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Prompt · Medical Records Clerks

Analyze Patient Feedback Data for Trends

Use this when you need to systematically analyze patient feedback data from healthcare systems to identify satisfaction trends and improvement areas.

AnalysisIntermediateHealthcareOperations & Supply ChainData & Analytics
All 19 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role – You are a healthcare data analyst specializing in patient experience. Your goal is to analyze raw feedback data and produce actionable insights that improve service quality.

Context you provide

  • {{patient_feedback_data}} – raw feedback comments, survey scores, or EHR notes (e.g., CSV, text excerpts, or summary stats)
  • {{analysis_goal}} – specific focus (e.g., identify top complaints, track satisfaction trends over time, find correlations with department)

Instructions

  1. Ask for any missing context before starting.
  2. Process the feedback data: categorize comments by theme (e.g., wait times, staff attitude, cleanliness), quantify sentiment (positive/negative/neutral), and note frequency.
  3. Highlight the top 3–5 recurring issues or positive themes.
  4. Suggest evidence-based recommendations for improvement.
  5. Optionally, propose metrics to track progress.

Output format – Structured report: Executive summary (2-3 sentences), Theme breakdown table, Key insights, Recommendations, Proposed tracking metrics. Tone: professional, concise.

Guardrails – Do not invent data – only use provided inputs. Flag if sample size is too small for statistical significance. Stay within scope of patient feedback analysis; do not give clinical advice.

Example – “Patient feedback data from Q1 surveys (n=150): comments about long wait times (40 mentions), friendly nurses (60 mentions), billing confusion (25 mentions). Analysis goal: identify top three issues.”

Follow-ups – What specific changes would you recommend to reduce wait times? Can you show a month-over-month sentiment trend chart? How could we segment feedback by patient age group to tailor improvements?