Healthcare & clinical survey templates

A periodic self-assessment covering physical health, sleep, stress, activity levels, and emerging symptoms — used by clinics, employers, or wellness programs to spot concerns early. An AI follow-up interview digs into whatever symptom or concern the respondent flagged as most pressing, capturing duration, severity, and impact that a checkbox can't.

Confirms that a new group therapy member understands confidentiality limits, voluntary participation, and what to expect, while gauging comfort with sharing in front of others. An AI follow-up interview surfaces the specific worries behind low comfort or confusion so facilitators can address them before the first session. Built for therapists and group practices running intake.

Measures how people actually use an AI therapist chatbot, how helpful and trustworthy they find it, and whether it has ever felt unsafe or judgmental — for product and clinical safety teams. The AI follow-up interview reconstructs a specific real conversation instead of relying on vague satisfaction ratings.

Captures how patients experience the wait between referral and treatment — expectations vs. reality, communication quality, anxiety, and health impact — for clinics and health systems managing waiting lists. An AI follow-up interview digs into the specific moment communication broke down or worked well, surfacing fixes that satisfaction scores alone can't show.

Captures a patient's oral hygiene habits, symptoms, and dental visit history ahead of or following a screening, for dental practices, insurers, or public health programs. The AI follow-up interview digs into any reported pain or concern to surface details a checklist alone would miss, helping clinicians triage and prioritize care.

Captures how patients experience the medical clearance process — for surgery, sports participation, return to work, or travel — from scheduling through receiving the final decision. An AI follow-up interview digs into the moments that created confusion or delay, giving clinics concrete detail behind satisfaction scores.

Assesses whether patients left the hospital genuinely prepared to manage recovery at home — covering instruction clarity, medication confidence, and follow-up care arrangements. An AI follow-up interview digs into what actually happened after discharge, especially for patients who needed unplanned care, to surface where the handoff broke down.

Captures which health conditions run in a patient's family, which relatives are affected, and how concerned the patient is about their own risk. Built for primary care, genetic counseling, or preventive health intake. An AI follow-up interview digs into the single most concerning condition — who had it, how severe, and whether it has already changed the patient's screening or lifestyle habits.

Tracks how smoothly patients move from being referred to actually being seen by a specialist — scheduling ease, clarity of instructions, and communication between providers — with an AI follow-up that pinpoints exactly where the handoff broke down or worked well. Built for practice managers and care coordination teams.

Tracks what actually happened to graduates of a nursing, allied health, or clinical training program after they left — employment status, licensure pass-through, field relevance, and pay — with an AI follow-up that reconstructs the real job-search story behind the numbers. Built for program directors, career services, and accreditation reporting.

Captures how patients, family members, and care partners experienced a mental capacity assessment — clarity of communication, respect shown, and involvement in the final decision. Built for healthcare and social care teams reviewing how well their assessment process meets legal and ethical standards, with an AI follow-up interview that digs into what made the process feel fair, respectful, or confusing.

Measures how patients experience physical therapy care — clarity of communication, pain management during exercises, and real functional progress — built for clinics, rehab practices, and outpatient PT groups. The AI follow-up interview digs past satisfaction scores to reconstruct what actually happened in sessions and why recovery felt fast, slow, or stalled.

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.

A whole-person health check-in covering self-rated health, energy, sleep, stress, symptom severity, and existing conditions — for clinics, employers, or wellness programs tracking how people are really doing. An AI follow-up interview digs into whatever symptom or condition the respondent flagged as most severe, uncovering impact and what they've already tried before their next visit.

Measures nurse satisfaction across workload, staffing, pay, management support, and career growth, plus burnout frequency and intent to leave. Built for hospital and health-system nursing leadership; the AI follow-up interview digs into the specific shift, policy, or interaction behind a nurse's lowest-rated aspect instead of settling for a generic complaint.

Gathers a new family therapy client's presenting concerns, family structure, history, and goals before the first session, with an AI follow-up that explores the core relational conflict in the client's own words. Built for family and marriage therapists to speed up intake and arrive at session one with real context.

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.

A pre-treatment or progress-check survey for occupational therapy clients that maps real difficulty with daily living tasks, pain during activity, need for assistance, and personal priorities for regaining independence. An AI follow-up interview digs into the single hardest task to surface what's really blocking it and what a good outcome would look like, giving therapists richer intake data than a checklist alone.

For behavioral health practices and clinics considering a group therapy offering, this survey gauges patient interest, preferred format and focus, and the barriers that hold people back. An AI follow-up interview digs into the real reasons behind someone's interest or hesitation so program design reflects what patients would actually attend.

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.

Explores why teachers from underrepresented racial and ethnic backgrounds enter, stay in, or leave the profession — the pull factors, the barriers, and the workplace support that shapes retention. An AI follow-up interview digs into the specific turning point behind each person's decision, surfacing detail closed questions can't capture. Built for district HR, teacher-prep programs, and equity researchers.

Assesses how well career training and development programs serve clinical and clinical-support staff — what's completed, how confident staff feel applying new skills, and what's blocking follow-through. The AI follow-up interview digs into a real recent attempt to use training on the job, surfacing gaps generic satisfaction scores miss.

Captures how patients experience scheduling, wait times, communication, and results delivery during routine or diagnostic medical exams. Includes a best-worst trade-off to prioritize service improvements and an AI follow-up interview that reconstructs what actually happened during the visit — especially around how results were explained — rather than just a satisfaction score.

Gathers clinicians' honest experience of the clinical supervision they receive — safety to disclose mistakes, usefulness of feedback, and depth of case discussion — for supervisors, training directors, or agencies auditing supervision quality. An AI follow-up interview reconstructs what happened in the most recent supervision session instead of relying on general impressions.

Captures how people experience contactless health screening kiosks or apps—ease of use, trust in accuracy, and privacy comfort—for clinics, workplaces, and events. An AI follow-up interview digs into the specific moment trust broke down or friction occurred, beyond a simple satisfaction score.

Measures pet owner satisfaction, trust, and cost transparency after a veterinary visit, with a best-worst trade-off on what drives clinic choice. An AI follow-up interview digs into the reasoning behind trust and cost decisions that closed-ended ratings can't fully explain.

Captures how attendees, exhibitors, and staff actually experienced contactless health screening (temperature scans, symptom check-ins, digital health passes) at entry points of an expo or conference. Built for event organizers and venue health & safety teams, with an AI follow-up that digs into the specific moment screening felt slow, confusing, or untrustworthy rather than just a satisfaction score.

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 easy, clear, and efficient the patient registration and check-in process felt — from forms and insurance verification to staff interactions — with an AI follow-up that digs into the specific friction point that slowed a patient down or made them repeat information. Built for clinics, hospitals, and practice managers evaluating intake workflows.

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.

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.

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.

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.

Captures how patients and employees experience contactless vital-sign screening — camera- or sensor-based checks for temperature, heart rate, or oxygen — covering ease of use, trust in accuracy, and privacy comfort. Built for clinics and workplaces piloting no-touch screening, with an AI follow-up that probes what drove doubt or confidence in the reading.

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.

Measures how financial stress shows up at work, how well employees understand and use existing financial benefits, and where new investment would land best. Built for HR and total-rewards teams, with an AI follow-up interview that uncovers why eligible employees skip benefits they say they value.

Surveys nursing home staff on teamwork, staffing adequacy, handoffs, communication openness, and overall resident safety, aligned with the AHRQ Nursing Home Survey on Patient Safety Culture framework. An AI follow-up interview reconstructs a recent safety event or near-miss to surface what actually happened versus what gets reported.

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.

Captures a new or returning patient's health background, current medications, allergies, and family history before a clinical visit, with an AI follow-up that clarifies vague or incomplete symptom and condition details so clinicians get a fuller picture without a rushed in-office conversation.

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.

Captures how smoothly parents moved through enrollment — from first inquiry through submitting health, immunization, and emergency paperwork — and what drove their choice of provider. An AI follow-up interview reconstructs the exact moment registration got confusing or stalled, so admissions and health-compliance teams know what to fix first.

Captures how prospective families experienced a daycare center tour and what's still standing between them and enrollment. Includes an AI follow-up interview that digs into the specific hesitations behind a family's enrollment likelihood, so directors know exactly what to address before the family decides elsewhere.

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.

Collects the essentials for placing a child on a daycare waitlist — schedule needs, special accommodations, and priority status — plus what matters most when a spot opens. The AI follow-up interview digs into the family's actual flexibility and care requirements so directors can match openings more accurately instead of guessing from a checkbox form.

A short pre-visit intake for veterinary clinics that captures pet basics, visit reason, and recent symptom changes before the appointment, with an AI follow-up that reconstructs the timeline and severity of the primary concern so the vet walks in prepared instead of starting from scratch.

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.

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.

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.

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.

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.