This article will give you tips on how to analyze responses from a patient survey about test results communication using AI, so you can understand what your patients really care about and turn raw feedback into useful insights.
Choosing the right tools to analyze patient survey data
The approach—and best tool—depends on the type of data you’ve collected from your patient survey about test results communication. Here’s how I think about it:
Quantitative data: For straightforward, countable answers (like “how many patients preferred phone calls?”), tools like Excel or Google Sheets work perfectly. You can quickly tally percentages, filter, and visualize multiple-choice responses.
Qualitative data: Open-ended feedback (“describe how you felt about receiving your test results”) is where things get tricky. When you have dozens or hundreds of patient comments, it’s impossible to read and interpret everything manually. That’s why I recommend using AI tools to rapidly analyze and distill these responses.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
A simple route is to export your patient comments from your survey and paste them into ChatGPT (or another LLM tool). Then, you can ask questions about the data, find patterns, and generate summaries.
This is usually okay for small datasets. But when you have a lot of responses, it quickly becomes tedious and hard to manage. You’ll have to organize, crop, or segment your data and manage context size limits manually. Chatting with a generic LLM lacks all the convenience and structure you get in a dedicated tool for survey analysis.
All-in-one tool like Specific
Specific is built specifically for these kinds of surveys. It not only collects qualitative feedback in a conversational style but also analyzes responses using AI.
When you use Specific to build your survey, it automatically asks follow-up questions as patients reply. That leads to higher quality data—you get more detail and context, not just one-word answers. It’s especially valuable since studies show that only 44% of patients actually receive their results in their preferred way, a disconnect that leads to dissatisfaction and is best explored through open, conversational responses. [1]
AI-powered analysis in Specific summarizes all responses, finds the main themes, and turns them into actionable insights—instantly and without spreadsheets or manual sorting. You can chat about your results with AI, just like in ChatGPT, but with features like in-context conversation management, targeted filtering, and collaborative analysis. See how it works in detail on the AI survey response analysis page.
Useful prompts that you can use for patient test results communication survey analysis
Strong prompting unlocks more value from AI. Here are some battle-tested prompts for analyzing patient survey responses about test results communication:
Prompt for core ideas: The best starting point for summarizing lots of rich feedback is asking for the main themes:
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
The AI consistently does a better job if you give it relevant background: your goals, your survey method, and any special context about your patient group. For example:
I ran a patient survey about how people prefer to receive test results from our clinic. We serve mostly adults, and the main goal was to find pain points in our current communication process. Please analyze the responses for key patterns and recommendations.
Prompt to go deeper on a specific theme: Once you spot an insight (like “patients want more confidentiality”), explore further:
Tell me more about confidentiality concerns in these responses.
Prompt to check for a specific issue: Validate if a topic came up—especially useful if you’re investigating something specific about result delivery (for example, secure messaging):
Did anyone talk about using secure online portals? Include quotes.
Prompt for personas: Great for understanding distinct patient types and how different groups prefer to get their results.
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: Directly surface common frustrations patients face in the results process.
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: Useful when you want to understand why people choose certain result delivery options (e.g., why they like phone calls or prefer written results):
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 sentiment analysis: Quickly understand the overall mood of your patients when they talk about your test results process—positive, negative, 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.
Prompt for unmet needs: Pinpoint gaps in your communication, so you know where to focus improvements:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
Dive deeper with more insights: If you need templates, benchmarking, or more prompt ideas, check out our article on the best questions for patient test results communication surveys.
How Specific analyzes different types of patient survey questions
I like that Specific isn’t just about the data dump—it intelligently breaks down results by question type, which is especially useful in patient communication surveys:
Open-ended questions (with or without follow-ups): You get a cohesive summary for all responses to each question, with further breakdown for any follow-up questions related to that response. Everything is grouped so you see not only what was said, but also the context on why and how patients responded the way they did.
Multiple-choice with follow-ups: Each option (for example, phone, portal, letter) gets its own summary, benchmarked against qualitative feedback on the reasons behind that choice.
NPS questions: Detractors, passives, and promoters each have their responses summarized separately—including all their follow-up comments, which highlights what each group values or finds frustrating. This mirrors the evidence that satisfaction rates for test result communication jump when patient preferences are met, especially for timely and confidential communication. [3] [4]
You could absolutely do this in ChatGPT, but it’s a lot more work splitting and feeding each group’s responses in manually.
If you want to build such a survey from scratch, the AI survey generator for patient test results communication can give you a running start.
How to handle context size limits in AI analysis of patient responses
Context—the total amount of text an AI can process at once—is a hard limit in every LLM. And for patient surveys, you can easily run into this ceiling if you have too many free-text responses.
There are two main solutions, both available in Specific out of the box, but you can use these methods if you’re working with any AI:
Filtering: Narrow the data by focusing just on the conversations (survey responses) where the patient replied to a specific question, or selected a certain answer. That way, the AI only processes what’s most relevant to your current question, not the entire dataset.
Cropping: Instead of sending every question and answer, crop your export to include only the questions you want the AI to analyze. This makes maximum use of the context window and brings out more focused insights.
Specific manages this with simple settings for including/excluding questions and applying filters on the fly. This is particularly valuable considering that automated systems for test result management have been shown to significantly improve patient satisfaction, so being able to analyze a large volume of feedback efficiently is key if you want changes to have real impact. [2]
Collaborative features for analyzing patient survey responses
Working in a team to analyze patient survey data presents a common challenge: making sure everyone is on the same page while exploring different perspectives.
Specific solves this with AI chat features designed for teamwork. You can create multiple distinct chat threads, each with unique filters applied—for example, one focused on confidentiality concerns, another on preferences for telephone calls. Each thread shows the creator’s name and avatar, so your colleagues always know whose insights they’re reading.
Transparency is key: When collaborating in Specific, sender avatars show who asked each question or shared an idea in the chat. This way, feedback and insights from different members of the healthcare team are always attributed, making it easy to build a collective understanding and document decisions.
Explore and share findings as you like: Discuss new findings, pose questions, and iterate through hypotheses. The AI-powered chat means everyone—even those less comfortable with raw data—can join in and get instant value.
You can learn about survey set-up and collaboration tips in our guide on how to create patient surveys about test results communication.
Create your patient survey about test results communication now
Start collecting and analyzing patient feedback with AI-powered tools—capture deeper insights, align your process with your patients’ real preferences, and transform communication in your healthcare practice today.