This article will give you tips on how to analyze responses from a Patient survey about Medication Adherence—especially if you want to use AI for survey response analysis.
Choosing the right tools for survey response analysis
The approach you use for analyzing survey data really depends on the type and structure of responses you’ve gathered from patients about medication adherence. Here’s the breakdown:
Quantitative data: These are things like how many patients selected “I always take my medication,” or picked “I sometimes forget.” Numbers, ratings, and tallies are easy to handle in tools like Excel, Google Sheets, or any basic spreadsheet. You can use built-in functions to calculate adherence rates and spot important patterns fast.
Qualitative data: Open-ended responses, follow-up answers, and patient stories about their medication habits? That’s where it gets challenging. Reading through hundreds of long-winded answers isn’t possible when time is tight. This is where AI survey analysis tools step in—they “read” the data for you, summarizing insights and themes that matter.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you're using ChatGPT or another large language model, you can copy your exported data (CSV or spreadsheet) and paste large parts into the AI's chat window to discuss key findings. This works, but it’s not ideal—you'll spend time fiddling with data formatting, chunking up responses so they fit the chat context, and it’s easy to lose track of what you’ve covered already.
Chatting about your survey data in generic tools like ChatGPT feels clunky. You'll often reach limits on how much data you can paste, and setting the right prompts each time gets repetitive.
All-in-one tool like Specific
Specific is built for patient surveys and qualitative data analysis. It offers both data collection—your conversational AI survey that asks smart, real-time follow-up questions—and built-in AI analysis features. Because Specific runs the entire workflow, you get the added bonus of higher quality responses: the AI keeps probing until it understands what the patient means, so the insight from each conversation runs deeper. Interested in building one yourself? Try Specific’s AI survey response analysis features—they're designed exactly for these use cases.
AI-powered analysis with Specific: After data collection, the built-in AI summarizes all open-ended answers, finds core themes, and makes the findings actionable—without needing to copy and paste into yet another tool. If you want, you can even chat directly with the AI about your results, much like with ChatGPT, but with tailored survey prompts and advanced controls that let you filter or crop data before running each analysis.
Features that stand out: Chat with AI about the results, manage how much data the AI "sees," and get instant insights. For teams, you also gain tailored collaborative features, so everyone gets more from the data.
Useful prompts that you can use to analyze Patient Medication Adherence survey responses
If you want to analyze qualitative survey responses with AI, the prompts you use are everything. Here are my go-to prompts for capturing meaningful insights about medication adherence from patient feedback. These work whether you’re using Specific, ChatGPT, or another conversational AI. Just remember: the more context from your study and your goals you add, the better the insights.
Prompt for core ideas: Use this to quickly surface the big themes in responses gathered from patients:
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
Boost your results by giving AI more context: The AI performs best when you share background about your survey, what you’re trying to achieve, the survey audience, and any particular areas of concern (e.g., patient forgetfulness or barriers to adherence—because about 39% of patients simply forget to take their medications [1]). For example, add a lead-in like:
You are analyzing survey responses from a group of patients with chronic conditions. Our goal is to understand the main challenges they face with medication adherence and identify opportunities to improve outcomes and reduce hospitalizations. Extract at least 5 themes and explain each.
Dive deeper on a theme: After identifying your core ideas, use:
Tell me more about [core idea]
Prompt for specific topic: Validate if patients discussed a specific factor or concern:
Did anyone talk about [side effects, cost, forgetfulness, etc.]? Include quotes.
Prompt for personas: This can be particularly useful for understanding the variety among patient groups. Try:
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: Crucial for surfacing the major blockers preventing patients from sticking to their plans:
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: Get to the heart of why some patients do adhere:
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 suggestions & ideas: Gather direct improvements patients might suggest:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Looking for more inspiration? See this guide on the best questions for patient medication adherence surveys or start building a custom survey from scratch with our AI survey generator.
How Specific analyzes qualitative data, by question type
The type of question determines how AI in Specific handles the analysis:
Open-ended questions (with or without follow-ups): You get a succinct summary for all responses, plus deeper summaries for each follow-up—the AI links back each insight to your original questions and probes.
Choice questions with follow-ups: Each response to a specific choice is analyzed separately, so you see what patients who picked “I forget sometimes” mention as their key barriers, frustrations, or needs in their follow-ups.
NPS questions: In medication adherence surveys, all patient comments are grouped and summarized by category—detractors, passives, and promoters. It’s clear where adherence breakdowns occur, and who your strongest advocates are.
You can do something similar in ChatGPT or other LLM tools, but it takes more manual work—copying, pasting, and sorting your responses by type before asking for analysis.
If you’re running a patient survey soon and want to see what makes a qualitative survey valuable, check out this step-by-step on how to create a patient survey about medication adherence.
Tackling context size limits when analyzing large Patient survey data sets
Even the best AI tools have a context window—meaning, they can only “see” a limited amount of data at once. So, what if you have dozens or hundreds of patient responses about medication adherence? Here’s how I handle it (and how Specific makes it easy):
Filtering: Focus the analysis on conversations where patients responded to certain questions or made specific choices. For instance, you might analyze only those who struggled with forgetfulness—after all, about half of patients with chronic diseases don’t stick to their prescribed meds [1]. This way, the AI digs into the most relevant data without overwhelming its capacity.
Cropping questions for analysis: Send only selected questions (such as the big open-enders or NPS follow-ups) to the AI for processing. This approach helps you stay well within context limits and keeps your analysis tightly focused on actionable insights.
Want to give this a spin? Try Specific's out-of-the-box AI survey response analysis chat—it bakes in filtering, cropping, and advanced prompts by default.
Collaborative features for analyzing Patient survey responses
Survey analysis, at its heart, needs to be a team sport—especially when tackling complex, real-life challenges like patient medication adherence, which touches clinicians, administrators, and researchers alike.
Collaborative analysis in Specific: You don’t have to work alone. With Specific, you analyze survey data simply by chatting with AI as a team. Each chat session is distinct—you can set specific filters for each analysis, and the platform logs who created every chat. This makes it straightforward to organize insights, share findings, and build consensus, even if your team is scattered across departments.
Know who said what: As you’re discussing key themes or surprises from patients, each AI chat shows the sender’s avatar, making it obvious who’s driving which part of the analysis and ensuring accountability and context for the whole group.
Curious about launching a conversational AI survey for your patient medication adherence study? Try the guided generator for patient medication adherence surveys—it’s a huge time-saver, and your team will thank you for it.
Create your Patient survey about Medication Adherence now
Make your survey analysis effortless and actionable: launch a conversational AI survey, capture deep insights with smart follow-ups, and turn hours of manual review into instant, collaborative results.