Patient Discharge & Medical Release Experience Survey
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.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
What type of visit were you discharged from?
- Emergency room
- Inpatient hospital stay
- Outpatient surgery or procedure
- Urgent care
- Other
Did you receive written discharge instructions before you left?
- Yes, complete written instructions
- Yes, but they were incomplete or hard to read
- No written instructions, verbal only
- I don't remember
Thinking about your discharge conversation, how much do you agree with each statement?
- The staff explained my diagnosis or reason for the visit in terms I understood
- I understood which medications to take, stop, or change
- I understood the warning signs that would mean I should seek care again
- I knew who to call if I had questions after leaving
- I had enough time to ask questions before I left
Was a follow-up appointment scheduled or clearly arranged before you left?
- Yes, scheduled before I left
- Yes, but I had to schedule it myself afterward
- No follow-up was needed
- I'm not sure if one was arranged
How confident did you feel about managing your recovery or condition at home right after discharge?
How would you rate the overall clarity of your medication instructions specifically?
In the 7 days after leaving, did you have to call the office, visit an ER, or get readmitted because something about the discharge instructions was unclear or wrong?
- No, everything was clear and went fine
- I called with a question but didn't need further care
- I sought additional care (ER visit, urgent care, or readmission)
- Not applicable / no issues arose yet
Reconstruct exactly what happened after the respondent left care: identify the single instruction (medication, activity restriction, warning sign, or follow-up step) that was least clear to them, ask what they actually did when they hit that confusion, and whether it led them to call the office, guess, ignore it, or seek additional care. If they said everything was clear, probe one specific detail (like a medication dose or a warning sign) to verify true understanding rather than general satisfaction. If they sought additional care afterward, get the specific timeline and root cause.
Is there anything about the discharge process you'd want us to change for the next patient?
What is your age range?
- 18-29
- 30-44
- 45-59
- 60-74
- 75+
- Prefer not to say
What is your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Thank you for sharing your experience. Your responses go directly to our care quality team and help us improve discharge instructions and follow-up support for future patients.
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.
이 템플릿을 선택하는 이유
이 템플릿의 설계 목적을 소개합니다. 다른 설문 도구에서는 직접 비교할 만한 템플릿을 찾지 못했습니다.
차별화 포인트
- Includes an AI follow-up interview that reconstructs exactly what happened after discharge and pinpoints the specific moment instructions became unclear, rather than only capturing a static rating
- Combines structured questions (visit type, written instructions received, follow-up appointment status, medication clarity rating, post-discharge ER/office contact) with an open-ended change request, giving both quantifiable and narrative data
- Uses a matrix question to assess agreement across multiple discharge-conversation statements plus an opinion scale for at-home recovery confidence, capturing nuance a single Likert item would miss
- Closes with a transparent message telling respondents their answers go directly to the care quality team, and separately collects age range and gender for segmentation without mixing demographics into the clinical questions
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