This article will give you tips on how to analyze responses from a patient survey about telehealth experience. If you're working with data from patient feedback on virtual care, keep reading for practical advice on survey analysis with AI.
Choosing the right tools for analyzing patient survey responses
The approach and tools you should use depend on the form and structure of your patient survey data. With structured, quantitative data—like rating scales or multiple choice—you can quickly count and chart results using spreadsheet tools. For more complex, qualitative data from open-ended responses or follow-ups, you’ll need AI to find real insights without getting lost in the details.
Quantitative data: If you’re looking at numbers—think satisfaction rates, NPS scores, or how many people picked a certain answer—tools like Excel or Google Sheets do the job. You can filter, sort, and visualize this kind of information with basic formulas and built-in charts.
Qualitative data: Patients’ comments, descriptions, and thoughtful answers to open questions are where the nuance lives, especially when they’re telling you why they like or dislike telehealth. Reading every response yourself isn’t practical; meaningful analysis only happens when you use AI to summarize and extract common themes. Even with a few dozen responses, patterns can hide in subtle phrases or rare feedback, so leveraging specialized AI tools is essential to avoid missing out on actionable insights.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Direct chat-based analysis: You can export your patient survey data as plain text (typically from Excel or Google Sheets) and paste it into a chat with ChatGPT or similar AI models.
Manual process: This process lets you “talk” with the AI about your responses. However, copying raw text, formatting the data, and iterating on prompts is rarely convenient. You’ll spend extra time managing the workflow, and maintaining context (especially for large data sets) quickly becomes cumbersome. The result: basic exploration is possible, but deeper survey analysis gets tricky to scale or share.
All-in-one tool like Specific
Purpose-built for patient surveys: Specific is designed from the ground up for survey analysis, from creation to actionable insights. You build your patient survey (about telehealth experience or any topic), collect responses, and immediately start analyzing with AI—all in one space.
Higher quality data upfront: Because Specific surveys can ask personalized follow-up questions using AI, you get richer patient responses (see how automatic AI followup questions work for deeper insights).
Effortless analysis: The AI-powered analysis engine in Specific instantly summarizes all patient responses, identifies central themes, and turns unstructured feedback into clear, actionable patterns—no spreadsheets or manual copy-paste required.
Conversational exploration: You can “chat” directly with the AI about your results, layer on filters, and experiment with different prompts—much like ChatGPT, but with additional tools for managing large amounts of patient data. This makes the analytic workflow faster, more interactive, and more reliable for teams.
Useful prompts that you can use to analyze patient telehealth survey data
Using the right AI prompts is key for extracting real value from your survey responses—especially with rich, open feedback from patients. Here are the ones I find work best:
Prompt for core ideas: If you want a high-level overview—what patients care about most, in the clearest possible terms—this is your go-to prompt. It’s the cornerstone method used by Specific, but it also works well anywhere you use an AI.
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
AI always performs better if you give it more context about your survey, your goals, or your specific needs. For patient surveys on telehealth experiences, tell the AI things like what type of care was involved, who the respondents are, or what kind of changes you’re considering. For example:
We asked 100 patients about their experiences using telehealth for ongoing chronic care management. Our goal is to identify what works, uncover sources of frustration, and prioritize improvements for this service channel.
Once you get your list of core ideas, dive deeper by asking:
Prompt for digging into one theme: “Tell me more about XYZ (core idea)”
Prompt for a specific topic: This lets you fact-check your hypotheses or spot-check new angles.
Did anyone talk about (e.g., “waiting times”)? You can also add: "Include quotes".
Prompt for pain points and challenges: Perfect for surfacing recurring frustrations—such as tech glitches or communication issues—your patients faced:
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: To get a read on the general mood in patient feedback, use this:
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: Spotting new areas for service improvements or innovation is gold in healthcare. Try:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you're ready to design a new survey, check our detailed guide on how to create a patient survey about telehealth experience and explore the AI survey generator for patient telehealth experience to get started quickly.
How Specific analyzes qualitative data based on type of question
With Specific, you don’t have to manually categorize survey answers. Here’s what happens for each major patient survey question type:
Open-ended questions (with or without followups): The system groups all responses to a single question—plus any AI-powered follow-ups that clarify each answer—into a single unified summary. This gives you a concise, comprehensive view of patient perspectives.
Choices with followups: If patients select a structured answer, then explain “why” in a followup, Specific creates a separate summary of all open responses for each unique choice. You’ll see, for example, what all the “satisfied” patients said in their own words.
NPS (Net Promoter Score): For NPS surveys, you get a distinct summary for promoters, passives, and detractors. Each pulls together every follow-up response tied to that score segment—so you can understand, at a glance, what drives delight or dissatisfaction.
Of course, you can do something similar by manually filtering and copying segments into ChatGPT, but you’ll spend a lot more time sorting and reformatting your data. Specific makes it instant and less error-prone.
Learn more about AI-powered survey response analysis and how the workflow compares with manual approaches.
How to tackle challenges with AI context limits
One of the biggest frustrations when using general-purpose AIs (like ChatGPT) for survey analysis is running into context size limits: if there are too many patient responses, not everything fits at once. This means either truncating your data or missing out on themes buried in the “overflow.”
Specific solves this with two powerful tools:
Filtering: You can zero in on relevant responses by filtering conversations. Just choose which user replies or groups (e.g., only detractors, or only patients who mention technical barriers) you want the AI to analyze. This keeps the context small enough to stay within AI limits but ensures you zero in on the data that matters most.
Cropping: Select which questions—or even which parts of the survey—you want sent to the AI for analysis. Everything else is dropped, so you never waste precious context space on less relevant details. This technique lets you analyze larger cohorts of patient feedback, even in long, multi-topic surveys.
Pair these techniques for best results, whether you’re using Specific or charting your own workflow with exports and GPT tools.
Collaborative features for analyzing patient survey responses
Analyzing survey data on patient telehealth experience is rarely a solo act. Multiple stakeholders—medical staff, administrators, researchers—need to explore findings together and chase different lines of inquiry.
Real-time collaboration: With Specific, you can analyze responses simply by chatting with AI—and do so collaboratively. Each member of your team can open their own conversation thread (chat), ask unique questions, and set their filters or focus. That means less stepping on each other’s toes, more tailored insights.
Ownership and transparency: Every chat in Specific displays who created it—so it’s clear who asked what, and which line of investigation produced each insight. When you’re in a shared workspace, each AI chat message even displays the sender’s avatar, so collaboration feels more natural and you avoid confusion.
Instant sharing and feedback: Teammates can comment, suggest new analytic prompts, or steer the direction of the conversation in real time, eliminating bottlenecks that often happen with exported transcripts or static dashboards.
For deeper dives, check out our detailed article on the best questions for patient telehealth experience surveys and see our tips for using AI-powered survey editors in collaborative research.
Create your patient survey about telehealth experience now
Transform your telehealth research with Specific—launch a conversational survey, gather deeper patient feedback, and get instant AI-powered analysis that highlights what matters most.