Q01
Message
All templatesHealthcare & Clinical
Clinician Satisfaction With AI Documentation Tools
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.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
14 questions · ~7 min
Q02
Multiple ChoiceRequired
Which type of AI documentation tool do you primarily use for clinical notes?
- Ambient voice-recording scribe (listens during the visit)
- Template or smart-phrase based note generator
- Fully automated note drafting integrated with the EHR
- Other
- Not sure / don't know which type
Q03
Opinion ScaleRequired
Overall, how satisfied are you with this AI documentation tool?
Scale: 1 – 5
Min:Very dissatisfiedMax:Very satisfied
Q04
MatrixRequired
How much do you agree with each statement about the tool?
4 rows × 5 columns
- The AI-generated notes accurately capture clinically relevant details
- I trust the AI-generated note enough to sign it with only minor edits
- Using this tool has improved my ability to focus on the patient during visits
- The tool fits smoothly into my existing documentation workflow
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q05
Number
In a typical week, about how many hours does this tool save you on documentation compared to before you used it?
Q06
Multiple ChoiceRequired
In the last 30 days, how often did you need to correct the AI-generated note before signing off?
- Never
- Rarely
- Sometimes
- Often
- Every time
Q07
RankingRequired
Rank these issues from the one that most hurts your satisfaction with the tool to the one that matters least.
- Inaccurate or fabricated clinical details
- Missing important context from the visit
- Time spent editing notes before signing
- Poor integration with the EHR
- Lack of specialty-specific terminology
- Concerns about cost or billing implications
Drag to rank
Q08
AI Interview
Anchor on the satisfaction rating and the correction frequency the clinician gave. Ask them to walk through one specific recent encounter where the tool's note either clearly helped or clearly went wrong — what it got right or missed, how long fixing it took, and whether they'd have trusted it unedited. If they rated satisfaction low, probe what single change (accuracy, speed, integration, oversight workflow) would move them to 'satisfied.' If they rated it high, probe whether there are edge cases (complex patients, rare diagnoses, difficult conversations) where they still don't trust it.
Q09
Opinion ScaleRequired
How likely are you to recommend this AI documentation tool to a colleague?
Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q10
Message
Last, a few quick details about you — these help us compare results across specialties and settings. All optional.
Q11
Dropdown
What is your primary clinical specialty?
- Primary care / Family medicine
- Internal medicine
- Emergency medicine
- Surgery
- Psychiatry / Behavioral health
- Pediatrics
- OB/GYN
- Other specialty
- Prefer not to say
Q12
Multiple Choice
How long have you been using this AI documentation tool?
- Less than 1 month
- 1-6 months
- 6-12 months
- More than a year
- Prefer not to say
Q13
Multiple Choice
What is your primary practice setting?
- Inpatient / hospital
- Outpatient clinic
- Emergency department
- Telehealth
- Other
- Prefer not to say
Q14
Message
That's everything — thank you for the candid feedback. Your responses will be combined with your colleagues' to guide fixes and decisions about how this tool is used going forward.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Includes an AI follow-up interview that reconstructs a specific recent encounter, anchored on the clinician's satisfaction rating and correction frequency, to surface concrete fixes a score alone can't show
- Covers accuracy, trust, time saved, and workflow fit through a dedicated matrix question plus opinion scales for overall satisfaction and likelihood to recommend
- Includes a ranking question isolating which issue most hurts satisfaction, a numeric estimate of weekly hours saved, and a correction-frequency question to quantify trust in the AI output
- Captures specialty, practice setting, and tool tenure so health systems and vendors can segment results by clinical context
Jotform
200+ Customer Satisfaction Evaluation FormsThis is a broad category/directory page of generic customer satisfaction form templates, not a fielding-ready survey built for clinicians or AI documentation tools. It would require substantial rebuilding to fit a clinical-workflow use case. Useful mainly as a form-builder starting point rather than a domain-specific instrument.
What it does well
- Large library of customizable form templates and a drag-and-drop builder
- Established form-hosting and basic reporting infrastructure
- Easy to adapt generic satisfaction questions to many industries
Where it falls short
- Static rating-scale forms with no adaptive AI follow-up to probe a specific clinical encounter
- No healthcare or clinical-documentation-specific question logic or terminology
- No automated quality scoring of open-ended responses
SurveySparrow
FREE Customer Satisfaction Survey TemplateA free, single generic customer-satisfaction template with SurveySparrow's conversational chat-style format. It is ready to field quickly but is written for general customer feedback, not clinician evaluation of AI documentation tools, so questions on accuracy, trust, and workflow fit would need to be authored from scratch. No healthcare-specific branching or scoring is present.
What it does well
- Conversational, chat-like survey experience that can feel more engaging than static forms
- Free to use and quick to deploy
- Simple customization within SurveySparrow's builder
Where it falls short
- No adaptive AI interview to reconstruct a specific clinical encounter or surface root-cause fixes
- Not built for clinical audiences—lacks specialty, practice-setting, or documentation-tool-specific questions
- No transparent, published methodology for how follow-up questions (if any) are generated
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies from the same category.
Healthcare & Clinical
Health Insurance Plan Satisfaction & Claims Experience Survey
Measures how members feel about their health insurance plan—value for cost, network access, claims experience, and renewal intent—for health plans, brokers, or HR benefits teams. An AI follow-up interview reconstructs what actually happened the last time a member tried to use their coverage, surfacing friction that closed-ended ratings miss.
View templateHealthcare & Clinical
Oral Health Habits & Dental Care Experience Survey
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.
View templateHealthcare & Clinical
Student Wellbeing and Health Access Screening Survey
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.
View templateHealthcare & Clinical
Dental Health History and Care Habits Survey
Captures a respondent's dental visit history, oral hygiene habits, past procedures, and comfort level with dental care, for use by clinics, insurers, or researchers studying access and prevention. An AI follow-up interview digs into the real reasons behind dental anxiety or delayed care, going beyond a checkbox answer.
View templateHealthcare & Clinical
Dental Plan Member Experience Survey (CAHPS-Style)
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.
View templateHealthcare & Clinical
Therapy Informed Consent Understanding Survey
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.
View template