This article will give you tips on how to analyze responses from a patient survey about inclusion in care using AI-powered solutions and best practices in survey analysis.
Choosing the right tools for analyzing survey responses
When analyzing patient survey data about inclusion in care, your approach and choice of tools should match the data you collect. Here’s how I break it down:
Quantitative data: If you ask questions like “How satisfied are you?” or have checkbox options, these are easy to count and visualize using Excel or Google Sheets. It’s straightforward and works for metrics or ratings.
Qualitative data: Open-ended questions or follow-ups capture richer insights—but reading through everything manually is overwhelming, especially when you have hundreds of responses. This is where AI tools shine: they efficiently summarize what patients are saying without slogging through walls of text.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste workflow: One way is to export your survey responses and paste them into ChatGPT. You can then use AI prompts to extract insights, identify themes, or ask questions about the data.
It’s functional but clunky: This approach works, but it’s not ideal. You’ll need to format your data, juggle copy-paste limits, and keep track of context yourself. It doesn’t allow for easy data filtering or combining of structured & unstructured data.
All-in-one tool like Specific
Purpose-built for survey response analysis: Specific is designed to both collect and analyze survey responses using AI in one smooth workflow. You can launch an AI-powered survey about inclusion in care, and Specific handles both the follow-up questions (which boost the depth and quality of responses) and the post-survey AI analysis.
Faster, richer insights—no spreadsheets: With AI survey response analysis in Specific, you instantly get summaries, key themes, and actionable insights. It’s like having a research assistant on demand. You can chat with AI about your results, refine your queries, and manage what parts of the data the AI analyzes—all without exporting anything.
Smarter follow-ups, higher quality data: Thanks to automatic AI follow-ups (learn how this feature improves answers), you can uncover deeper motivations, pain points, and expectations from your patient audience. This translates directly into better research findings.
Useful prompts that you can use to analyze patient survey responses about inclusion in care
Harnessing AI for survey analysis is all about the right prompts. Here are prompts I find helpful when exploring data from patients on inclusion in care:
Prompt for core ideas: Use this whenever you want a concise summary of main topics across many responses. This works whether you use Specific or copy-paste your data into ChatGPT. Try this:
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
Give more context to get better results: AI always performs better when you clarify what the survey covers and what you want to find out. For example, try adding this before your data:
We conducted an anonymous survey with 150 patients about their experiences with inclusion in care at our hospital. Please analyze the open-ended responses and identify key themes regarding communication, respect, and involvement in decisions.
Dive deeper on a theme: Once you see a top theme, use this to explore it further:
Tell me more about communication with medical staff.
Validate a topic: To see if respondents mentioned something specific, use:
Did anyone talk about access to language interpreters? Include quotes.
Spot personas: Helpful for understanding patient segments:
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.
Find 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.
Identify unmet needs & opportunities:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you want more inspiration on writing effective survey questions or designing better prompts, check out our guide to the best questions for patient surveys about inclusion in care.
How Specific analyzes qualitative data by question type
When you analyze qualitative survey data with Specific, the AI tailors its summaries to the structure of each question:
Open-ended questions (with or without follow-ups): Specific provides a summary of what patients said for both main questions and any AI-generated follow-ups. This means you not only see key topics but also get insight into the reasons and motivations behind the answers.
Choice questions with follow-ups: Here, the AI summarizes all open responses linked to each choice, so you see what patients who chose “no” versus “yes” actually meant, in their own words.
NPS questions: Each group (detractors, passives, promoters) gets its own summary from their follow-up answers. This makes it easy to understand why patients feel the way they do about your care or inclusivity efforts. See how to quickly create such a survey using the NPS survey builder for inclusion in care.
You can absolutely do something similar using ChatGPT, but you’ll need to do more manual work to slice answers by group and keep track of what’s being summarized for which question.
If you want a step-by-step walkthrough for setting up your own survey, see this article on how to create a patient survey about inclusion in care.
Tackling context size limits when using AI
One real-world obstacle in AI survey analysis is the context size—there’s only so much text you can fit into the AI’s brain at once, especially if you have hundreds of detailed responses.
There are two classic ways to work around this, both built into Specific:
Filtering: You can filter conversations, having AI look only at patients who replied to specific questions or selected certain answers. This way, you analyze just the most relevant data and stay within AI capacity.
Cropping: You can crop questions, sending only the answers to a particular set of questions for analysis. This makes it much easier to fit a large number of conversations into the AI’s context window.
Both approaches let you go deep without getting lost or missing out on key patterns that might not stand out in aggregate stats. For more details, check out AI-powered survey response analysis.
Collaborative features for analyzing patient survey responses
It’s pretty common for multiple people—doctors, patient advocates, quality managers—to need to collaborate on survey analysis around inclusion in care. But keeping track of everyone’s perspective and findings in a shared spreadsheet is a headache.
Chat with AI, together: Specific lets you analyze survey data conversationally: chat with AI about results, ask your own questions, and get instantly tailored insights. It’s not just more productive—it feels less like fighting spreadsheets and more like talking to a teammate.
Multiple chats, organized by contributor: Each collaborator can start new chats with their own filters, focusing on questions or segments that matter to their team. Every chat clearly names the creator, making it easy to see who’s digging into what.
Real-time visibility of who says what: When collaborating in chat, every message shows the sender’s avatar. This makes it easier to trace decisions, share findings, and align on next steps—especially valuable in multidisciplinary healthcare teams working toward more equitable patient experiences.
Create your patient survey about inclusion in care now
Start collecting real patient insights and instantly turn them into actionable results with AI-powered analysis. Specific’s conversational surveys get you deeper feedback, faster—so you can improve inclusion in care with clarity and confidence.