This article will give you tips on how to analyze responses from a Patient survey about Shared Decision-Making using AI, from choosing smart tools to surfacing actionable insights.
Choosing the right tools for Patient survey analysis
The approach you pick for analyzing Patient survey responses about shared decision-making depends on the type and structure of the data you collect.
Quantitative data: When you have numbers—like how many patients chose a specific option or rated a process—it’s straightforward. I usually reach for tried-and-true tools like Excel or Google Sheets for counting, sorting, and basic charting.
Qualitative data: Open-ended answers or detailed explanations? That’s a different ballgame. Manually combing through dozens or hundreds of Patient responses isn’t practical. That’s where AI-driven analysis tools come in, finding patterns and surfacing themes you’d miss in a noisy spreadsheet.
We really have two main approaches for tooling when it comes to qualitative response analysis:
ChatGPT or similar GPT tool for AI analysis
You can paste exported responses straight into ChatGPT or another GPT-like tool and have a back-and-forth conversation about the data.
This method works, but it isn’t the most efficient—formatting the data for the AI, dealing with context limits, and interpreting AI’s often generic responses can get messy. You’ll spend time copying, prepping, and re-prompting, so it’s better for smaller data sets or ad hoc exploration rather than deeper, structured research.
All-in-one tool like Specific
An AI tool built for survey analysis—like Specific—lets you both collect conversational Patient surveys and instantly analyze responses with AI.
One of my favorite things: When you use a conversational survey, the AI can ask immediate, relevant follow-up questions, leading to richer, higher quality data. (Here’s more on how automatic AI followup questions work.)
Specific’s AI survey response analysis instantly summarizes all Patient feedback, finds recurring themes, and builds concise, actionable insights—no spreadsheets or manual coding required. You can chat directly with AI about the results, just like with ChatGPT, but with extra features like filtering and context management to handle tricky or nuanced findings.
It’s all in one place: collect, analyze, and collaborate with AI, focused on Patient feedback and shared decision-making.
Useful prompts that you can use to analyze Patient survey data about Shared Decision-Making
Whether you’re using ChatGPT or Specific, prompts are your main tool for surfacing themes and patterns from Patient open-ended responses. Here are some of my best tips for prompts, plus examples you can copy-paste.
Prompt for core ideas: If you want nuggets of insight—what Patients are actually talking about—this prompt is battle-tested. It’s the default in Specific, but you can use it anywhere:
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
Always add context for better results: AI shines when it understands your Patient survey’s goals, the workflow, or what you care about most. Here’s how I provide survey background in my prompt:
I ran a Patient survey about shared decision-making in healthcare—questions focused on how informed patients felt about treatment options, whether their input was valued, and what information was missing. Please extract and group the main ideas and themes as above.
You can also ask:
"Tell me more about XYZ (core idea)": This lets you zoom in and mine the details behind the patterns, so you can target next steps or deeper interventions.
Prompt for a specific topic: Quickly check if Patients raised a key concern—did anyone mention pain, confusion about risks, or communication gaps?
Did anyone talk about achieving consensus on treatment plans?
Add “Include quotes.” and the AI will pull supporting evidence directly from your Patient responses.
Prompt for personas: Sometimes, segmenting Patient responses is key. I often use this for shared decision-making studies:
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: Get a quick list of where Patients feel friction in the decision-making process:
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 motivations & drivers: Great for surfacing why Patients prefer certain options or decision approaches:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
If you’re designing your Patient survey from scratch, check out our guide on best survey questions for shared decision-making.
How Specific analyzes qualitative Patient survey data based on question type
What makes Specific stand out is how it tailors AI analysis to each survey question type. Here’s how it works for your Patient feedback about shared decision-making:
Open-ended questions (with or without follow-ups): You get a summary aggregating all Patient responses. If you have follow-up questions, those are linked so you see context-rich explanations and clarifications.
Choices with follow-ups: Each option (e.g., “preferred in-person discussion” vs. “online portal”) is summarized with its own cluster of follow-up responses—making it easy to capture not only what was chosen, but why.
NPS: For rating questions (“How likely are you to recommend...?”), summaries separate detractors, passives, and promoters, including their detailed explanations or gripes—so you don’t just see scores, but what’s driving Patient sentiment.
You can run a similar process using ChatGPT, but it’s truly labor-intensive—splitting responses, categorizing manually, and pasting into the right prompt. Specific wraps it all up for you.
How to handle AI context limits with large Patient survey data sets
One thing to remember: GPT-based tools—including ChatGPT and Specific—have a context limit. If you try to analyze too many Patient survey responses at once, the tool may truncate or skip important data.
There are two ways to handle this (and Specific gives you both):
Filtering: Narrow the analysis to conversations where Patients replied to certain questions, or only those who chose a particular treatment option. This reduces the response set and keeps insights relevant.
Cropping: Select only relevant questions (for example, those related to shared decision-making, side effects, or information needs) to send to the AI for analysis. This keeps your data within the context limit and ensures you’re analyzing what matters most.
Collaborative features for analyzing Patient survey responses
One challenge with Patient shared decision-making surveys is getting research, operations, and clinical teams on the same page about what’s actually in the responses—and what actions to take next.
Analyze collaboratively by chatting with AI: In Specific, you can create multiple AI Chats, each focused on a different angle or filtered slice of the Patient data. This means the data isn’t just visible—it’s conversational, and collaboration happens in real time.
See who’s saying what: Every AI Chat shows the creator and participant avatars, so everyone knows who asked which questions or dug into which themes. No more confusion over whose insights are whose—it’s transparent by design.
Build institutional knowledge: Separate chats, with custom filters and focused questions, help teams build a library of Patient perspectives on shared decision-making. You can always return to previous analysis, compare findings, and keep improving your Patient care workflows.
Curious about building your own Patient survey or want tips on getting started? Our guide on how to create a Patient survey for shared decision-making walks you through every step, from setup to actionable results.
Create your Patient survey about shared decision-making now
Get deeper, richer insights into Patient perspectives on shared decision-making with AI-powered analysis—structured prompts, instant summaries, and collaborative tools make upgrading your research effortless and impactful.