헬스케어·임상

Measures clinicians' reporting behaviors, system usability, barriers to completeness, and safety culture around adverse event and near-miss reporting. Designed to surface actionable improvements for patient safety and quality leaders.

Measures how comfortable patients are with AI tools that listen, transcribe, and summarize clinical visits, and what would make them trust those tools more. Built for health systems and clinics piloting AI documentation. An AI follow-up interview digs into the specific moment or topic that made a patient hesitant, going beyond a simple comfort score.

A periodic self-assessment covering physical health, sleep, stress, activity levels, and emerging symptoms — used by clinics, employers, or wellness programs to spot concerns early. An AI follow-up interview digs into whatever symptom or concern the respondent flagged as most pressing, capturing duration, severity, and impact that a checkbox can't.

A pre-treatment or progress-check survey for occupational therapy clients that maps real difficulty with daily living tasks, pain during activity, need for assistance, and personal priorities for regaining independence. An AI follow-up interview digs into the single hardest task to surface what's really blocking it and what a good outcome would look like, giving therapists richer intake data than a checklist alone.

Captures a respondent's dental visit history, oral hygiene habits, past procedures, and comfort level with dental care, for use by clinics, insurers, or researchers studying access and prevention. An AI follow-up interview digs into the real reasons behind dental anxiety or delayed care, going beyond a checkbox answer.

바이오테크 및 헬스케어 분야 실험실 전문가를 대상으로 최근 30~90일간의 경험을 바탕으로 실험실 정보 시스템의 사용성, 데이터 정확성, 워크플로 효율성을 평가합니다.

Assesses caregiver strain, care coordination challenges, and support needs among adult caregivers of children receiving medical care to inform improvements in family-centered services.

자가 평가 건강, 활력, 수면, 스트레스, 증상 심각도, 기존 질환을 포괄하는 전인적 건강 점검입니다. 사람들이 실제로 어떻게 지내고 있는지 추적하는 병원, 고용주, 웰니스 프로그램을 위한 것입니다. AI 후속 인터뷰가 응답자가 가장 심각하다고 표시한 증상이나 질환을 깊이 파고들어, 다음 방문 전에 그 영향과 이미 시도해 본 것들을 밝혀냅니다.

Captures a new or returning patient's health background, current medications, allergies, and family history before a clinical visit, with an AI follow-up that clarifies vague or incomplete symptom and condition details so clinicians get a fuller picture without a rushed in-office conversation.

Captures which health conditions run in a patient's family, which relatives are affected, and how concerned the patient is about their own risk. Built for primary care, genetic counseling, or preventive health intake. An AI follow-up interview digs into the single most concerning condition — who had it, how severe, and whether it has already changed the patient's screening or lifestyle habits.

Measures how easy, clear, and efficient the patient registration and check-in process felt — from forms and insurance verification to staff interactions — with an AI follow-up that digs into the specific friction point that slowed a patient down or made them repeat information. Built for clinics, hospitals, and practice managers evaluating intake workflows.

Measures oncology patient perceptions of care access, appointment scheduling, provider coordination, and navigation support services. Designed for healthcare systems seeking to identify actionable gaps across the oncology care journey.

Measures dietary patterns, nutrition goals, purchasing behavior, and barriers to healthy eating. Use to segment customers, identify unmet needs, and inform food product or service development.

A short pre-visit intake for veterinary clinics that captures pet basics, visit reason, and recent symptom changes before the appointment, with an AI follow-up that reconstructs the timeline and severity of the primary concern so the vet walks in prepared instead of starting from scratch.

Confirms that a new group therapy member understands confidentiality limits, voluntary participation, and what to expect, while gauging comfort with sharing in front of others. An AI follow-up interview surfaces the specific worries behind low comfort or confusion so facilitators can address them before the first session. Built for therapists and group practices running intake.

진료 전 환자의 현재 건강 상태, 만성 질환, 복용 약물, 알레르기, 가족력, 생활 습관 위험 요인을 수집합니다. 체크박스 대신 가장 중요한 질환이나 증상의 실제 이야기를 재구성하는 AI 후속 질문을 포함합니다. 진료 전 병력 수집을 표준화하려는 병원, 원격 진료 접수, 건강 연구 팀을 위해 만들어졌습니다.

Assesses whether patients left the hospital genuinely prepared to manage recovery at home — covering instruction clarity, medication confidence, and follow-up care arrangements. An AI follow-up interview digs into what actually happened after discharge, especially for patients who needed unplanned care, to surface where the handoff broke down.

Tracks what actually happened to graduates of a nursing, allied health, or clinical training program after they left — employment status, licensure pass-through, field relevance, and pay — with an AI follow-up that reconstructs the real job-search story behind the numbers. Built for program directors, career services, and accreditation reporting.

Captures how environmental conditions like air quality, extreme heat, and pollution are affecting people's health and daily choices, for public health teams and clinicians planning outreach. An AI follow-up interview digs into the respondent's most pressing environmental health concern to surface real symptoms, triggers, and what would actually help.

Measures patient-provider alignment across clinical, communication, and access dimensions to identify mismatch drivers and care preferences. Designed for healthcare organizations, insurers, or researchers seeking to improve provider matching and patient retention.