
Healthcare organizations implementing AI triage solutions report dramatic improvements in operational efficiency, with some achieving up to 30% reductions in patient wait times and 43% decreases in charting time by 2025.
The 8 Best AI Triage Nurses in 2025: TL;DR
We’ll cover each solution in detail below, but here’s a quick overview to help your healthcare organization choose the right AI triage partner.
Sully.ai - Best all-in-one AI medical team for complete patient journey automation: Sully.ai brings together six specialized AI healthcare agents (Nurse, Receptionist, Scribe, Medical Assistant, Coder, and Pharmacy Tech) to manage intake, triage, documentation, and follow-up. With seamless Epic and Athena integration, multi-language support, and real clinical accuracy proven to reduce diagnostic errors by 85%, Sully.ai stands as the gold standard for healthcare automation in 2025.
Infermedica: A highly validated triage engine using probabilistic modeling to recommend the right care level. Trusted worldwide for accuracy and regulatory rigor, though its focus remains limited to symptom checking rather than end-to-end workflow automation.
Clearstep.health: A market leader in digital self-triage for U.S. health systems. Known for strong patient satisfaction and proven Schmitt-Thompson reliability, though less adaptable beyond self-triage and routing use cases.
Mednition (KATE AI): A nurse-built solution for emergency departments, providing real-time AI insights to identify high-risk patients. Outstanding in ER-specific use cases, but not designed for full patient journey automation.
Paratus Health: A promising Y Combinator-backed startup specializing in AI voice intake. Great for small practices seeking conversational pre-visit data collection, but less mature in broader clinical integration.
Teneo.ai: An enterprise-grade conversational AI platform focused on large-scale contact center automation. Excellent for automating healthcare call volumes but lacks deep clinical reasoning capabilities.
ScribeHealth.ai (HealOS): A strong contender for AI-powered documentation and intake. Ideal for reducing physician burnout through automated note generation but primarily a scribing solution, not a true triage platform.
ClearTriage: A trusted, protocol-based telephone triage system. Reliable and easy to use, but largely manual and lacking the advanced AI reasoning or automation of newer entrants.
Next, we’ll dive into how each of these AI triage nurse platforms is transforming patient assessment, improving care navigation, and reshaping healthcare operations in 2025.
1. Sully.ai: The Comprehensive AI Medical Team
Sully.ai is the most comprehensive AI healthcare automation platform, offering far more than a stand-alone triage tool. Rather than addressing one fragment of the workflow, Sully.ai delivers an integrated suite of six specialized “AI medical employees”: AI Nurse, Receptionist, Scribe, Medical Assistant, Coder and Pharmacist; designed to work together across the full patient journey, from check-in through prescription fulfilment. Given its comprehensive solution across the entire patient journey, the company is trusted by over 400+ healthcare organizations.
In particular, the AI Triage Nurse is engineered as the first touch point for patients contacting the system, gathering symptom data, screening for red flags, prioritising urgency and routing each case appropriately, while handing off to human clinicians for review. By doing so, Sully.ai aims to reduce delays in care delivery, eliminate unnecessary clinician burden, and elevate the accuracy and safety of triage decisions.
Features
Sully.ai’s AI Triage Nurse comes equipped with a number of advanced capabilities, supported by the broader platform’s ecosystem of agents. Some of the most noteworthy features:
Intake across multiple channels (phone, web chat/portal, SMS): The AI Triage Nurse verifies identity and preferred language before proceeding.
Adaptive symptom collection: asks contextual follow-up questions (onset, severity, modifiers, location, associated symptoms) and supports photo attachments where permitted.
Red-flag screening: automatically detects urgent symptoms (e.g., chest pain, difficulty breathing) and escalates immediately to the appropriate clinical pathway.
Prioritisation & routing: assigns suggested urgency (urgent, soon, routine, admin) and routes structured summaries to clinician inboxes, care-team queues or scheduling modules per organisation rules.
EHR integration & documentation: logs triage summaries, attachments, timestamps, and updates the patient chart so clinicians have full context in their workflow.
Multi-language support & accessibility: supports real-time multilingual education and communications, enabling broader patient populations to engage.
Modular platform: As part of the Sully.ai agent ecosystem, the Triage Nurse works in concert with other agents (Scribe, Coder, etc) delivering automation from intake to documentation and follow-up.
Pros
Sully.ai offers several strong advantages for healthcare organisations seeking to modernise triage and patient intake workflows. The AI Triage Nurse is part of a larger agent ecosystem, organisations can implement a single platform rather than multiple point tools. This reduces vendor complexity, integration overhead and training burden. Also, the documented accuracy and efficiency gains offer significant cost savings and improved patient/clinician experience.
Key benefits include:
Significant reduction in triage cycle time and improved patient routing (fewer mis-triages, faster escalation of urgent cases).
Lower administrative burden for nursing/triage teams, freeing them to focus on higher-value tasks and clinician engagement.
Seamless integration with major EHR systems (e.g., Epic, AthenaOne) meaning less disruption and faster deployment.
Real-world adoption: the platform is used across hundreds of organisations, demonstrating scalability and reliability.
Multi-language and multilingual support broadens access for diverse patient populations, helping reduce health disparities.
Safety features: human-in-the-loop review, explicit escalation of red flags, audit trails and compliance controls help mitigate risk.
Cons
No product is perfect even leading platforms have trade-offs. For Sully.ai’s AI Triage Nurse (and the broader platform) you should keep in mind the following limitations and considerations:
Change management: Adoption of AI in clinical settings often faces resistance; even a great tool requires staff training, process redesign and ongoing monitoring of performance and safety.
Data & monitoring requirements: To maintain accuracy and compliance, organisations must carefully govern data flows, audit logs, consent management and human-in-the-loop oversight. Sully.ai provides features for this, but the responsibility still lies with the provider.
2. Infermedica
Infermedica has built a strong reputation in the virtual triage and symptom-assessment space. With over a decade in the market and rollout across more than 30 countries, the company focuses specifically on helping health systems, insurers and telemedicine providers route patients to the right level of care rather than simply generating diagnoses. Its core offering is a clinically validated symptom-checker and triage engine that supports both patient-facing and clinician-support use cases.
In the context of AI triage nurses, Infermedica is a solid option, especially when your priority is front-door symptom assessment and care navigation, though it does not aim (in its current form) to replace full workflow automation or comprehensive agent ecosystems.
Features
Infermedica’s triage solution packs in a variety of capabilities that make it attractive for organisations looking to improve patient flow and digital front-door access:
A large medical knowledge base: includes 900+ conditions, 1,800+ symptoms, 340+ risk factors (per company data).
Probabilistic inference engine: goes beyond simple rule-based decision trees to assess multiple symptoms and risk factors and deliver one of five triage levels: Self-care, Consultation, Consultation within 24 hours, Emergency, Emergency Ambulance.
Multichannel and multilingual support: the triage module is available in 24–26+ languages and can be used via websites, portals, apps, or call-centres.
API & integration-friendly: supports pre-visit symptom collection, medical history, specialist recommendation, configurable UI, analytics dashboards.
Certification and regulatory posture: Certified as a Class IIb medical device under the MDR in the EU, which indicates attention to clinical safety and regulatory compliance.
Analytics & workflow reporting: built-in dashboards show interview times, triage outcome distributions, symptom and risk-factor statistics, etc.
Pros
Infermedica offers several tangible advantages, particularly if your organisation’s use case prioritises intake/triage rather than full end-to-end automation.
Strong clinical credibility: The probabilistic engine, large knowledge base and certification suggest a higher level of validation than many simpler triage tools.
Flexible deployment: Because it’s API-first and modular, Infermedica can slot into existing systems (web portals, contact centres, call lines) without requiring wholesale workflow redesign.
Multilingual and global reach: For organisations operating in multi-language or international contexts, the multiple‐language support is useful.
Data-rich insights: The analytics capabilities allow organisations to monitor triage performance, user behaviour and symptom/risk trends.
Efficiency gains: There are case studies of reduced intake time, fewer inappropriate urgent referrals, and improved patient navigation.
Cons
There are trade-offs to keep in mind before selecting Infermedica for your triage nurse use-case.
Narrower workflow scope: Though strong for triage/intake, Infermedica does not aim (at least publicly) to cover the full gamut of patient-journey automation (documentation, billing, pharmacy tech, etc) in the way some broader platforms do.
Implementation and customisation still required: While it is modular and configurable, organisations must still invest in integration, user-experience design, routing rules, clinician oversight, and change-management to make the triage solution operationally effective.
Clinical acuity limits: Some use cases (e.g., very complex specialty medicine, inpatient/emergency-department workflows) may push beyond the core design of a virtual triage tool, meaning more human oversight remains required.
Competitive ceiling: Because the tool is well defined in triage/intake, organisations looking for a complete AI medical staff automation strategy may find it lacking compared to platforms positioned as “AI teams” across multiple agents and functions.
3. Clearstep.health
Clearstep has positioned itself as a broadly-adopted digital self-triage solution in the U.S., especially for health systems seeking to provide a self-service “digital front door” for patients. Its Smart Access Suite offers a web-based (and app/portal) symptom check and routing tool built on clinical triage protocols. According to the company, it serves many health-system websites and reaches a large patient base.
While Clearstep is a solid choice for patient facing symptom assessment and routing, it may not deliver the same breadth of workflow automation (documentation, full care-team AI agents) as more comprehensive platforms.
Features
Clearstep’s solution includes a number of features aimed at reliable, efficient triage and care navigation:
Evidence-based foundation: Uses clinical content derived from the Barton Schmitt triage protocols (used in ~95% of U.S. nurse call-centres) which gives the platform a credible clinical starting point.
Natural language input + fewer questions: Patients can type free-text symptoms; the system asks roughly 10-15 follow-up questions and then recommends next steps.
Care-routing and scheduling integration: Beyond symptom checking, Clearstep routes patients to appropriate care endpoints (self-care, tele-visit, primary care, urgent care, emergency) and supports booking options online.
White-label & brandable: Healthcare organisations can embed the tool in their website/portal and customise logos, colours and routing paths so the patient-experience aligns with their brand.
Analytics and workflow optimisation: Provides data on patient intent, triage outcomes, routing efficiency, call-centre deflection, and mobile vs web usage, enabling operational insights.
24/7 self-service access: The platform is designed for patient entry outside traditional hours, supporting multi-channel (web, chat, portal) access and deflecting routine queries away from staffed lines.
Pros
Clearstep presents several compelling advantages for organisations focused on digital self-triage and patient access:
Clinical credibility in triage routing. Because the triage logic is built on established protocols (Schmitt) and validated for routing accuracy, health systems may feel more confident in the clinical basis of the tool.
Strong digital front-door capability. For organisations wanting to offer patients a quick online symptom-check + routing + booking flow, Clearstep provides a relatively polished solution.
White-label branding + configurable. Being able to tailor the experience to your organisation’s brand and routing structure helps adoption and coherence with existing patient journeys.
Operational efficiency benefits. The analytics and self-service capabilities mean call-centres and triage lines can potentially offload volume, freeing staff time for higher-acuity work. (e.g. blog data: call centre deflection, improved routing)
Mobile-first UX and engagement. The company reports shorter question flows and higher completion rates, which is important for patient digital uptake.
Cons
Scope focused primarily on self-triage/intake. Clearstep’s strength is in front-door triage and routing, but it may lack the deeper clinical workflow support (documentation, coding, post-visit automation) that full-suite platforms offer.
Integration and configuration required. To make the routing and booking flows truly seamless, organisations must invest effort in mapping endpoints, integrating scheduling/EHR systems, defining care-path endpoints, and change-management. While implementation is reported as relatively fast, internal effort remains.
Dependence on patient digital literacy and access. Because the tool is self-service and digital, populations with low tech-access or preference for human contact may not engage as well, so fallback workflows and staff training remain necessary.
Not designed for full continuity of care. If your goal is to automate not just triage but also documentation, coding, pharmacy flows, follow-up care-management, you may find the platform less complete than vendors that emphasise “AI medical team” models.
Clinical acuity & complexity bounds. For very high-acuity settings (complex specialty triage, inpatient flows, ICU/ED red-flagging) the tool may not be sufficient on its own; human triage and oversight will still be required.
4. Mednition - Emergency Department AI Excellence
Mednition is a company focused specifically on the challenges facing emergency departments (EDs). Their flagship product, KATE AI, is designed to assist triage nurses by integrating with existing workflows and leveraging EHR data to identify high‐risk patients early, help prioritise acuity, and improve patient flow.
In typical use-cases, hospitals deploy KATE in their ED triage zones where time, acuity identification and throughput are critical. For organisations with significant ED burden, this tool can function as an “AI triage nurse assistant” supplement rather than a full spectrum patient-intake system.
Features
Mednition’s KATE AI platform offers several specialised capabilities for ED triage and risk stratification:
Real-time acuity and risk detection: KATE analyses structured and unstructured EHR data (e.g., chief complaint, vitals, narrative notes) to identify high-risk patients at triage.
Seamless integration & minimal workflow disruption: The system is designed to slot into current nurse triage workflows and major EHRs (such as Epic) without requiring major procedural changes.
Clinical-data engine & NLP: KATE utilises natural language processing (NLP) to process free-text alongside structured data and supports retrospective analytics and quality improvement.
Early sepsis and deterioration detection: One of the modules (KATE Sepsis) focuses on early detection of sepsis at triage, before lab results, using hundreds of clinical concepts.
Proven deployment metrics: According to Mednition, KATE has supported nearly 4 million ED patient visits and is used across multiple hospital systems.
Pros
When considering Mednition’s KATE AI for an ED triage-assistant role, the following advantages stand out:
Focused ED triage specialism: For organisations whose biggest challenge is ED throughput, acuity assignment and risk stratification, KATE offers a tailored and mature solution.
Minimal disruption: Because it is built to fit existing workflows and major EHRs, adoption may be less burdensome than tools requiring full workflow redesign.
Supporting nursing staff: The system helps triage nurses feel supported with an “extra set of eyes,” which may improve job satisfaction, retention, and decision-confidence.
Recognised credibility: KATE AI was awarded “Best in Show” at HIMSS25 Emerge Innovation Experience in the Hospital Capacity Crisis category.
Cons
However, there are trade-offs to keep in mind when evaluating Mednition for broader triage nurse automation:
Narrower scope: KATE focuses on ED triage and risk detection rather than full patient-intake, documentation, coding, post-visit workflows or multi-agent automation. If your ambition is an “AI medical team” covering all patient journeys, you may find gaps.
Requires high data maturity: Effective performance relies on access to rich EHR data (including narrative/free text) and existing triage processes. Less digitised EDs may face integration and data-quality hurdles.
Implementation oversight needed: Although workflow disruption is minimised, proper change-management, clinician engagement, alert calibration (to avoid alert fatigue) and monitoring remain necessary (especially noted in nursing leadership commentary).
Focused on acute/pre-hospital triage: For outpatient, self-triage or non-ED settings, the solution may not be optimally suited, meaning other competitors may offer better fit in those domains.
Cost and scale considerations: Large-system ED deployments may justify the investment, but smaller clinics or non-ED triage programmes may find the business case less compelling given the specialised nature of the platform.
5. Paratus Health - AI Voice Intake Specialists
Paratus Health is a relatively new player, established 2024, in the health-tech space that focuses exclusively on automating the patient intake process using conversational voice and chat AI agents. Founded by Stanford AI students Tannen Hall and Pablo Bermudez‑Canete, the company secured backing from Y Combinator and quickly positioned itself as a digital front-door tool for outpatient clinics seeking to reduce manual staff burden and improve pre-visit readiness.
Their core value proposition: patients engage with a voice or chat AI before an appointment, the AI gathers symptoms and history, flags concerns, and generates a structured summary that integrates with EHRs like Epic or AthenaOne so that clinicians walk into visits better prepared.
Features
Conversational Voice & Chat Intake: Patients can engage via phone, SMS, or web/chat interface; the system collects symptom history, demographics, insurance verification and other intake items.
Evidence-Based Protocols: The platform uses over 500 established clinical protocols (including those from the Schmitt‑Thompson Group) and cross-checks against medical literature to ensure triage recommendations and intake questions follow recognised clinical patterns.
EHR & Practice-System Integration: The AI agent connects to major EHR/practice systems (Epic, Athena) to deliver structured summaries, SOAP notes, and conversation transcripts directly into the clinician workflow, reducing context-switching.
24/7 Coverage & Multi-Channel Access: The AI assistants are available around the clock, handling inbound patient calls, chats and texts, performing intake outside typical office hours and freeing staff from initial screening tasks.
Analytics & Operational Metrics: The system reports patient response rates, new patient growth (+20% in some reported cases), admin cost reduction, and deployment pace (some clients live in under 3 weeks) according to company data.
Pros
Improved intake efficiency: By automating the front-door steps (symptom/interview/intake) clinics can free up staff and reduce time wasted on repetitive triage and history-gathering.
Better clinician readiness: With a structured summary delivered ahead of the visit, doctors arrive with richer context, enabling more efficient visits and potentially better care decisions.
Scalable 24/7 access: AI agents don’t sleep, they can engage patients outside business hours, reduce call-waiting and no-show risk, and thus improve patient experience and practice throughput.
Rapid deployment: According to Paratus, many practices can go live in a few weeks with minimal disruption, which is appealing for clinics seeking faster ROI.
Cost-savings potential: Early data suggest practices saw substantial reductions in administrative/call-centre staff load (~-60%) and increased new-patient growth (~+20%) in some cases.
Cons
Limited to intake/triage front-door: Paratus excels at pre-visit intake and patient access, but it does not (at least publicly) offer full downstream workflows like chart documentation, coding, pharmacy fulfilment or full “AI medical team” automation.
Dependence on outpatient setting readiness: The value case is clearest for clinics with sufficient call/phone intake volume and patient engagement; smaller practices or those without robust digital workflows may need extra change-management.
Integration & data-governance requirements: Although the company emphasises rapid deployment, clinics still need to integrate with their EHR/practice management systems, define protocols and ensure data quality and oversight, which takes internal effort.
Human oversight is still needed: Even with automated intake, clinicians must review summaries and decisions; the AI doesn’t replace clinical judgement, especially in complex cases.
Scale vs specialty limitations: For very high-acuity specialties (emergency, inpatient) or large health-system workflows, Paratus’ outpatient-intake focus may not suffice; other platforms with broader clinical workflow support may be more suitable.
6. Teneo.ai
Teneo.ai specialises in conversational and contact-centre automation in healthcare settings, offering a multilingual, enterprise-grade AI platform that handles high volumes of patient or member interactions across voice, chat and digital channels. The company emphasises scalability, operational cost savings, 24/7 service availability and regulatory compliance (HIPAA/GDPR) for healthcare call-centres and triage support.
While not positioned purely as a clinician-triage-nurse replacement, Teneo’s offering supports telephone/virtual triage flows by automating routine symptom checks, scheduling, routing and patient engagement, making it a meaningful option for organisations looking to augment triage call centres rather than replace full clinical workflow automation.
Features
Teneo.ai brings a set of features oriented toward large-scale patient interaction automation, especially for front-door triage, call volumes and customer service workflows:
Multi-channel automation: voice, chat, digital apps, IVR; enabling around-the-clock patient access and symptom/check-in routing.
Multilingual support: The platform supports dozens of languages and dialects, enabling organisations to engage diverse patient populations.
High interaction accuracy: The company reports up to ~99% accuracy in patient interactions for healthcare customer-service scenarios and strong containment of routine queries.
Enterprise-grade compliance and security: With features such as PII/PHI redaction, audit logs, data-residency controls, HIPAA/GDPR alignment and full traceability for voice/text interactions.
Triage/triage-nurse support: Specific modules or use-cases focus on automating routine symptom checks and triage routing tasks, freeing nurses to focus on higher-acuity cases.
Rapid deployment and integration: The platform is designed to integrate with existing call-centre and contact-centre systems, with claims of deployment in weeks rather than months.
Pros
Scalability & volume handling: For organisations with large call volumes or multi-channel patient access needs, Teneo supports high throughput and can reduce reliance on manual staff.
Standardisation & consistency: Automated triage/symptom-routing can ensure more consistent patient handling, fewer mis-routes, and lower variation than purely human-staffed lines.
Cost and efficiency gains: According to company case-studies, call-handling costs drop, agent hours saved increase, wait times reduce and patient satisfaction improves (e.g., a global provider saved ~$6M annually and saved ~36,000 agent hours).
Regulatory/technical readiness: The platform’s emphasis on HIPAA/GDPR, audit trails and enterprise security reduces risk for large healthcare organisations.
24/7 patient access & front-door triage: By making symptom-checking, scheduling and routing available at all hours and across channels, the platform helps organisations handle demand surges, off‐hours access and routine triage outside business hours.
Cons
Not specifically a clinician-triage-nurse replacement: While Teneo handles routing, symptom check inbound calls and front-door triage logic, it is less focused on full clinical decision support, detailed medical history intake, deep documentation workflows, or specialty‐triage like inpatient/ED levels.
Workflow and data maturity required: To realise full value, organisations need strong interoperability, well-defined routing logic, integration with EHRs/contact-centres, and change-management. Less mature settings may struggle with deployment.
Dependence on routine use cases: The cost-savings and efficiency are strongest for higher-volume, lower-acuity interactions (call-centre type). For highly complex triage, human clinicians remain essential and the tool may not reduce workload as much.
Brand differentiation & clinical depth: Compared to triage-specialist platforms that focus on symptom-assessment and clinical protocols tailored for nursing, Teneo’s strength is in conversational automation and call-centre workflows. Organisations seeking deep clinical triage may find alternate vendors more tailored.
Change management & governance: Even though deployment is marketed as rapid, successful adoption requires governance, staff training, oversight of AI decisions, monitoring for accuracy and ensuring fallback to human review when needed.
7. ScribeHealth.ai (HealOS)
HealOS began its journey as ScribeHealth.ai with the goal of solving one of the most pressing burdens in healthcare: documentation. Today, the company has evolved beyond a pure scribing‐tool to an automation platform that supports intake, documentation, workflow tasks and more.
When it comes to its AI triage nurse offering, HealOS offers pre-visit patient intake, structured history capture, and symptom collection capabilities, though its core strength remains clinical documentation and downstream workflow automation rather than replacement of in-person nurse triage.
Features
HealOS brings a set of features tailored toward documentation, intake and workflow automation:
Advanced voice recognition and transcription: The system captures patient-clinician conversations (voice or audio) and converts them into structured medical notes in real time, with reported up to 98% accuracy for general medical terminology and about 95% for specialty jargon.
Multi-format documentation support: Generates SOAP, HPI, MSE formats, and supports specialty-specific templates (e.g., physical therapy, pediatrics) to match diverse clinical settings.
Broad EHR integration: Compatible with major EMR/EHR systems (e.g., Epic, Cerner, Athena, Nextech) via browser extension or direct integration, enabling notes to populate directly into charts.
Pre-visit patient intake and symptom/history collection: The platform supports intake workflows where patients provide history and symptoms ahead of visit, thereby enabling structured data for clinician review.
Compliance & security: Fully HIPAA-compliant hosting, bank-level encryption, automatic deletion of audio recordings post‐transcription, and business-associate agreements included.
Expanded workflow automation: Beyond scribing, HealOS now supports AI Receptionist, benefit verification, pre-authorization workflows and other front-office/administrative tasks.
Pros
Major time savings: Clinicians report substantial reductions in time spent charting and documenting, allowing more face-time with patients and less after-hours work.
High accuracy & reliable notes: The reported accuracy levels (98% general, 95% specialty) are strong for AI documentation tools, reducing clinician editing time.
Strong integration: Because it ties into existing EHR systems and supports multiple formats, the disruption to workflow is relatively less than a “rip-and-replace” solution.
Beyond triage: broader automation: The ability to handle intake, documentation, front desk automation, and other administrative tasks helps organisations reduce tool-sprawl and vendor complexity.
Affordability & scalability for smaller practices: The platform appears well-suited for smaller clinics seeking to improve efficiency without massive infrastructure investment.
Cons
Primary focus on documentation vs clinical triage: While the platform supports pre-visit intake and symptom/history gathering, it is not positioned primarily as a true triage-nurse AI engine (e.g., real-time risk stratification, routing of acute cases) in the way some dedicated triage vendors are.
Front-office & intake workflows require readiness: To benefit fully from the intake plus documentation automation, practices need solid patient-portal usage, workflows that capture pre-visit data, and staff willing to adapt to new onboarding/consent processes.
Complex cases still require human oversight: Especially for high-acuity, specialty or inpatient settings, manual review remains essential. The AI may not replace nursing judgement for complex triage decisions.
Integration & governance needed: While the tool integrates well, organisations still must ensure data governance, clinician review workflows, and training for optimal use, implementation still takes effort.
Less emphasis on triage-routing vs full care-navigation: For organisations whose primary need is triage routing (e.g., symptom check + care‐level recommendation + scheduling) the documentation-centric focus may mean some feature gaps compared to triage-specialist platforms.
8. ClearTriage
ClearTriage is a well-established nurse telephone-triage software solution, introduced in 2014 and now used by thousands of nurses across more than 800 organizations, processing millions of triage calls each year.
The core value of ClearTriage lies in structured decision-support for triage nurses using the Schmitt-Thompson Group telephone triage protocols (adult & pediatric). Rather than being a full-end automation of all patient workflows, it focuses on the triage-call scenario: symptom assessment, urgent-vs-routine determination, patient education and disposition documentation.
Features
ClearTriage offers a number of features targeting the telephone triage workflow:
Evidence-based protocol library: Uses Schmitt-Thompson protocols (hundreds indexed by adult/pediatric, by anatomical group) which are peer-reviewed and updated annually.
Intuitive nurse interface: A web-based tool where nurses select a chief complaint, walk through a checklist of questions, document the disposition and hand off patient education (text/email) as needed.
Flexible EHR/EMR compatibility: ClearTriage allows documentation to be copied/pasted or embedded into many EHRs without heavy integration effort.
Customization & reporting: Supports tailoring of protocols, dispositions and provides robust reporting (e.g., “protocols used”, “dispositions given”).
Versions for office-hours and after-hours triage: Specialised protocol sets and pricing for after-hours operations, catering to call centres or 24/7 triage environments.
Pros
Clinical reliability & standardisation: By leveraging gold-standard protocols (Schmitt-Thompson), it offers nurses structured decision support and helps reduce variability in triage decisions.
Ease of adoption: Because it can integrate in a lightweight fashion (copy/paste, launch within many EHRs) and is designed for call-centre nurses, implementation burden is relatively lower than full-suite AI triage platforms.
Affordability: The licensing model (especially for office-hours triage) is comparatively cost-effective, making it accessible for clinics, practices, and call centres.
Strong support for triage‐call workflows: For practices that receive significant inbound symptom calls, ClearTriage gives nurses a focused tool built exactly for that scenario.
Customisation & reporting: Organisations can tailor protocols/dispositions to their patient population and monitor triage outcomes via reports, supporting quality improvement.
Cons
Scope is narrow: ClearTriage is focused specifically on telephone nurse triage (symptom-assessment calls, dispositions). It is not designed to automate full patient-journey workflows (intake → triage → documentation → coding → pharmacy) or advanced AI routing/regulation.
Less emphasis on AI and conversational automation: The tool is more of a decision-support system for human nurses rather than a conversational-AI triage nurse operating autonomously. For organisations seeking full digital front-door automation (chatbots/voice agents), ClearTriage may feel limited.
Dependence on nurse staffing and calls: Because the model assumes nurse-led triage calls, environments with low call volume or that aim to shift more to self-service digital triage may not maximise its value.
Integration still needed: While the tool supports many EHRs and offers flexible documentation workflows, organisations will still need to plan for protocol customisation, nurse training, and workflow change management.
Limited routing/triage beyond phone context: If your priority includes triage across multiple channels (chat, mobile app, multilingual voice agents) or fully automated routing without a nurse in the loop, ClearTriage may not offer the full breadth of modern AI triage platforms.
The Future of AI Triage Nurses
The evolution of AI triage nurse technology continues and this system is being adopted quickly by healthcare organizations across the US and the world. The quick evolution and adoption are driven by advances in LLMs, improved clinical validation, and growing acceptance among healthcare providers and patients. Several key trends are shaping the future landscape.
Enhanced Personalization
AI triage systems will increasingly incorporate patient-specific data from electronic health records, genetic information, and historical health patterns. Rather than relying solely on symptom descriptions, these platforms will provide context-aware recommendations based on comprehensive medical histories, previous diagnoses, laboratory results, and medication regimens.
Seamless Healthcare Ecosystem Integration
Future AI triage nurses will connect more deeply with the broader healthcare technology ecosystem, automatically coordinating with scheduling systems, telemedicine platforms, care management tools, and population health analytics. This integration will enable true continuity of care, with triage recommendations flowing seamlessly into appointment booking, virtual consultations, and follow-up care plans.
Expanded Clinical Applications
While current AI triage focuses primarily on acute symptom assessment, future applications will extend to chronic disease management, mental health support, medication adherence monitoring, and preventive care coaching. These expanded capabilities will transform AI from reactive triage tools into proactive health management partners.
Improved Transparency and Explainability
As healthcare organizations demand greater accountability, AI triage platforms are developing enhanced explainability features that show exactly how recommendations are generated. This transparency addresses the "black box" problem that currently affects 67% of healthcare AI models, building trust with both clinicians and patients. (10)
Choosing the Right AI Triage Nurse for Your Organization
Selecting an AI triage nurse solution requires careful evaluation of your organization's specific needs, existing technology infrastructure, patient population characteristics, and strategic goals. Consider these essential factors during your decision-making process.
Clinical Accuracy and Validation
Prioritize platforms with rigorous clinical validation, preferably including peer-reviewed research demonstrating safety and effectiveness. Look for solutions based on established medical protocols like Schmitt-Thompson guidelines and those achieving certification under medical device regulations such as MDR Class IIb.
Integration Capabilities
Evaluate how seamlessly potential solutions integrate with your existing EHR systems, call center technologies, patient portals, and other healthcare IT infrastructure. The ideal AI triage nurse should enhance workflows rather than creating additional administrative burdens or requiring staff to juggle multiple disconnected platforms.
Scalability and Reliability
Choose solutions proven to handle your current patient volumes while offering headroom for growth. Review uptime statistics, disaster recovery capabilities, and support responsiveness to ensure the platform will perform reliably when patients need assistance most.
Compliance and Security
Verify that any AI triage solution maintains strict HIPAA compliance, employs robust data encryption, and follows industry best practices for protecting sensitive health information. Request evidence of security audits, penetration testing results, and business associate agreements.
User Experience
AI triage nurses provide little value if patients find it confusing or clinicians resist adopting it. Evaluate platforms through pilot programs, gathering feedback from both patients and staff about ease of use, satisfaction, and perceived value.
Frequently Asked Questions (FAQ)
1. What exactly is an AI triage nurse?
An AI triage nurse is a digital system that uses artificial intelligence to assess patient symptoms, determine the urgency of care, and route individuals to the appropriate healthcare service. Unlike basic symptom checkers, modern AI triage nurses use advanced natural language processing, probabilistic modeling, and clinical protocols to guide patients safely and accurately through the care process.
2. How accurate are AI triage nurses compared to human triage?
Recent studies show that AI triage systems can achieve up to 75-80% accuracy in predicting patient acuity levels, outperforming traditional manual methods that average around 60% accuracy. However, human oversight remains essential, most healthcare organizations use these systems as decision-support tools rather than replacements for clinical judgment.
3. What types of healthcare organizations use AI triage nurses?
AI triage nurses are used across a broad range of healthcare environments, including:
Primary care practices and urgent care centers
Emergency departments and hospital systems
Telemedicine providers and call centers
Outpatient clinics and specialty practices
Large systems often integrate AI triage within their broader digital front door strategy, while smaller clinics use it to automate intake and reduce administrative workload.
4. Are AI triage systems safe and HIPAA-compliant?
Yes. Most leading platforms, including Sully.ai, Infermedica, and Clearstep, maintain full HIPAA compliance and employ encryption, anonymization, and secure data-handling practices. Some, like Infermedica, are also certified under MDR Class IIb regulations in the EU, indicating medical-device-grade safety standards.
5. Do AI triage nurses replace human nurses?
No. These systems are designed to augment, not replace, human clinical staff. AI triage nurses handle routine assessments and information gathering, freeing licensed nurses to focus on complex cases and patient care. Sully.ai’s model, for instance, includes human-in-the-loop safeguards to ensure clinical oversight at every stage.
6. What are the main benefits of adopting an AI triage nurse?
Organizations typically report:
30-40% reduction in wait times and call-center volumes
Faster patient routing and reduced mis-triage incidents
Lower administrative workload for nurses and front-desk teams
Improved patient satisfaction, particularly through 24/7 availability and multilingual support
7. How do AI triage nurses integrate with EHR systems?
Modern AI triage platforms integrate directly with major EHR systems like Epic, Cerner, and AthenaOne via APIs or extensions. Sully.ai, for example, sends structured summaries and triage results directly into clinician workflows, so data is instantly available without duplicate entry or switching between platforms.
8. What’s the difference between self-triage tools and AI triage nurses?
Self-triage tools (like early symptom checkers) typically provide static, rule-based recommendations. AI triage nurses, on the other hand, use conversational AI and dynamic reasoning to adapt follow-up questions based on responses, offering a more clinically accurate and personalized assessment.
9. Which AI triage nurse platform is best overall?
While each platform serves a specific niche, Sully.ai stands out in 2025 as the most comprehensive, end-to-end AI medical platform. It goes beyond triage to manage the full patient journey, from intake and symptom assessment to documentation, coding, and prescription workflows, making it the best all-around solution for healthcare organizations seeking full operational automation.
10. What does the future hold for AI triage nurses?
By 2026 and beyond, AI triage systems will evolve toward personalized, predictive, and fully integrated care management tools. Expect deeper connections with telemedicine platforms, chronic-disease monitoring, mental-health screening, and AI agents that proactively coordinate follow-up care, transforming triage from a one-time assessment into continuous patient support.
References
https://pubmed.ncbi.nlm.nih.gov/37838714/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134957/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236733/
https://www.himss.org/resources/artificial-intelligence-healthcare
https://www.aap.org/en/practice-management/telephone-triage-protocols/
