This article will give you tips on how to analyze responses from a Patient survey about accessibility for people with disabilities using AI tools and proven approaches to survey response analysis.
Choosing the right tools for analyzing survey data
The best approach and tooling for survey response analysis always starts with the type and structure of your data.
Quantitative data: If your survey data is mostly numbers (e.g. how many patients chose a certain answer), traditional tools like Excel or Google Sheets are perfect. Counting and simple aggregation is straightforward here.
Qualitative data: When you’re dealing with open-ended responses or rich follow-up answers, things get much harder. It’s almost impossible to read and synthesize everything manually, especially as response counts grow. That’s the main reason AI-powered tools are so valuable for qualitative survey analysis.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copying data into ChatGPT can be a quick and easy way to get started. You can paste exported survey responses and prompt the AI to synthesize main ideas, look for sentiment, or summarize patterns. It works—but the process is often clunky, especially if you need to repeatedly copy, paste, and rephrase prompts for nuanced insights.
Handling data with ChatGPT isn’t very convenient for large datasets. There’s little structure, so keeping context, tracking follow-ups, and digging into subsets of responses gets overwhelming fast—especially for projects dealing with detailed topics like accessibility barriers for people with disabilities or longitudinal patient feedback.
All-in-one tool like Specific
Specific is built specifically for AI-powered survey analysis (see the AI survey response analysis feature). It works like this:
Data collection and follow-ups: Specific’s conversational survey format helps you capture high-quality, deep qualitative data—every answer can trigger follow-up questions (learn how AI follow-up works), so you end up with richer context compared to flat survey forms.
AI-powered analysis: When survey responses are in, Specific instantly summarizes open-ended and follow-up answers, highlights key themes, and turns big response sets into actionable insights. You never have to export or wrangle spreadsheets.
Conversational AI for data exploration: You can actually chat with the AI about your survey results, drill down on specific subsets, and ask for clarifications—just like you would with ChatGPT, but purpose-built for survey workflows and with extra tools for filtering and context management.
Results are easy to share and keep organized for collaborative analysis, making it a perfect fit for teams who want to engage stakeholders or work iteratively.
You can check out how this works or launch your own by using the AI survey generator for patient accessibility surveys or start from scratch with the flexible survey maker.
Whatever approach you choose, robust tooling is essential for keeping your analysis focused and uncovering what truly matters—especially with high-stakes survey topics like healthcare accessibility. Globally, 15% of people experience some form of disability, making this topic both urgent and broad in impact [1].
Useful prompts that you can use for patient survey response analysis
Great AI survey analysis often comes down to using the right prompts or instructions. Here are several tried-and-true prompts I use to analyze patient survey data around accessibility for people with disabilities—these work for both GPT tools like ChatGPT and in tools like Specific.
Prompt for core ideas: This extractive prompt is foundational for surfacing main topics from a large dataset. It is 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
AI always performs better if you give it more context about your survey, audience, and goals. Here’s how you could embed context in your prompt:
You are analyzing responses from a patient survey about accessibility barriers for people with disabilities in healthcare settings. Our goal is to identify the top obstacles patients face and suggest actionable next steps for hospital administration.
After surfacing core ideas, dig deeper:
Prompt for details on a topic: “Tell me more about XYZ (core idea).” This helps you drill into specific issues, such as attitudes toward physical access or assistive technology.
Prompt for specific topic validation: “Did anyone talk about wheelchair accessibility?” (You can also add “Include quotes.”) This instantly finds real-world voices supporting or questioning a theme.
Prompt for personas: “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.” Useful for understanding patient diversity in accessibility needs.
Prompt for pain points and challenges: “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.” This is vital, considering 72% of Canadians with disabilities reported experiencing one or more accessibility barriers in the last year [3].
Prompt for motivations & drivers: “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.”
Prompt for sentiment analysis: “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.”
Prompt for suggestions & ideas: “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 unmet needs & opportunities: “Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.” Given that only 44% of UK workplaces are fully accessible for employees with disabilities [4], you can expect similar gaps in healthcare environments—surfacing these unmet needs is the beginning of better design.
For more prompt and question ideas, I recommend this guide to survey questions on accessibility or the AI survey editor for iterating on your survey structure itself.
How Specific analyzes qualitative data by question type
I want to touch on how AI survey analysis works differently based on the kind of questions you asked. Here’s how Specific structures its synthesis—making sense of even big pools of qualitative feedback from patients about accessibility for people with disabilities:
Open-ended questions (with or without follow-ups): Specific generates a clean summary of all responses, plus synthesizes insights from follow-up replies, giving you a full picture for each open question.
Choice questions with follow-ups: Every answer choice gets its own mini-report. For each group, you get a summary of what people who selected that choice said in follow-up questions, revealing underlying reasons and nuances.
NPS questions: Each NPS category—detractor, passive, promoter—gets its own summary of follow-up comments, showing you what’s driving positive, neutral, or negative feedback on accessibility in healthcare.
If you’re not using Specific, you can still do this in ChatGPT—it just requires copying and filtering data by hand, which quickly gets labor-intensive for bigger data sets.
To learn more about building surveys that support robust analysis, check out this guide on survey creation.
How to overcome AI context size limitations in survey analysis
If you’ve ever pasted survey data into ChatGPT and got a warning about context limits, you know the pain. AI tools have a set limit on how much text (“context”) they can process at once—crucial if you’re handling thick qualitative data sets, as you get in comprehensive patient feedback projects.
Specific offers a couple of proven approaches to keep analysis focused and within the AI’s context window:
Filtering: Zero in on specific subsets of conversations—only the conversations where patients replied to certain questions or selected certain answers are sent to the AI for detailed analysis. This makes it easier to explore, for instance, why patients who identified a particular barrier had different experiences.
Cropping: Select which survey questions should be included in AI analysis. If you care only about responses to questions about digital access (or want to ignore meta questions), you can crop the rest out—perfect for managing scope and avoiding context overload.
Both methods make running large-scale AI explorations feasible—and keep you focused on the most actionable parts of your survey response analysis.
Collaborative features for analyzing patient survey responses
Collaboration is a huge challenge when teams are exploring accessibility feedback together. Traditional methods—passing around spreadsheets and long PDFs—get messy fast. When the topic is as complex and sensitive as patient accessibility for people with disabilities, getting multiple eyes on the analysis is critical for insight and accountability.
With Specific, you analyze survey data in real-time chats with AI. You can have multiple “AI chats” per survey—each one can be filtered for a different slice of your patient audience (for example: “patients using mobility aids” versus “patients with cognitive impairments”). This means teams in different roles (admins, patient advocates, accessibility managers) can ask unique questions and get summarized, relevant data instantly.
Every chat shows who asked what. When collaborating, it’s easy to see who created a given analysis thread and who contributed comments. Avatars show up in the chat view, making it easier to coordinate work across teams, track insights, and share findings with colleagues.
Collaboration drives better outcomes and prevents groupthink, especially when the stakes are high for patients with disabilities who might be facing unique or intersectional barriers. This structure beats the old “one and done” report model, and keeps the entire process transparent and flexible.
Create your patient survey about accessibility for people with disabilities now
Collect deeper insights and analyze patient feedback like a pro by using an AI-powered conversational survey that does the heavy lifting—turning survey response analysis into clear, actionable results in minutes.