헬스케어·임상

Measures patient perceptions of post-visit support access, health education clarity, and care coordination quality. Designed for healthcare organizations seeking to identify gaps in patient communication and follow-up processes.

A confidential check-in for schools and campus health teams to spot patterns in student sleep, stress, nutrition, and access to care before they become crises. An AI follow-up interview explores the biggest wellbeing concern in the student's own words, surfacing what support would actually help rather than just a severity score.

Measures patients' experiences with insurance prior authorization, capturing process clarity, wait times, care impacts, and pain points to identify systemic improvement opportunities.

입원, 외래, 응급, 원격의료 방문에서 통증 관리, 의료진 소통, 퇴원 준비도 전반에 걸친 환자 만족도를 측정합니다.

Tracks how clinicians are adjusting to shifting multi-state licensure requirements and controlled-substance prescribing rules for telehealth, including care disruptions and compliance burden. An AI follow-up interview reconstructs a specific recent case where a rule change affected a patient encounter, surfacing details a closed-ended survey would miss.

Assesses daily living challenges, home safety, care coordination, and support priorities among community-dwelling older adults to inform program planning and resource allocation.

Captures how clearly patients understood their discharge instructions, medications, and follow-up care after being released from a hospital, ER, or outpatient procedure. An AI follow-up interview digs into the specific point where instructions were unclear, so care teams can fix the discharge process before it causes a readmission or complication.

Evaluates patient access barriers, session comfort, perceived therapeutic outcomes, and format preferences for teletherapy. Designed for healthcare organizations and telehealth platforms seeking to improve virtual care delivery.

Measures patient satisfaction across appointment access, provider communication, follow-up timeliness, and digital touchpoints for healthcare organizations seeking actionable improvement insights and NPS benchmarking.

Captures how patients experience scheduling, wait times, communication, and results delivery during routine or diagnostic medical exams. Includes a best-worst trade-off to prioritize service improvements and an AI follow-up interview that reconstructs what actually happened during the visit — especially around how results were explained — rather than just a satisfaction score.

Measures student stress levels, workload, sleep habits, and use of mental health support, then identifies the biggest barriers keeping students from asking for help. Built for student affairs, wellness centers, and academic advisors; the AI follow-up reconstructs a specific recent moment a student needed support but didn't seek it, and what would have changed that.

Captures how patients experience the wait between referral and treatment — expectations vs. reality, communication quality, anxiety, and health impact — for clinics and health systems managing waiting lists. An AI follow-up interview digs into the specific moment communication broke down or worked well, surfacing fixes that satisfaction scores alone can't show.

Measures how well patients understand medical information across print, digital, and telehealth channels. Designed for healthcare communicators seeking to identify comprehension barriers and optimize plain-language materials.

Understand how self-directed health consumers choose, combine, and abandon supplements and wearables — what they spend, which data or advice actually changes their behavior, and where an AI follow-up interview surfaces the real story behind their most recent stack change instead of the tidy version.

예비 가정이 어린이집 견학을 어떻게 경험했는지, 그리고 등록까지 여전히 무엇이 걸림돌이 되고 있는지를 파악합니다. 가정의 등록 가능성 뒤에 숨은 구체적인 망설임을 깊이 파고드는 AI 후속 인터뷰가 포함되어 있어, 원장님이 가정이 다른 곳을 결정하기 전에 정확히 무엇을 해결해야 하는지 알 수 있습니다.

A CAHPS-style survey for dental insurance plans, measuring how easy it was to find a dentist, get timely care, and get help from customer service, plus an overall plan rating. An AI follow-up interview digs into the story behind that rating so plan administrators know exactly what to fix.

Captures how smoothly parents moved through enrollment — from first inquiry through submitting health, immunization, and emergency paperwork — and what drove their choice of provider. An AI follow-up interview reconstructs the exact moment registration got confusing or stalled, so admissions and health-compliance teams know what to fix first.

예약 접근성, 임상의 커뮤니케이션, 방문 후 후속 관리, 전반적 만족도에 걸친 환자 경험을 평가하여 실행 가능한 진료 품질 개선 사항을 파악합니다.

For behavioral health practices and clinics considering a group therapy offering, this survey gauges patient interest, preferred format and focus, and the barriers that hold people back. An AI follow-up interview digs into the real reasons behind someone's interest or hesitation so program design reflects what patients would actually attend.

Measures how patients interpret, understand, and respond to lab results and reference ranges in online portals. Identifies usability barriers and preferred features to improve portal design and health literacy support.