헬스케어·임상

A patient-facing needs assessment capturing menopause symptom burden, care access barriers, and service preferences to inform care model design and resource prioritization.

Measures how recent reductions in Medicare telehealth coverage are affecting beneficiaries' access to care, out-of-pocket costs, and health outcomes. Built for health systems, patient advocacy groups, and policy researchers tracking rollback effects, with an AI follow-up that reconstructs one specific care disruption in the patient's own words.

진료 예약, 확정, 알림 과정에 대한 환자의 만족도를 평가하여 불편 요소를 파악하고, 커뮤니케이션 시점을 최적화하며, 예약 부도(노쇼) 비율을 낮추기 위한 조사입니다.

Assesses teamwork, communication openness, staffing adequacy, and response to mistakes in a pharmacy setting, modeled on AHRQ's patient safety culture framework. Built for pharmacy managers and quality leads, with an AI follow-up interview that reconstructs a specific near-miss or error to surface the real contributing factors behind the ratings.

Measures how patients experience each stage of a dental visit — booking, wait times, pain management, cost transparency, and trust in the dentist — and what drives their willingness to recommend the practice. Built for dental practices and group practices benchmarking patient experience, with an AI follow-up that uncovers the real story behind low or high recommendation scores, especially around anxiety and trust.

검진 전후로 환자의 구강 위생 습관, 증상, 치과 방문 이력을 파악하며, 치과, 보험사, 공중보건 프로그램을 위한 자료입니다. AI 후속 인터뷰는 보고된 통증이나 우려 사항을 심층적으로 파악하여 체크리스트만으로는 놓칠 수 있는 세부 정보를 드러내고, 임상의가 진료를 분류하고 우선순위를 정하는 데 도움을 줍니다.

Collects chief complaint, symptom severity, medical history, and current medications before an appointment so clinicians walk in prepared. An AI follow-up interview reconstructs the timeline and context behind the patient's main concern — onset, triggers, and what's already been tried — that a checkbox history alone can't capture.

Measures how satisfied clinicians are with AI-assisted clinical documentation tools — accuracy, trust, time saved, and workflow fit — for health systems and vendors evaluating scribe or note-drafting technology. An AI follow-up interview reconstructs a specific recent encounter where the tool helped or failed, surfacing concrete fixes that a satisfaction score alone can't show.

A pre-session intake for massage and bodywork clients that captures health history, contraindications, areas of tension, and treatment goals so therapists can plan a safe, effective session. An AI follow-up interview digs into the client's primary concern — onset, triggers, and what's already been tried — so therapists walk in with context instead of a blank form.

For healthcare organizations evaluating their patient portal's digital experience. Measures navigation ease, records access, secure messaging satisfaction, and Net Promoter Score to identify priority UX improvements.

Captures how patients experienced a recent visit — wait times, communication, staff respect, and overall likelihood to recommend — with a best-worst prioritization of improvement areas and an AI follow-up that digs into the specific moment behind their recommendation score. Built for clinics, hospitals, and practice managers tracking care quality.

Measures patient comfort, consent preferences, and privacy concerns when sharing health data across providers, insurers, and digital health tools. Ideal for healthcare organizations, privacy teams, and compliance officers seeking to understand patient attitudes toward data sharing.

Measures everyday brushing and flossing habits, common symptoms like gum bleeding or sensitivity, satisfaction with dental visits, and the real barriers keeping people from care. Built for dental practices, oral care brands, and public health researchers. An AI follow-up interview digs into the specific symptom or barrier each respondent flagged, uncovering the actual reasons behind it instead of a generic checkbox.

Measures whether clients actually understood the informed consent process before starting therapy — confidentiality limits, fees, and their right to ask questions — rather than just whether they signed a form. Built for clinics and individual practitioners auditing their intake process, with an AI follow-up that digs into exactly what was unclear and what would have made it clearer.

Measures user onboarding experience, plan adherence behaviors and drivers, and self-reported health outcomes for digital health product teams seeking to improve retention and demonstrate impact.

Benchmarks how staff in your medical office perceive teamwork, communication, error reporting, and leadership support for safety, modeled on AHRQ's patient safety culture domains. An AI follow-up interview digs into the most recent near-miss or error staff witnessed and what stopped or helped them report it.

Evaluates the clarity, duplication, and effort associated with healthcare patient intake forms across digital and paper modalities. Designed for healthcare administrators seeking actionable insights to reduce patient burden and improve the check-in experience.

Measures how much people actually pay for healthcare, how financially burdensome those costs feel, and whether cost causes them to delay or skip care. Built for health plans, providers, employers, and benefits teams tracking affordability; the AI follow-up interview reconstructs the real story behind a specific delay-or-skip-care decision instead of a checkbox answer.

Measures skin type, top concerns, and satisfaction with current skincare products and routines, plus what actually drives purchase decisions. An AI follow-up interview reconstructs any recent skin reaction in detail — what product was involved, what happened, and what the person did next — so findings reflect real experience, not guesswork.

Captures how much pet owners actually spend on veterinary care, medications, insurance, and routine upkeep, where the biggest cost pressures sit, and whether cost ever delays treatment — with an AI follow-up that unpacks a real recent expense decision instead of a hypothetical one.