This article will give you tips on how to analyze responses from a Patient survey about Imaging Services Experience using AI and other smart approaches for survey response analysis.
Choosing the right tools for analysis
Your approach—and the best tools—depend on the form and structure of your Patient survey data about Imaging Services Experience.
Quantitative data: If you want to know how many patients selected each choice (like satisfaction scores or NPS ratings), conventional tools like Excel or Google Sheets will do the job. Counting responses is straightforward and quick.
Qualitative data: When you want to make sense of open-ended answers—like stories about MRI wait times, or opinions on radiology staff—manual approaches break down fast. It’s nearly impossible to read every response at scale, especially if you want to find recurring themes or key pain points. For this, AI tools are a game changer and practically required.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Paste and chat: You can paste your exported survey data into ChatGPT or similar GPT-based tools and start asking questions about the content. This lets you have a conversation with your data instead of sifting through endless responses on your own.
Not very convenient: Handling lots of text this way gets messy. It's easy to lose track, formatting can be a pain, and you always end up re-pasting or wrangling files—especially with large survey results or follow-up questions for specific groups.
All-in-one tool like Specific
Built for the job: A purpose-built, all-in-one tool like Specific makes this process dramatically smoother. You can collect data, ask follow-up questions in the flow, and analyze everything directly in the platform.
Quality of data: Specific asks AI-powered follow-up questions as patients complete the survey—meaning you get richer and more actionable data automatically. This matters, because 74.5% of the factors influencing patient experience relate to staff behavior, so probing into how patients felt about staff helps surface what matters most [1].
Instant insights: Once data is in, Specific uses AI to summarize responses, identify common themes, and highlight actionable insights instantly—no spreadsheets or copy-pasting required.
Conversational analysis: You can chat directly with AI about the results (like ChatGPT), but with survey-specific features. This lets you dig into the data conversationally, filter by different response groups, and share findings with your team. Managing exactly what data AI sees is easier because the platform handles context limits for you.
Useful prompts that you can use to analyze Patient survey about Imaging Services Experience
Qualitative analysis with AI is supercharged with the right prompts. Here are some practical, context-rich prompts that work for Patient survey data about Imaging Services Experience:
Prompt for core ideas: Extract common topics and explanations efficiently, even from large data sets. This is the backbone of Specific’s own analysis, but works anywhere, including ChatGPT:
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
AI delivers the best results when you give it extra context about your survey, your goals, or specific concerns. Try introducing your data with:
I conducted a Patient survey about Imaging Services Experience in our radiology department. My main goal is to understand patient pain points with MRI, including staff communication, wait times, and access to reports. Use this context when analyzing responses.
Drill-down prompt: Once you have a core idea, get more detail with: “Tell me more about XYZ (core idea)”.
Prompt for specific topic: To search for targeted feedback or validate a concern: “Did anyone talk about waiting times for MRI scans? Include quotes.”
Prompt for pain points and challenges: To surface bottlenecks or sources of dissatisfaction—which is crucial when, for example, wait time and staff interaction are proven to have an outsized impact on patient experience [3]:
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 personas: If your data set is large, understanding distinct patient ‘types’ based on responses can guide improvements:
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 suggestions & ideas: Encourage actionable improvements, especially since specific feedback on MRI services has been shown to raise satisfaction scores [5]:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for sentiment analysis: Understand whether feedback skews negative or positive overall. This matters when you see, for example, that MRI has a higher dissatisfaction rate compared to mammography—context that guides where to focus improvement efforts [2]:
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.
Want more on crafting effective questions for your patient imaging survey? Check out our guide to the best questions for Patient surveys about Imaging Services Experience or start building from scratch with the AI survey generator with preset for Patient and Imaging Services Experience.
How Specific handles qualitative data: question-by-question analysis
Specific streamlines analysis by automatically adapting to the structure of each survey question, whether open-ended or choice-based:
Open-ended questions: You get a concise AI-generated summary for all responses plus summaries for any follow-ups related to that question. This is crucial for extracting actionable improvement ideas from individual patient stories.
Choices with follow-ups: For every answer choice (like “MRI” or “radiography”), responses to related follow-up questions are grouped and summarized separately. So you’ll see exactly what patients struggled with for each service modality—a big plus, since dissatisfaction rates vary significantly by modality [2].
NPS questions: Each NPS segment (promoters, passives, detractors) gets its own summary of all follow-up responses. This lets you pinpoint, for example, what detractors said about wait times versus what promoters liked about staff friendliness.
You can get similar results with ChatGPT—it’s just a lot more manual, requiring careful sorting and pasting before each prompt.
If you want to create a survey that takes advantage of this structure, you can start with our AI survey generator for Patient Imaging Services Experience or learn how to design a high-impact survey.
Working with AI context limits: Getting the most from large data sets
AI models like GPT-4 have context size limits—if you have a lot of Patient survey responses about Imaging Services Experience, you can hit those limits quickly. Specific solves this out of the box, but the principle applies everywhere.
Filtering: Only analyze conversations where patients replied to selected questions (such as “Describe your experience during the MRI”) or chose specific answers (“I experienced discomfort”). This makes sure you extract insights from the most relevant data.
Cropping: Send only the specific questions you want analyzed to the AI. This keeps analysis focused and efficient and ensures even big data sets can be covered without exceeding the AI’s capacity.
Learn about handling complex logic and follow-ups in your survey with automatic AI follow-up questions in Specific.
Collaborative features for analyzing Patient survey responses
It’s common for Patient experience teams and radiology departments to struggle with sharing insights from Imaging Services Experience surveys—especially when working across roles or locations. Collaboration is far smoother when everyone can actually explore the data together.
Chat with AI, as a team: In Specific, you can analyze survey data simply by chatting with AI, just like in ChatGPT—but with context and options designed for survey data.
Multiple collaborative chats: You’re not limited to a single thread. Multiple chats can be created, each with its own filters (“Show me what MRI patients said about wait times”), and each is attributed to whoever started it. This is a lifesaver for teams who want focused discussions around different survey slices.
Clear attribution: In every chat conversation, you can instantly see which team member asked each question, with avatars showing exactly who said what. This transparency makes it easier for cross-functional teams to actually move from analysis to action—and avoid repeating work or missing a key insight.
For more ideas on building collaborative workflows for survey creation, see how teams use the AI survey editor in Specific.
Create your Patient survey about Imaging Services Experience now
Don’t wait to understand what truly drives patient satisfaction in imaging—create your next Patient survey about Imaging Services Experience and unlock deep, actionable insight with a conversational, AI-powered approach in minutes.