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How to use AI to analyze responses from patient survey about billing transparency

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Adam Sabla

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Aug 21, 2025

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This article will give you tips on how to analyze responses from a patient survey about billing transparency. If you want to get actionable insights from your survey, AI can help you make sense of complex survey data quickly and accurately.

Choosing the right tools for survey analysis

The approach and tooling you use really depends on the type and structure of the survey data you have. Let’s break it down simply:

  • Quantitative data:

    These are the counts—how many patients rated something a certain way or picked a specific answer. They’re straightforward to analyze using tools like Excel or Google Sheets.

  • Qualitative data:

    Here’s where it gets challenging. Open-ended feedback, stories from patients about billing confusion, or elaborations on their NPS scores—this can’t just be tallied up. If you’ve ever tried reading through hundreds of comments, you know it’s impossible to process them all manually. That’s where AI tools, with their knack for extracting themes and summarizing text, really shine.

There are two main approaches when you want to analyze qualitative survey responses:

ChatGPT or similar GPT tool for AI analysis

You can copy and paste your survey data into ChatGPT or another GPT-based tool and chat about it directly.

It’s flexible—you can ask follow-up questions, clarify something, or dig into specific topics on the fly.


However, the experience isn’t seamless. Exporting, cleaning, then pasting your data over and over can get tedious, especially with bigger surveys or when multiple people need access to the findings.

All-in-one tool like Specific

Platforms like Specific are purpose-built for survey collection and AI-powered analysis.

With Specific, you gather responses in a conversational way, and the AI automatically prompts for clarifying follow-up questions. This boosts the quality and depth of your data—no shallow answers or one-word gripes.


When it’s time for analysis, it’s almost instant: Specific automatically groups common themes, summarizes sentiments, and even lets you chat directly with the AI about your patient survey results—just like ChatGPT, but you get controls for filtering data, managing context, and segmenting by patient type or response.

You can read more about how Specific does AI survey response analysis and how it helps turn comments into actionable insights, all without fiddling with spreadsheets or scripts.

Useful prompts that you can use to analyze patient survey data on billing transparency

Prompts are the secret sauce for getting deeper insights from your billing transparency surveys. Here are the most useful prompts for analyzing what patients really say:

Prompt for core ideas:

This is hands down the best prompt for surfacing major themes in large survey datasets. It’s what we use at Specific, but it works in ChatGPT and similar tools 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

Give AI more context for better results:

Before pasting your responses, set the stage for the AI with your survey’s goal, the audience, and the situation. For example:


You are analyzing responses from a patient survey on hospital billing transparency. Our aim is to understand pain points, confusion, or frustration caused by unclear bills. The survey includes open-ended questions about what surprised or confused patients. Focus on what patients find unclear or challenging, as well as any requests for improvement.

Dig deeper into specific findings:

After surfacing a key trend, prompt the AI with:


“Tell me more about [core idea]”


Check for particular topics:

For quick scanning if a pain point or suggestion was mentioned:


“Did anyone talk about [billing estimate]?”

(You can add “Include quotes.” for richer answers.)


Identify patient personas:

Use this prompt to segment responses by common patient experiences:


“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.”


Unpack pain points and challenges:

Get the AI to cluster complaints or friction points:


“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.”


Extract motivations & drivers:

For understanding patient behaviors:


“From the survey conversations, extract the primary motivations, desires, or reasons patients express for their choices about hospitals or bill payment. Group similar motivations together and provide supporting evidence from the data.”


Sentiment analysis:

Quickly see if patients are generally frustrated, happy, or neutral:


“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.”


Collect suggestions & ideas:

Useful to capture patient fixes 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.”


Spot unmet needs & opportunities:

Find out where patients feel let down or what would improve their experience:


“Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by patients.”


You can always adjust the prompt wording to fit your survey focus. For more ideas, check out best questions to ask patients about billing transparency to inform both survey design and analysis.

How Specific handles qualitative data by question type

Analyzing billing transparency survey responses isn’t just about the raw text—it’s about how those responses map to your survey’s structure. Here’s how Specific automatically organizes and summarizes analysis by question type:

  • Open-ended questions (including follow-ups):

    You get summaries that group both initial responses and any automatically-triggered follow-up questions. This gives you not just what people said, but why they said it—a crucial distinction when tackling thorny issues like unclear medical bills.

  • Choices with follow-ups:

    Let’s say you ask, “Did you understand your bill?” and offer “Yes” or “No.” For each answer, you get a separate nutshell summary of all follow-ups related to that specific group. You spot trends instantly.

  • NPS questions:

    Patients rank from 0–10, and for each segment (detractors/passives/promoters), the AI groups and distills all related follow-ups. You know exactly what frustrates detractors or delights promoters with zero manual tagging.

You can absolutely do similar segmentation in ChatGPT, but it usually means a lot of copy-pasting, slicing data, and re-running analyses. Specific automates the organization, so you spend less time hunting for answers and more time driving improvements. To see this approach in action, try creating your own AI-powered patient survey on billing transparency and analyzing real results.

Dealing with AI context size limits

One of the biggest challenges with AI survey analysis is the sheer volume of responses—context window limits get in the way when you have hundreds of patient comments. If you don’t manage this, the AI either truncates the data or misses important trends. Here’s how you can tackle it:

  • Filtering: Filter conversations by relevant responses. For example, only include patients who expressed confusion about billing, or who selected “No” when asked if they understood their bill. This ensures the most pertinent feedback gets analyzed—no noise.

  • Cropping: Only send the questions (and their responses) that matter most. If your survey is long, you can crop the data so only billing-specific parts get summarized, making the analysis snappier and staying within context limits.

Specific automates both these steps, allowing you to segment, filter, and crop your data before sending it to the AI for analysis. Learn more about these AI response analysis strategies that keep your workflow efficient.

Collaborative features for analyzing patient survey responses

Making sense of billing transparency survey findings can get messy fast—especially when several people need to weigh in, from admin staff to finance leads.


Analyze survey data just by chatting:

With Specific, you get a collaborative AI chat environment for survey results. It’s as easy as messaging a teammate, but you’re talking with the AI to uncover patterns and insights.


Multiple chats, multiple filters:

Each chat session lets you explore different angles—maybe one chat looks only at patients who were late on their medical bills (nearly half, according to a 2024 Waystar survey [3]). Another might zoom in on those frustrated by federal billing requirements that hospitals themselves often fall short on [1].


See who’s exploring what:

Every chat shows who started it and who contributed, with clear avatars. This creates an audit trail, reduces duplicated work, and makes it easy to ping the right person about a finding.


Work cross-functionally, fast: Instead of wrangling exported files or sending PDFs back and forth, your whole team can interact with survey analysis right inside Specific. It’s designed for busy healthcare teams, letting you collaborate on billing transparency insights quickly and securely. For ideas on making the most of cross-team analysis, check out this guide on creating effective patient surveys on billing transparency.

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Sources

  1. TechTarget. Little progress made with hospital price transparency compliance: 2024 report by PatientRightsAdvocate.org.

  2. Axios. Health Affairs study on hospital upcoding and increased payments, 2024.

  3. Waystar. 2024 Consumer Price Transparency Survey: More than half of consumers receive unexpected medical bills.

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.