Help Us Improve – Share Your Hospital or Clinic Visit Experience

Important Note: This analysis provides strategic insights to help you get the most from your form's submission data for powerful follow-up actions and better outcomes. Please remove this content before publishing the form to the public.

Overall Form Strengths

This Hospital & Clinic Experience Survey is a well-architected instrument that balances breadth with usability. By segmenting questions into thematic sections—ranging from access and arrival to discharge and follow-up—it mirrors the patient journey, reducing cognitive load and encouraging narrative-style responses. The liberal use of conditional follow-ups (e.g., mobility-assistance ratings only if assistance was needed) keeps the form dynamic and relevant, while the mix of rating scales, numeric inputs, and optional comment fields supplies both quantitative KPIs and rich qualitative data for service-improvement teams.

The form also demonstrates strong data-quality discipline: only six of 49 elements are mandatory, yet these six capture the minimal viable dataset required for statistical weighting, longitudinal linkage, and regulatory reporting without creating a deterrent to completion. Visual hierarchy is reinforced through section headings, placeholder examples, and plain-language labels, making the survey accessible to patients with varying health-literacy levels.

Question-by-Question Insights

Type of facility visited

Purpose: This opening mandatory question routes the respondent into contextually correct skip-logic and provides the stratification variable essential for benchmarking hospitals against clinics, EDs against elective centres, etc.

Effective Design: A single-choice list avoids ambiguity while covering the full continuum of care sites. Because it is asked first, it activates patient memory of the specific episode, improving accuracy of downstream date and experience items.

Data-collection implications: The categorical data integrates cleanly with facility master-files for automated dashboarding and supports CMS or NHS-style acuity adjustments when satisfaction scores are compared across sites.

User-experience considerations: The question is phrased in plain language and appears immediately after the welcome paragraph, establishing trust that the survey is relevant to the episode just lived.

Date of visit (or admission)

Purpose: Supplies the temporal anchor that allows the organisation to link the survey with operational data (occupancy, staffing ratios, infection rates) and to compute recall bias when surveys are completed weeks later.

Effective Design: Open-ended date picker format supports both short outpatient visits and multi-day stays without forcing unnatural precision. Placing it second leverages the priming effect of the facility-type question.

Data-collection implications: Date granularity is essential for time-series analyses and for fulfilling regulatory requirements that mandate surveying within a fixed post-discharge window.

User-experience considerations: Optional calendar pop-up on mobile devices reduces typing burden; placeholder text clarifies that an approximate date is acceptable, lowering anxiety for patients with memory issues.

Main reason for visit

Purpose: Captures the patient’s own wording of the clinical episode, which is invaluable for natural-language processing to detect emerging themes (e.g., ‘long-COVID’, ‘knee replacement’) that ICD codes may miss.

Effective Design: Single-line text with examples strikes a balance between brevity and expressiveness. Because it is mandatory, analysts avoid the ‘missing chief-complaint’ problem that undermines root-cause reviews.

Data-collection implications: Free-text labels can be mapped to SNOMED CT or ICD-10 via text-mining pipelines, enriching risk-adjusted satisfaction models without additional clinician coding.

User-experience considerations: The placeholder examples reassure patients that a lay description is sufficient, mitigating worry about medical terminology.

First-visit flag

Purpose: Enables loyalty analytics (first-vs-return experience) and identifies onboarding pain-points unique to new patients.

Effective Design: Binary yes/no keeps response friction minimal. Used as a covariate in Net Promoter Score modelling, it often explains significant variance.

Data-collection implications: Combined with the date field, organisations can compute first-visit volumes over time, supporting capacity-planning and marketing ROI calculations.

User-experience considerations: Because the question is personal but non-sensitive, it sustains engagement without raising privacy alarms.

Likelihood to recommend (0–10)

Purpose: The anchor metric for Net Promoter Score, globally recognised for correlating with clinical quality and reimbursement in value-based payment models.

Effective Design: 11-point scale is the industry standard, ensuring comparability with HCAHPS, NHS FFT, and private vendors. Mandatory status guarantees every submitted survey carries a KPI.

Data-collection implications: Continuous 0–10 data supports parametric statistics (t-test, ANOVA) for small-sample service-line comparisons, whereas trinary promoters/passives/detractors simplify executive dashboards.

User-experience considerations: Placed in the ‘Overall Impression’ section, it acts as a cognitive summary question—patients intuitively understand the scale and feel their rating encapsulates the entire episode.

Consent checkbox

Purpose: Satisfies GDPR, HIPAA, and institutional review-board requirements for processing anonymised data for quality-improvement.

Effective Design: A single mandatory checkbox with concise legal language avoids the multi-clause scroll boxes that reduce consent rates. Digital signature remains optional, preserving flexibility for patients uncomfortable with e-sign yet willing to contribute data.

Data-collection implications: Because consent is captured at submission time, analysts can safely include the record in secondary research datasets without re-contacting patients.

User-experience considerations: Positioned at the very end, it functions as a confirmation step, reinforcing trust that no data are used surreptitiously.

Important Note: This analysis provides strategic insights to help you get the most from your form's submission data for powerful follow-up actions and better outcomes. Please remove this content before publishing the form to the public.

Mandatory Field Rationale

Type of facility visited
Justification: This field is the essential stratification variable required for any meaningful benchmarking or reimbursement analytics. Without knowing whether the respondent is rating an inpatient stay versus an urgent-care visit, subsequent satisfaction scores cannot be risk-adjusted, rendering comparative dashboards invalid. Regulatory programmes such as HCAHPS or NHS FFT also demand facility-type reporting, making its collection mandatory for compliance.

Date of visit (or admission)
Justification: Accurate temporal linkage enables the organisation to merge survey responses with contemporaneous operational data (e.g., staffing levels, infection rates, bed occupancy) to perform root-cause analyses. It also fulfils survey-timeframe rules set by pay-for-performance programmes that require patient experience data to be collected within a specific post-discharge window. Missing dates would break longitudinal tracking and violate audit requirements.

Main reason for visit
Justification: Capturing the patient’s stated reason in their own words is the only reliable way to risk-adjust satisfaction for case-mix when ICD coding is still pending or when the encounter is outpatient. It is mandatory to avoid surveys devoid of clinical context, which would otherwise pollute comparative analytics and obscure service-line specific issues such as poor orthopaedic pain management or emergency-department communication.

Was this your first visit to this facility?
Justification: First-visit status is a powerful predictor of loyalty and reimbursement; new-patient experience scores typically run 5–10 points lower than return patients. Making this field mandatory ensures analysts can apply the correct statistical weights and avoid biased rankings that would misdirect improvement resources.

On a scale of 0–10, how likely are you to recommend this facility to family or friends?
Justification: As the global standard Net Promoter question, this metric directly influences value-based purchasing penalties and public reporting star ratings. A survey without an NPS value cannot be included in regulatory dashboards, would reduce sample size below statistical power, and could disqualify the facility from incentive payments. Mandatory capture guarantees every completed survey contributes to the KPI.

I consent to the use of my anonymized responses for quality-improvement purposes
Justification: Ethical and legal frameworks (HIPAA, GDPR, local IRB rules) require explicit, auditable consent before any personal health experience data can be processed or stored. Making the checkbox mandatory protects both the patient and the institution from unlawful data use allegations and ensures downstream analytics teams can confidently include the record in improvement projects without re-contacting patients.

Overall Mandatory Field Strategy Recommendation

The current form adopts a ‘minimum viable mandatory’ philosophy: only six of 49 elements are required, striking an effective balance between data completeness and form-completion rates. Empirical studies show that surveys with <15 % mandatory fields maintain completion rates above 70 %, whereas those exceeding 30 % mandatory drop below 45 %. By keeping high-value predictor variables mandatory and rich contextual questions optional, the design maximises analytic utility while respecting respondent burden.

Future enhancements could introduce conditional mandation—e.g., if a patient rates pain management as ‘Very poorly’, the follow-up text field could become mandatory to capture narrative detail. Similarly, making demographic questions mandatory only when equity analytics are planned would prevent non-response bias in specific sub-populations. Finally, consider surfacing a dynamic progress bar that visually distinguishes optional from mandatory items; this transparency has been shown to raise completion rates by 8–12 % in healthcare settings without degrading data quality.