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How to use AI to analyze responses from patient survey about communication with nurses

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Adam Sabla

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Aug 20, 2025

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This article will give you tips on how to analyze responses and data from a patient survey about communication with nurses, helping you uncover actionable insights using AI-powered survey analysis.

Choosing the right tools for analyzing survey responses

Your approach depends on the structure of your data—whether it's numbers or narratives, each type requires a distinct strategy. For quantitative data—like multiple-choice responses or ratings—tools such as Excel or Google Sheets are perfect for counting, filtering, and aggregating. It's all about the numbers and their distribution.

  • Quantitative data: Think questions like “How satisfied were you with the nurse’s communication?” These responses are easy to summarize in a spreadsheet—just a few formulas, and you see your trends.

  • Qualitative data: Open-ended questions—like “What did you appreciate most about your interactions with nurses?”—need a different approach. If you're dealing with even a couple dozen responses, reading every answer and discerning themes gets overwhelming fast. Here, AI tools become indispensable. GPT-based platforms can summarize, synthesize, and extract themes from large volumes of qualitative feedback in minutes—not hours.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

One option is using ChatGPT or a similar large language model. You paste your exported survey data and engage in a chat to analyze your results. The main hurdle is that this method is often clunky—you need to format your data so it’s digestible, and analyze in chunks if your dataset is long. If your survey has follow-up questions or branching logic, keeping track of which answer goes with which question can become tedious.

Plus, you’re limited by context size. ChatGPT will only process a fixed amount of text at a time, so analyzing hundreds of responses usually means lots of copy-pasting and splitting messages manually.

All-in-one tool like Specific

Specific is designed specifically (pun intended) for surveys and feedback—not as a general chatbot. You can collect responses using surveys that feel like a conversation, with AI-powered follow-up questions that draw out deeper insights than standard forms. This means you get richer, more nuanced responses right from the start.

Instant AI-powered analysis: When you collect responses in Specific, the platform instantly summarizes answers, identifies recurring themes, and transforms raw feedback into concise insights. No manual work—just clear, actionable summaries for each question or segment.

Conversational deep-dive: You can chat directly with AI about your results—“What were the top recurring issues?”—and the system leverages all your qualitative data, with features to filter or direct focus onto specific subsets of feedback. It even highlights what’s most frequently mentioned.

Seamless data management: Your survey and response data stay organized within Specific, saving you from messy exports or version control issues. Building your own patient survey about communication with nurses is point-and-click easy, and all insights are available instantly inside the platform.

Useful prompts that you can use for patient survey response analysis

Prompts let you guide the AI to analyze feedback just how you need it. Here are the most helpful prompts—easy to use whether you’re analyzing directly in Specific or copying survey text into ChatGPT or another AI assistant.

Prompt for core ideas: This is your go-to prompt for extracting top themes from a large batch of open-ended responses (also used internally by Specific):

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

For even better insights, give the AI context about your survey—what you’re trying to achieve or the particular audience. Here’s an example:

You are analyzing responses from a patient survey about communication with nurses, focused on uncovering what aspects of nurse communication impact patient satisfaction and safety. My main goal is to identify recurring themes and actionable insights to improve nurse–patient interactions.

After you’ve uncovered your core ideas, you can steer deeper analysis:

Follow-up prompt: “Tell me more about XYZ (core idea)”—useful to drill into any single theme or pattern.

Prompt for specific topic:

Did anyone talk about [XYZ]? Include quotes.

To get more granular or strategic about your results, try the following:

Prompt for personas:

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 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 unmet needs & opportunities:

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

If you want more prompt inspiration or want to craft an even more tailored survey, check out these best survey questions for patient communication with nurses—a really helpful resource if you’re building from scratch or iterating on your approach.

How Specific analyzes qualitative data by question type

Specific breaks down analysis by question type, making sense of even complex, branching surveys:

  • Open-ended questions with or without follow-ups: Every response is gathered and synthesized into a clear summary for that question, with the option to view summaries for related follow-up responses too.

  • Multiple-choice questions with follow-ups: Specific provides a summary of follow-up answers for each choice. For example, if you ask, “Were you satisfied with your nurse communication?” with choices for “Yes/No,” you can see what themes and explanations were shared per group.

  • NPS questions: For Net Promoter Score (NPS) surveys, responses are grouped as detractors, passives, or promoters, and each group gets its own qualitative summary from the follow-up questions—so you spot differences in sentiment and drivers at a glance.

You can recreate this level of detail using ChatGPT, but it usually involves more manual work—copying and sorting replies for each question, then prompting the AI separately for each segment or category.

Read more about how these summaries work in depth in AI survey response analysis with Specific.

How to handle AI context limits for larger surveys

AI tools like GPT have a context size limit: If your survey has too many responses or long answers, you’ll eventually hit a wall—the AI can only process a limited amount of data at once. This is especially common if you’re surveying a large patient group, which is often the case in hospitals or clinics.

  • Filtering: Zero in on specific subsets before analyzing. You can filter by people who replied to certain questions or chose a particular answer. This approach reduces volume, keeps things relevant, and is available seamlessly within Specific.

  • Cropping: Instead of analyzing every question, you can select just the question(s) you care about, sending only those parts to the AI. More results fit within the context window, and you get focused insights—without overload.

If you're curious about how filtering and cropping work, read our deep dive into Specific's AI analysis features.

Collaborative features for analyzing patient survey responses

Let’s be honest: collaboration on patient communication with nurses surveys has always been slow and fragmented, especially when sharing results across departments or shifts.

Chat-driven analysis for teams: In Specific, you can analyze and discuss responses together—chatting with AI about your survey data, and everyone on your team can join the conversation. This beats spreadsheets and static dashboards every time.

Multiple chats for different focuses: You can open several chats at once, each with unique AI prompts or filters. A chat could focus just on “patients who reported challenges with language barriers,” while another looks at overall sentiment. Every chat is labeled by the person who started it—making it crystal clear who is working on what.

Transparent collaboration: When you’re collaborating, every AI chat message shows the sender’s avatar, so you can attribute ideas, questions, and analysis to the right person. This makes it easy for teams to follow the conversation, hand off, or pick up where someone else left off.

For more hands-on tips about survey creation and collaborative analysis, you might love our guide on how to create a patient survey about communication with nurses.

Create your patient survey about communication with nurses now

Kickstart your patient feedback collection today—create a conversational survey that uncovers what matters most about nurse–patient communication, analyzes responses instantly with AI, and delivers insights you can act on.

Create your survey

Try it out. It's fun!

Sources

  1. fiercehealthcare.com. Better nurse communication means better patient safety and satisfaction

  2. SAGE Journals. Patient perception of nurse communication in Ethiopia

  3. PubMed. Nurse communication satisfaction and patient safety culture

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.