This article will give you tips on how to analyze responses from a patient survey about medication understanding using AI-powered survey response analysis and practical tools.
Choosing the right tools for survey response analysis
The approach and tooling you choose will depend on the form and structure of your patient survey data. Here’s how I think about each type:
Quantitative data: If your survey includes questions like “did you take your medication today?” or “select the purpose of your prescription,” those responses are easily counted and summarized in conventional spreadsheets like Excel or Google Sheets. These work perfectly for numbers and single-choice answers.
Qualitative data: If the survey contains open-ended questions (“How do you feel about your current medication regimen?”) or allows for rich, multi-sentence answers, things change. You can’t just read through dozens or hundreds of responses—in fact, research regularly shows we underestimate how difficult it is to extract themes just by skimming through pages of text. AI-powered tools are a necessity here, especially when analyzing patient understanding, since studies show high variability in medication comprehension. In a U.S. study, for example, 30% of patients couldn’t name at least one of their medications, and 19% didn’t know their purpose. These knowledge gaps become even more glaring when you read qualitative responses. [2]
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy and chat: You can export survey responses and paste them into ChatGPT (or any large language model). Then, you can instruct the AI to look for themes, extract summaries, or search for specific topics.
Downside: This approach is not very convenient. Formatting responses for copy-paste, staying under the AI’s context limits, and making sure you don’t expose sensitive data are all headaches. You’ll also be stuck manually organizing follow-ups and any deeper dives into specific response groups. Still, for small sets of responses with relatively short answers, ChatGPT is a completely viable option.
All-in-one tool like Specific
Purpose-built for Patient Medication Understanding surveys: Specific is designed specifically for conversational, AI-driven surveys and their analysis. It goes beyond simply collecting data. Instead, it asks targeted follow-up questions in real time as patients complete the survey, which is shown to improve both the quality and completeness of responses. If you’re curious how that works, here’s a guide on automatic AI followup questions.
AI-powered analysis on autopilot: When the responses come in, Specific summarizes all the open-ended answers, identifies major themes, and highlights actionable insights—without you needing to pull anything into a spreadsheet. You can chat with the AI just like you would in ChatGPT, but with direct access to filtering, segmentation, and extra features for controlling what the AI “sees.” Read more about the workflow in AI survey response analysis.
Zero manual work: Instead of spending hours collating responses, you can focus just on interpreting the takeaways and moving forward with real improvements in your patient education process.
If you want to see how easy it is to create and launch a survey like this, check the AI survey builder preset for medication understanding.
Useful prompts that you can use for patient survey response analysis
Prompts supercharge your AI analysis. They help you focus on exactly what matters, whether you’re reviewing understanding gaps, the effects of medication labeling, or emotional reactions from patients.
Prompt for core ideas — Use this if you simply want the big-picture themes. It’s a gold standard, whether you’re using Specific or basic GPT chatbots:
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 more context for better results: AI always works best when it knows your exact use case, your goal, and any context you can give. Here’s an example—prepend it to your data for stronger output:
This survey asked 120 patients about how well they understood their current medications, including purpose, dosage, and side effects. I want to identify main patterns contributing to low adherence, confusion over medication names, and overall patient sentiment.
Drill down on a topic: If you want the AI to elaborate, just use:
Tell me more about “XYZ (core idea)”
Spot-check if anyone discussed a specific topic: Just ask:
Did anyone talk about medication side effects? Include quotes.
Find patient personas: To better segment and understand who your survey audience is:
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.
Pinpoint 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.
Motivations and drivers behind adherence or confusion:
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.
Overall sentiment: This is helpful if you’re assessing the emotional tone of responses or the impact of drug labeling changes:
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.
For even more prompt ideas, check out this resource on AI survey response analysis.
How Specific analyzes qualitative data by question type
The strength of Specific and similar AI-powered tools is that they automatically tailor the analysis approach based on the question’s structure—a crucial feature, especially if you have a mix of open-ended and structured items typical in a patient medication understanding survey.
Open-ended questions (with or without followups): You get a summary for each question and for the full set of followup responses, neatly grouped. This means you spot if patients repeatedly mention not knowing their medication’s name or purpose—a known concern, as seen in studies where only 66% understood side effects and 73% understood the medication’s name. [3]
Choice questions with followups: Every choice is treated as its own “mini group” with a dedicated summary for just those patients—like grouping by “antibiotic” or “blood pressure medication” so you can see unique challenges for each.
NPS questions: Responses are categorized by detractors, passives, and promoters; each category has its own AI-generated summary. This helps isolate what turns patient experience negative or positive when it comes to medication understanding.
You can do exactly the same with ChatGPT, but you’ll spend more time prepping and reformatting your data for every new question or group.
For structure and inspiration on composing your patient survey, see best questions for patient surveys about medication understanding or learn how to create a patient survey about medication understanding step by step.
Working around AI context size limits
Even the best AI tools (including ChatGPT or Specific) have “context limit” issues—if the response set is too large, not everything can be analyzed in one go. Here’s how to keep your analysis sharp:
Filtering: Select only conversations where patients answered specific questions or provided certain responses before sending them to AI analysis. This keeps the dataset manageable and focused, so you won’t exceed limits. With Specific, this is done instantly with built-in filters.
Cropping: Send only the questions you want to analyze to the AI. This lets you dive deep without losing precious context space to irrelevant items.
Both methods are available in Specific as simple controls, so you don’t need advanced technical skills to use them.
Collaborative features for analyzing patient survey responses
Collaborating on patient survey analysis is hard. Sharing large Google Sheets or emailing PDFs of raw data just isn’t practical—especially when you want several people to explore, tag, and discuss findings around patient medication understanding.
Real conversations, real collaboration: With Specific, you can analyze survey data simply by chatting with the AI—no dashboard, no confusion. Each user can create a separate chat, apply custom filters, ask different questions, or chase their own hunches. You always see who created each chat, which streamlines team collaboration.
Transparent teamwork: Every message in the AI Chat shows the sender’s avatar, encouraging direct collaboration and making it easy to track who contributed what. This is especially useful when different roles (clinicians, patient educators, researchers, or pharmacists) want to drill into their unique areas of interest.
Unified insights: Discuss findings as a group, create and share different analysis threads, and move the conversation forward—all without leaving Specific.
To experiment with your own survey designs, try the AI survey editor—it lets you build, refine, and collaborate on patient surveys entirely through chat.
Create your patient survey about medication understanding now
Move from raw opinions to actionable insight in minutes with AI-powered analysis, collaborative features, and instant summaries—bring clarity to your patient care strategies today.