This article will give you tips on how to analyze responses from a patient survey about smoking cessation support using the latest AI-powered survey analysis tools.
Choosing the right tools for patient survey data analysis
How you analyze your patient survey responses about smoking cessation support depends on what kind of data you’re dealing with. Here’s what matters:
Quantitative data: For numbers-driven results (like “how many patients used NRT?”), you can easily crunch these in Excel or Google Sheets. Tallying up choice-based answers makes simple trends jump out fast.
Qualitative data: When you’re looking at open-ended responses (“What made quitting hard for you?”) or detailed follow-ups, manual reading just isn’t realistic—especially with dozens or hundreds of answers. That’s where you need AI tools that can digest, summarize, and surface the meaningful patterns hidden in that raw text.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Quick and flexible: You can copy-paste your survey exports directly into ChatGPT or another generative AI tool. Prompt it with your own questions—like “What barriers did patients mention most often?” or “Summarize the top motivators for quitting.”
Caveats: This is fast, but not particularly convenient for repeat or ongoing analysis. Handling large data sets is tricky—context windows, copy-paste limits, and organization can get messy.
All-in-one tool like Specific
Purpose-built workflow: Specific is designed specifically for running and analyzing conversational surveys. You can create and launch AI-driven patient smoking cessation support surveys, with real-time, personalized follow-up questions that draw out deeper responses. That leads to higher-quality data than traditional multiple-choice alone. See this deep-dive on automatic AI follow-up questions for why that matters.
Automated, instant analytics: Once your responses come in, Specific summarizes them instantly, highlights recurring themes, and finds actionable insights—no spreadsheets or manual coding. You can chat with the AI about anything in your results (like ChatGPT, but fully integrated and context-aware). The platform lets you filter, segment, and ask granular questions about your data. For detail on how this works in practice, check out AI-powered survey response analysis.
Extra quality-of-life: Manage context, run multiple chats, and keep all your qualitative (open-ended) data organized without jumping between tools. You’re equipped to handle everything from single questions to large, multi-question, multi-patient surveys—much smoother than any generic AI tool.
Useful prompts that you can use for patient survey about smoking cessation support
AI is powerful, but it’s only as good as your prompts. Here’s how I recommend slicing qualitative responses for patient smoking cessation support surveys:
Prompt for core ideas: Use this to quickly discover the top themes. It works whether you’re using Specific or 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
Add context for better AI results: The more the AI knows about your survey’s background, the better its outputs. For example, you could say:
Analyze responses from a patient survey about challenges in quitting smoking, run by a hospital in urban New York City. The goal is to identify what barriers patients face, especially in relation to support services.
Dive into a particular idea: Once you know the main themes, probe deeper. For example: “Tell me more about NRT access barriers.”
Prompt for specific topic: If you want to know if anyone mentioned a certain thing, just ask the AI: “Did anyone talk about social media support? Include quotes.”
Prompt for personas: Great for teasing out typical patient types and their relevant patterns:
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:
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 and 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.
Use these as a starting point—you can get very specific, depending on what you want to explore about your patient population and their experience of smoking cessation support. For more prompt ideas and in-depth best practices, see the best questions for patient smoking cessation support surveys.
How Specific analyzes qualitative data from patient surveys
Specific is engineered with the exact workflow you need for modern survey analysis. The summary AI logic changes depending on how your questions are structured:
Open-ended questions (with or without follow-ups): Get an aggregated summary across all main answers, and a secondary summary for any follow-up the AI asked. That means you see both the big themes and the nuances behind them.
Choices with follow-ups: For every option (e.g., “Tried nicotine patches”), you’ll see a focused summary for all the supporting feedback related to each choice. This makes it much easier to understand the “why” behind each behavior or choice—which is critical, since in one 2022 study only 8.8% of U.S. adults who attempted to quit smoking were ultimately successful [1].
NPS (Net Promoter Score): Specific automatically organizes feedback by detractors, passives, and promoters—so you get instant clarity on what’s driving each group’s behavior, not just the overall score.
You can do all this in ChatGPT as well, but it takes more copy-pasting, more attention to context, and a lot of manual wrangling. Specific keeps it structured from the start. If you want to experiment hands-on, here’s a survey generator for patient smoking cessation support.
Tackling AI context size limits for large survey datasets
Any AI—including ChatGPT and other LLMs—has practical limits on how much text you can send at once. Too many lengthy responses? The AI can’t “see” it all in a single go. Here’s how you can get around those bottlenecks (and how Specific handles this seamlessly):
Filtering: Before sending your survey data to the AI, you can filter for only the conversations where patients answered a specific question, or where they selected certain options (like “used NRT”), reducing the dataset in focus. That means less noise, and higher accuracy.
Cropping: Select only key questions to send to the AI. For example, if you want to analyze just the responses to “What would have helped you more?”, exclude all other questions and keep the AI laser-focused. This approach is vital to truly scale your analysis and maintain accuracy on really big surveys. See more detail in Specific’s AI context management features.
For manual workflows, you can try similar tricks—export and filter responses in advance—but integrated tools can save hours and headaches.
Collaborative features for analyzing patient survey responses
Getting insights from a patient smoking cessation survey isn’t a solo activity; collaboration with healthcare teams, support staff, and even external analysts is common—and a real challenge with scattered spreadsheets or plain old ChatGPT chats.
Conversational AI analysis: In Specific, you can interact with your qualitative survey results (and all underlying data) just by chatting with the AI. That keeps things approachable regardless of your research background.
Multiple chats, clear ownership: Every team member can spin up their own chat, each focusing on specific themes or question filters (like “patients from a certain clinic,” or “those who tried digital interventions,” where, for instance, a Twitter-based program doubled quitting success over traditional methods [2]). Each conversation clearly shows who created it, so no one loses track.
Transparent collaboration: When multiple people join the analysis, avatar markers show exactly who asked which question. This isn’t just helpful for context—it’s essential when you’re looking at nuanced human data that really benefits from diverse perspectives. Collaboration features make it easy to distribute the work, address conflicting interpretations, and drive changes faster.
Full conversation history: You can revisit, copy, or expand on any prior discussion, keeping your analysis workflows consistent and audit-friendly. For more tips, see this guide to creating a patient survey about smoking cessation support.
Create your patient survey about smoking cessation support now
Get faster, deeper, and more actionable insights from your next patient survey by using AI analysis and conversational survey tools—find out what actually works in helping people quit, in record time.