This article will give you tips on how to analyze responses from a Clinical Trial Participants survey about Visit Burden. I'll show you approaches, prompt examples, and smart AI techniques to get actionable findings faster.
Choosing the right tools for analysis
Your approach and tooling hing on the structure and form of your survey data. For most Clinical Trial Participants surveys about Visit Burden, you’ll be working with both numbers and narratives—each demanding a different strategy.
Quantitative data: When you want to know, for example, how many participants cited parking as a challenge or how far they had to travel, you’re dealing with structured, countable information. Tools like Excel or Google Sheets easily crunch these stats.
Qualitative data: Open-ended answers or conversational follow-up responses offer rich context, but they're nearly impossible to review manually at scale. If you have even a few dozen responses—let alone hundreds—AI tools are indispensable for surfacing themes, patterns, and deeper insights.
There are two main ways to bring AI into your survey analysis workflow when faced with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can export your survey results—often as a CSV or plain text—and paste large blocks of responses into a chatbot such as ChatGPT. This lets you “talk” to your data, asking follow-ups or prompting the AI to summarize themes.
But, it’s clunky. Copy-pasting data isn’t scalable, and tracking which response led to which insight can turn messy fast. There’s limited granular control, and adding context (like follow-ups or branching survey logic) is tedious.
All-in-one tool like Specific
Platforms built for this task—like Specific—combine data collection and instant AI-powered analysis. The survey feels like a chat, intelligently asking follow-up questions that enrich the quality of insights. This matters—a recent study showed the burden on clinical trial participants has surged by 39% since 2019, with surveys themselves being a top contributor. The right tooling helps you capture what matters without overwhelming anyone. [1]
Where Specific shines: Its AI-powered analysis summarizes open-text responses, uncovers key themes, and highlights actionable takeaways automatically—no spreadsheet exports or manual coding. You can chat directly with AI about your data (with robust filters and controls for exactly what’s shared), speeding up the research cycle.
If you want to design surveys from scratch or tweak existing ones, try Specific’s intuitive survey generator for clinical trial participants or the general AI survey builder.
If you’re interested in the science of follow-up probing, here’s how Specific’s automated AI follow-ups work in practice for collecting richer data.
Useful prompts that you can use for analyzing clinical trial participant visit burden survey responses
Whether you’re using Specific or a generic AI assistant, prompts steer your analysis—turning floods of open-ended feedback into clear summaries. Here are the best, field-tested prompts for unpacking Clinical Trial Participants feedback about Visit Burden:
Prompt for core ideas: Run this on big sets of open-text responses to quickly uncover main topics and frequency. (This is Specific’s default—it works in ChatGPT, too.)
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
Tip: Always give the AI context about your survey, audience, or goal. The results are dramatically better, especially with nuanced data from visit burden surveys. For example:
Analyze these responses from clinical trial participants about their experiences with site visit burden. My goal is to identify the most common pain points and areas for improvement in reducing patient travel and procedure complexity.
Prompt to go deeper on any theme: Use after running the core ideas prompt. For example:
Tell me more about travel distance challenges.
Prompt for specific topic validation: If you want to know whether anyone talked about a certain subject:
Did anyone talk about financial hardship? Include quotes.
If you’re seeking richer insights to influence protocol design or participant burden strategies, here are more targeted prompt ideas:
Prompt for personas: Use if you want to uncover distinct participant types with different needs.
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: To systematically surface top obstacles:
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 sentiment analysis: This is especially useful if you need to report on overall satisfaction:
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 and ideas: If your survey includes open text on improvement or requests:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
How Specific analyzes qualitative data by question type
Specific’s built-in AI analysis maps the structure of your survey questions to how results are summarized and surfaced:
Open-ended questions (with or without follow-ups): You get a comprehensive summary that captures what participants shared, plus grouped insights from all additional probing.
Multiple-choice with follow-ups: AI provides per-choice summaries of all responses linked to each option. If several participants cite “travel time to site” as a challenge and expand on it in a follow-up, you see exactly how—and how often—that worry appears.
NPS questions: For Net Promoter Score (NPS) items, you receive a distinct summary for each category—detractors, passives, promoters—based on the follow-ups tied to each score bracket.
You can replicate this in ChatGPT by manually filtering and structuring responses, but Specific saves hours by doing it out of the box. If you want practical tips on building strong survey structures, check out this guide on the best survey questions for Clinical Trial Participants about visit burden.
Dealing with the AI context size limit: practical tips
Handling a large volume of qualitative feedback (hundreds of long interview scripts, for instance) will eventually run into AI context window limits. Here’s how to tackle the “it won’t fit” problem—these two tricks are central to Specific, but you can use them in your workflow, too:
Filtering: Narrow your analysis by pre-filtering conversations. For example, you might just analyze responses where participants rated visit burden > 7/10, or only look at people who traveled more than 50 miles—according to recent research, the average travel distance for clinical trial participants has soared to 67 miles each way[2].
Cropping by question: Before sending data to the AI, crop to just the question threads that matter—rather than sharing the entire transcript. Instead of throwing 50 pages of conversation at ChatGPT, you might restrict the dataset to “Describe your biggest challenge with study visits.”
Specific’s AI-powered analysis lets you apply both these strategies instantly—so you always stay within context limits, and focus only on high-impact parts of your Visit Burden survey.
Collaborative features for analyzing Clinical Trial Participants survey responses
Collaboration is a known pain point—especially with large Clinical Trial Participants Visit Burden surveys. Differing team priorities, multiple stakeholders, and the challenges of sharing long, sensitive feedback transcripts can stall decision-making.
Instant team chat on responses: In Specific, you can analyze survey results just by chatting with AI, and every chat keeps track of who’s asking what. Multiple chats can run side by side—each with custom filters, angles, and intents. As you explore the data, each conversation is attributed to its creator, visible with avatar icons—so you see who’s leading each thread and keep everyone on the same page.
Crystal-clear audit trail: When collaborating, you can quickly jump into a colleague’s analysis, pick up where they left off, and add your perspective. This accelerates insights and greatly reduces duplicated effort.
Seamless knowledge sharing: You’re not just getting faster results—you get deeper, more widely shared understanding across the study, clinical operations, and even site teams. This model also helps when sharing findings with external partners or regulatory teams—everything is fully documented and traceable.
For a deeper dive into how to efficiently create and analyze these surveys, take a look at how to create clinical trial participants surveys about Visit Burden.
Create your Clinical Trial Participants survey about Visit Burden now
Collect better insights and analyze responses in minutes—using AI to identify what truly matters, not just what’s easy to count.