모든 템플릿

Lab Requisition Process Experience Survey

Measures how smoothly clinical and lab staff can submit, track, and resolve issues with lab test requisitions — covering form clarity, turnaround time, and error rates. An AI follow-up reconstructs exactly what went wrong on a recent problematic requisition instead of relying on vague complaints, giving lab operations teams concrete fixes to prioritize.

샘플 질문

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질문 13개 · 약 7분
Q01
메시지

Thanks for taking a few minutes to share your experience with our lab requisition process — whether you order tests, collect specimens, or manage the paperwork, your feedback helps us fix real friction points. Your responses are completely confidential and anonymized. About 5 minutes.

Q02
객관식필수

Which best describes your role?

  • Physician
  • Nurse or clinical staff
  • Laboratory technician
  • Administrative or scheduling staff
  • Other
Q03
객관식필수

In the last 30 days, how often have you submitted a lab requisition?

  • Daily
  • A few times a week
  • About once a week
  • A few times a month
  • Rarely or never
Q04
매트릭스필수

Rate the current lab requisition process on each of the following.

5개 행 × 5개 열
  • Clarity of the requisition form or order-entry screen
  • Accuracy of the test codes and required fields
  • Specimen labeling and collection instructions
  • Turnaround time from submission to results
  • Integration with your EHR or lab information system
: Poor · Fair · Good · Very good · Excellent
Q05
의견 척도필수

In the last 30 days, how often has a lab requisition you submitted been rejected, delayed, or sent back for correction?

척도: 15
최소:Never최대:Very often
Q06
순위 매기기

Rank these issues from the biggest problem to the smallest in your day-to-day work with lab requisitions.

  1. Missing or incomplete patient information
  2. Illegible or unclear handwriting
  3. Incorrect test codes selected
  4. Specimen labeling errors
  5. Delayed turnaround time
  6. Duplicate or redundant orders
드래그하여 순위 지정
Q07
AI 인터뷰

Reconstruct the most recent time a lab requisition you submitted was rejected, delayed, or needed correction: what test was ordered, exactly what went wrong, and who or what caused the delay (form design, missing info, system issue, staffing). Ask what specifically would have prevented it, and if they say the process is generally smooth, probe for the one step they still find annoying or slow.

Q08
점수 배분필수

You have 100 points to allocate across ways to improve the lab requisition process. Give more points to what would help most.

  • Staff training on the requisition process
  • Better order-entry forms or software
  • EHR/lab system integration improvements
  • Additional lab staffing
  • Standardized test code reference guides
100점 배분
Q09
숫자

On average, roughly how many hours pass between submitting a routine lab requisition and receiving results?

Q10
의견 척도

How likely are you to recommend your organization's current lab requisition process to a colleague at another facility?

척도: 010
최소:Not at all likely최대:Extremely likely
Q11
객관식

What type of facility do you primarily work in?

  • Hospital
  • Outpatient clinic
  • Independent or reference lab
  • Long-term care facility
  • Other
  • Prefer not to say
Q12
객관식

How many years have you worked in your current role?

  • Less than 1 year
  • 1-3 years
  • 4-9 years
  • 10+ years
  • Prefer not to say
Q13
메시지

That's everything — thank you! Your feedback goes directly into a report the lab operations team uses to fix requisition forms, turnaround times, and system integrations.

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.

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차별화 포인트

  • Includes an AI follow-up interview that reconstructs exactly what happened on a respondent's most recent rejected requisition, replacing vague 'it was frustrating' complaints with a step-by-step account lab ops can act on
  • Pairs a matrix rating of form clarity, turnaround, and error rates with a numeric question on actual hours-to-result and an opinion-scale on rejection frequency, so perception and objective throughput are captured side by side
  • Uses ranking and a 100-point constant-sum allocation to force respondents to prioritize which fixes (form design, turnaround, communication, etc.) matter most rather than rating everything as important
  • Screens by role, facility type, and tenure so lab ops can segment feedback by who is actually submitting and reworking requisitions, then rolls everything into an auto-generated report

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