This article will give you tips on how to analyze responses from a Patient survey about Pediatric Care Experience using AI-powered survey response analysis—so you can get actionable insights faster, without drowning in spreadsheets.
Choosing the right tools for analysis
The best approach and tooling for AI survey analysis depends on the structure of your survey data. Whether your survey responses are quantitative or qualitative makes a world of difference.
Quantitative data: Numbers are your friend here. If your survey is mostly closed-ended—think multiple choice or rating scale questions—counting up responses in Excel or Google Sheets does the trick. You spot trends and filter results with basic formulas.
Qualitative data: Things get trickier when you’re dealing with open-ended answers or long-form feedback. With dozens or hundreds of Patient responses, there’s no way to read each verbatim and find patterns by hand. Here’s where AI comes in—you need the power of AI tools to make sense of the data.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste responses and chat: This is the “quick and dirty” way. Export your survey data as text, then paste it into ChatGPT or a similar tool. You can ask questions like, “What are the main pain points parents mention about pediatric care?” and get instant summaries.
Not always efficient: Handling large survey files this way is rarely convenient. You’ll hit context limits fast, lose track of who said what, and spend too much time updating data as responses roll in. Plus, you won’t have custom filters or organization for different question types—the process feels scattered.
All-in-one tool like Specific
Built for survey analysis: Specific was made for this problem—collecting and analyzing Patient feedback about Pediatric Care Experience in one place. You launch a conversational survey (that feels like a chat, with followup questions for richer data) and let AI summarize the results.
Instant insights, zero spreadsheets: The AI analysis in Specific gives instant summaries, surfaces key themes, and highlights actionable opportunities. You can chat directly with AI about your survey—asking any question you’d type into ChatGPT, with responses grounded in your survey data.
Smarter data collection: Because it asks AI-generated follow-up questions in real time, you capture better quality insights. You don’t have to guess what someone meant—the survey clarifies on the spot. For details on this, check out the automatic followup questions feature.
No manual work required: All the context sorting, grouping, and filtering is handled automatically. You can manage and segment your data before sending anything to the AI, making deep dives much easier. If you want to focus on particular questions or groups, you can do that instantly.
Useful prompts that you can use to analyze Patient pediatric care experience survey data
You’ll get the most from AI if you use the right prompts for Pediatric Care Experience data. Here are examples that work with both ChatGPT and in platforms like Specific (these are handy even if you’re using a generic AI tool):
Prompt for core ideas: This is the go-to for surfacing main topics and patterns in large qualitative data sets:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
Add extra context for the best results. The more you explain your survey’s purpose or your key goal, the smarter the AI’s analysis.
Prompt with extra context:
This survey is about Patient experiences in pediatric inpatient care. I want to understand what parents value most about their child's hospital stay, and where communication broke down between families and staff. Summarize the five main themes mentioned in the survey responses, and highlight gaps in hospital safety and staff communication.
Dive deeper on key ideas: If one theme stands out—like, say, “doctor communication”—just ask:
Tell me more about doctor communication
Prompt for a specific topic: To check directly if people mentioned an issue (such as safety):
Did anyone talk about safety? Include quotes.
Prompt for personas: Useful for segmenting parents or patients by their needs or experiences:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: Ideal for surfacing barriers in the patient experience:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for sentiment analysis: To understand the mood behind feedback:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
When you combine prompts like these with a tool that manages context, you’ll move from broad summaries to detailed, actionable recommendations—fast. There’s more on prompt engineering and data wrangling in this article on how to create patient surveys for pediatric care, and you can check out recommended survey questions here.
How Specific analyzes qualitative data by question type
Specific automatically tailors its AI-powered analysis based on the survey structure and question logic:
Open-ended questions with or without follow-ups: It gives you a summary for all responses, as well as responses to each follow-up related to that core question. This is essential for spotting nuanced parent or patient concerns.
Choices with follow-ups: For each multiple choice option, you get a focused summary of all follow-up answers relevant to that choice, letting you drill down into why people chose a certain option—like “quietness of hospital room,” which was shown to have large variations in patient survey results [1].
NPS (Net Promoter Score): Each NPS category (detractors, passives, promoters) gets its own summary of related follow-up answers, so you can see what makes fans happy and what’s bothering others.
If you’re doing this manually with ChatGPT, you’ll need to separate the data for each group yourself and repeat prompt analysis for every segment—a lot more resources and patience required.
How to deal with AI context limits when analyzing large survey sets
AI tools like ChatGPT and even powerful built-in solutions have a context size limit—there’s only so much survey content you can send in at once. When your Pediatric Care Experience survey has hundreds of Patient responses, you quickly hit this wall.
There are two proven approaches to squeeze the most out of AI, both included as options in Specific:
Filtering: You can hand-pick the conversations to analyze—say, only including parents who answered a specific follow-up (“How did you feel about hospital safety?”). This keeps your questions laser-focused, especially when survey volumes are high. It’s also super helpful when you want to zoom in on feedback about communication, where, for example, only 65% of children felt doctors always communicated well [1].
Cropping: Only send targeted questions to the AI, like qualitative follow-ups rather than every response or demographic field. This means the AI only gets what it needs for your current analysis, and you avoid context overflow.
Using these approaches, you can run deep, specific analyses of massive survey projects without technical headaches.
Collaborative features for analyzing Patient survey responses
Working as a team on Pediatric Care Experience surveys can get messy—different people chase different lines of inquiry, and analysis becomes a tangle of spreadsheets and files.
Analyze survey data together, live: In Specific, everyone on your team can chat with the AI about Patient survey responses, seeing answers and refining questions in real time.
Multiple chats, multiple perspectives: You can set up several chats, each with unique AI filters (such as by respondent type or question focus). You always know who started each chat and what angle they’re pursuing, making it much simpler to coordinate and share findings.
Real faces, real accountability: Each message in the AI Chat is linked to the team member who sent it, displaying avatars. This is a small touch that adds up—no more confusion about who asked what or which question led to that particular insight.
Fits your workflow: Whether one person handles reporting or you have a group of researchers, the platform adapts to both solo and collaborative analysis. And unlike most free-form AI tools, every bit of context, filtering, and collaboration is seamless.
For more on matching survey tools and team processes, check out the AI survey editor and survey generator for patient experience.
Create your Patient survey about pediatric care experience now
Get to actionable insights from Patient surveys about Pediatric Care Experience faster—combine structured conversational surveys with instant AI analysis, and unlock the full story behind your feedback with Specific’s workflow.