모든 템플릿

Referral-to-Treatment Intake Experience Survey

Measures how patients experience the intake journey between referral and first treatment — communication clarity, wait-time transparency, and booking friction — for clinics and healthcare providers managing referral-to-treatment pathways. An AI follow-up interview reconstructs exactly where the process broke down or worked well, beyond a satisfaction score.

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질문 13개 · 약 7분
Q01
메시지

Thanks for taking a few minutes to share your experience so far. Your responses are completely confidential and anonymized. We'd like to understand what it was like between your referral and your first appointment or treatment — there are no right or wrong answers. This should take about 5 minutes.

Q02
객관식필수

How were you referred for your current treatment pathway?

  • GP referral
  • Self-referral
  • Referred by another specialist
  • Online or digital referral form
  • Other
Q03
객관식필수

How long ago were you referred?

  • Less than 2 weeks ago
  • 2-4 weeks ago
  • 1-3 months ago
  • 3-6 months ago
  • More than 6 months ago
Q04
의견 척도필수

How clear was the communication you received about what would happen next after your referral was submitted?

척도: 15
최소:Not clear at all최대:Extremely clear
Q05
매트릭스필수

Please rate each part of your intake experience so far.

4개 행 × 5개 열
  • Time to first contact from the service after referral
  • Clarity of information about expected wait times
  • Ease of reaching someone with questions
  • Reassurance provided while you waited
: Poor · Below average · Average · Good · Excellent
Q06
평점 척도필수

How would you rate the ease of booking your first appointment?

범위: 15
최소:Very difficult최대:Very easy
Q07
객관식

Did any of the following happen during your intake process? Select all that apply.

  • No delays or issues
  • Appointment was rescheduled
  • Referral was lost or had to be resubmitted
  • Long wait for initial contact from the service
  • Unclear next steps at some point
  • Other
Q08
의견 척도필수

Based on your intake experience so far, how likely are you to recommend this service to someone else needing similar care?

척도: 010
최소:Not at all likely최대:Extremely likely
Q09
AI 인터뷰

Reconstruct the respondent's actual timeline between referral and now: what communication they received, when, and from whom. If they reported any delay, rescheduling, or unclear next steps, probe exactly what happened, how they found out, and what they had to do to move things forward. Anchor especially on gaps between the clarity rating and the recommend-likelihood score — if communication was rated poorly but they'd still recommend the service (or vice versa), ask why.

Q10
단문형

What one change would have made your wait between referral and treatment easier?

Q11
객관식

Which age group do you fall into?

  • Under 18
  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65+
  • Prefer not to say
Q12
객관식

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q13
메시지

Thank you for sharing your experience. Your answers help this service identify where the referral-to-treatment process needs to improve, and will be reviewed alongside other patients' feedback to guide changes to communication and scheduling.

포함된 기능

  • AI 후속 질문

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 주의력 확인 장치

    성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.

  • AI가 작성한 문안

    문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.

  • 자동 리포트

    응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

  • Purpose-built to measure the referral-to-treatment journey itself (communication clarity, wait-time transparency, booking friction) rather than just collecting patient details
  • Combines structured questions (multiple choice, opinion scale, matrix, rating) with an AI follow-up interview that reconstructs the respondent's actual timeline and where the process broke down
  • Captures concrete friction points via a 'select all that apply' checklist and a short-text question asking what one change would have eased the wait, giving both quantifiable and narrative data
  • Includes recommend-likelihood and demographic questions plus opening/closing chat messages, so it fields as a complete, ready-to-run survey rather than a bare form

Jotform

Psychedelic Experience Intake Form Template

This is a clinical pre-session intake form for a specific treatment modality (psychedelic therapy), used to collect patient background and health information before a session begins. It is not designed to measure how patients experienced the referral-to-treatment journey, wait times, or booking friction. Useful mainly as a data-collection form rather than an experience/satisfaction survey.

잘하는 점

  • Healthcare-specific template with fields tailored to a clinical intake context
  • Drag-and-drop form builder with standard field types (checkboxes, text, signature, etc.)

아쉬운 점

  • Static form with fixed fields — no adaptive follow-up questioning to explore why an answer was given
  • Focused on collecting patient background data, not on reconstructing or scoring the referral-to-treatment experience
  • No mechanism to probe communication breakdowns or booking friction the way an AI follow-up interview would

SurveyMonkey

Client Intake Form Template

A generic client intake form meant to onboard a new client and gather contact/service details, not a survey about the experience of moving from referral to treatment. It could be manually adapted for healthcare use, but as published it isn't scoped to wait-time transparency, communication clarity, or booking friction. It's a fielding-ready form, but for a different purpose than an experience study.

잘하는 점

  • Simple, quick-to-deploy template on a well-known survey platform
  • Broadly customizable for various client-onboarding use cases

아쉬운 점

  • No adaptive AI interviewing — follow-up questions require manual branching logic set up in advance
  • Not built around healthcare referral pathways or wait-time/booking-friction measurement specifically
  • No automated per-response quality scoring or auto-generated diagnostic reports

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