Patient Satisfaction Survey Analysis Pack
Three documents and three sheets that cross-reference comment themes against your lowest-scoring dimension, not just count how often each one comes up.
Free download · No account needed
Every patient experience survey produces two datasets: composite scores by dimension, and whatever patients wrote in the comment box. Almost every practice reads them apart, then ranks the comment themes by how often each one shows up and calls the top row the priority. That ordering answers a different question than the one that matters. It tells you what patients talk about most, not which theme is actually driving the one dimension that scored below the others.
The CAHPS Clinician & Group Survey scores four composite measures built from several items each, plus one overall rating. A group reporting under MIPS can run the CAHPS for MIPS Survey as one of the six measures it selects, which CMS says covers ten domains it calls summary survey measures. Whichever instrument you run, this pack finds the respondents who scored your lowest composite in its bottom response category, then checks each comment theme's share inside that group against its share among everyone else.
At Wrenfield Primary Care, a fictional four-provider practice, Access scored 61 percent top-box, 29 points below Office Staff at 90, out of 140 completed surveys. Front desk friendliness led the Comment Theme Register with 31 mentions against 29 for wait and scheduling delay, so a register sorted by count sends the action plan after friendliness first. Cross-referenced against the 34 respondents who scored Access worst, wait and scheduling delay is 8.4 times more concentrated among them than everyone else, and front desk friendliness is not concentrated there at all.
What's in the pack
Score by Dimension and Provider
Every composite's top-box score, by provider and practice-wide, respondent counts included. If the CAHPS for MIPS Survey is one of your six measures, its results land in the same sheet.
Comment Theme Register
Every coded theme with its raw mention count and its concentration ratio against the lowest-scoring dimension's bottom-box respondents, side by side, so a high-count theme that is not the cause stays visible instead of getting deleted.
Findings Summary
States the dimension ranking, the point gap between the lowest and the highest, and which theme the cross-reference actually tied to the low dimension, distinct from any high-count theme that was not.
Action Plan
Every action item names the exact dimension it is meant to move and the theme that justified it. A high-count theme that was not concentrated there gets logged separately rather than funded on the wrong evidence.
Staff Communication
A slide-by-slide outline for presenting the findings to staff or the board, holding the content of each slide rather than its design. Leads with the evidence before the ask.
Action Tracker
One row per action item, carrying its dimension, its owner and a target date, so next cycle's survey can check whether the score it was meant to move actually moved. Where a low dimension is a contract quality gate, a value-based contract performance review prices what that movement is worth.
How to use it
- 1
Open in River, or download it
Open the pack in River and let the agent build the cross-reference from your real survey export, or download the blank Word and CSV files and work through them yourself.
- 2
Send the scores and the comments
The dimension scores, broken out by provider where you have it, and the free-text comments themselves rather than a summary of what they say.
- 3
Read the cross-reference, not the count
Which theme is actually concentrated among the respondents who scored your lowest dimension worst, against everyone else who left a comment.
- 4
Act on the dimension it points to
Fund the actions tied to the concentrated theme first, and log any high-count theme that was not concentrated there as its own separate item.
Frequently asked questions
Is this template free?
Yes. Download the three documents and three sheets as Word and CSV files with no signup and no card. "Edit with AI" is a separate, optional path for practices that want the agent to build the cross-reference from their own export. The rest of the library is at the template library.
What format are the downloaded files?
Word documents (.docx) for the Findings Summary, the Action Plan and the Staff Communication outline, and CSV (.csv) for the three sheets, zipped into one download. They open natively in Word, Pages, Google Docs, Excel, Numbers and Sheets.
Why not just rank comment themes by how often they come up?
Because a theme can lead the register by count for reasons that have nothing to do with your lowest dimension. In the worked example, the highest-count theme was mentioned no more by the low-Access group than by anyone else, while a lower-count theme was mentioned 8.4 times more often specifically among them.
What if our survey is not a CG-CAHPS instrument?
The concentration method does not depend on CG-CAHPS specifically. It needs a survey that reports more than one dimension and comments that can be traced back to the respondent who wrote them. Read your instrument's own scoring guidance for what its dimensions actually are before running the cross-reference.
Does a low patient experience score cost the practice money?
Sometimes, when patient experience is one of the terms a contract gates payment on. A value-based contract performance review reads the arrangement itself rather than assuming every quality term converts to dollars the same way.
How many comments does a theme need before the ratio means anything?
There is no fixed threshold, but a ratio built on a handful of comments carries less confidence than one built on dozens, and the register says so rather than presenting every ratio with equal certainty. Treat a decisive-looking ratio on very few comments as a lead to watch, not a finding to act on yet.
Does this decide what to do about a low score?
No. It names which theme the evidence actually ties to your lowest dimension and which one it does not. Deciding whether a proposed fix is realistic, and whether it addresses the specific comments behind the theme, is a judgment call for whoever runs the practice.
Find the comment theme actually driving your lowest score
Download the blank pack as Word and CSV files, or open this exact pack in River and let the agent cross-reference your comments against the dimension that scored worst.
Edit with AI
Comment Theme Register, screening view
The theme with the most mentions is not the one holding Access down
Illustrative rows for a fictional practice, Wrenfield Primary Care. 140 completed surveys, 96 with a comment.
Front desk friendliness has the higher raw count. Wait / scheduling delay is mentioned by 76.7 percent of the patients who scored Access worst, against 9.1 percent of everyone else: 8.4 times more concentrated.
Remaining columns: Everyone Else's Mentions, Everyone Else's Rate, Read.