Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction

Chi Zhang; Jhonatan Medri; Jiayan Zhou; Jin Jin; Junjie Xiong; Lingyao Li; Siyuan Ma; Xiaoran Xu; Yongfeng Zhang; Zhaoqian Xue

arxiv: 2503.20981 · v2 · pith:ZMYQXVSKnew · submitted 2025-03-26 · 💻 cs.CL · cs.AI· cs.SI

Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction

Xiaoran Xu , Zhaoqian Xue , Chi Zhang , Jhonatan Medri , Junjie Xiong , Jiayan Zhou , Jin Jin , Yongfeng Zhang

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Siyuan Ma Lingyao Li

This is my paper

Pith reviewed 2026-05-22 21:56 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.SI

keywords urgent carepatient satisfactionLLM analysisonline reviewssentiment analysisaspect-based extractionmultivariate regression

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The pith

LLM analysis of Google Maps reviews identifies interpersonal factors and operational efficiency as the main drivers of urgent care patient satisfaction.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper collects online reviews of urgent care sites and applies GPT prompts to score each review on five separate aspects of the visit experience. It then runs statistical models to test which aspects still predict overall patient ratings after accounting for local demographics. A reader would care because the approach turns abundant public comments into scalable evidence about what actually improves satisfaction, without needing new surveys. Only population density shows a small link among the background factors examined.

Core claim

Prompt-engineered GPT analysis of Google Maps reviews across the DMV and Florida regions shows interpersonal factors and operational efficiency as the strongest predictors of satisfaction. Technical quality, finances, and facilities show no significant independent effects once adjusted in multivariate models. Among socioeconomic and demographic variables, only population density has a significant but modest association with ratings.

What carries the argument

Aspect-based sentiment extraction from review text using GPT prompts, combined with geospatial mapping and multivariate regression against Census Block Group characteristics.

If this is right

Urgent care centers should direct resources toward improving staff interactions and shortening wait times rather than toward equipment or billing changes.
Review data can reveal local differences in perceived care quality at scale without launching new patient surveys.
Socioeconomic targeting of interventions may be less necessary, since most demographic measures show no link to ratings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same review-analysis pipeline could measure whether satisfaction shifts after a clinic changes its scheduling or training practices.
The approach could transfer to other outpatient services where online reviews are plentiful.
Linking the sentiment scores to actual clinical outcome records would test whether review-derived drivers match measurable health effects.

Load-bearing premise

The GPT prompts accurately extract aspect sentiments from the reviews without systematic bias or misclassification.

What would settle it

Human re-coding of a random sample of the same reviews followed by re-running the multivariate models; if interpersonal factors and operational efficiency no longer emerge as the top predictors, the central result would not hold.

read the original abstract

Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limited scope, time, and spatial coverage. Crowdsourcing through online reviews or social media offers a valuable approach to gaining such insights. With recent advancements in large language models (LLMs), extracting nuanced perceptions from reviews has become feasible. This study collects Google Maps reviews across the DMV and Florida areas and conducts prompt engineering with the GPT model to analyze the aspect-based sentiment of urgent care. We first analyze the geospatial patterns of various aspects, including interpersonal factors, operational efficiency, technical quality, finances, and facilities. Next, we determine Census Block Group (CBG)-level characteristics underpinning differences in public perception, including population density, median income, GINI Index, rent-to-income ratio, household below poverty rate, no insurance rate, and unemployment rate. Our results show that interpersonal factors and operational efficiency emerge as the strongest determinants of patient satisfaction in urgent care, while technical quality, finances, and facilities show no significant independent effects when adjusted for in multivariate models. Among socioeconomic and demographic factors, only population density demonstrates a significant but modest association with patient ratings, while the remaining factors exhibit no significant correlations. Overall, this study highlights the potential of crowdsourcing to uncover the key factors that matter to residents and provide valuable insights for stakeholders to improve public satisfaction with urgent care.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The headline results on satisfaction drivers rest on unvalidated GPT aspect extraction, so the multivariate claims stay hard to evaluate from the abstract alone.

read the letter

The paper applies LLM prompt engineering to pull aspect-based sentiments from Google Maps urgent care reviews in the DMV and Florida regions, then links those to census block group variables and runs multivariate models. That combination of data sources and the resulting driver rankings (interpersonal factors and operational efficiency strongest, technical quality and finances not significant after adjustment, only population density mattering among demographics) is a new empirical slice even if the underlying technique has been used elsewhere. The work does a clean job of framing the problem around scalable crowdsourced evidence and showing geospatial patterns before the regressions. It also keeps the analysis focused on five clear aspects and reports adjusted associations rather than raw correlations. Those steps are straightforward and useful for an observational study. The soft spots are the missing pieces that matter most for the claims. The abstract gives no sample sizes, no validation metrics or human agreement scores for the GPT extraction step, no model specification details, and no discussion of review selection bias or how the prompt handles ambiguous language. Without those, the regression coefficients and significance tests cannot be checked for robustness. The stress-test concern about systematic misclassification in the aspect labels lands directly because the entire driver analysis flows from those labels. This is an observational analysis, not a derivation, so the results stand or fall on the data quality and extraction accuracy. The paper is for health services researchers or NLP groups looking at patient experience data at scale. A reader interested in urgent care operations or community health metrics could pull practical angles from the rankings if the methods hold up. It deserves a serious referee once the authors supply the full methods, validation, and data description; the current abstract alone is too thin to judge the central claims.

Referee Report

2 major / 0 minor

Summary. The manuscript collects Google Maps reviews of urgent care facilities in the DMV and Florida areas and applies GPT prompt engineering to extract aspect-based sentiments across five aspects (interpersonal factors, operational efficiency, technical quality, finances, facilities). It examines geospatial patterns of these aspects and fits multivariate models using Census Block Group-level socioeconomic and demographic covariates (population density, median income, GINI Index, rent-to-income ratio, poverty rate, uninsured rate, unemployment) to identify drivers of patient satisfaction ratings. The central claims are that interpersonal factors and operational efficiency are the strongest determinants, technical quality/finances/facilities show no significant independent effects after adjustment, and only population density exhibits a modest significant association among the demographic variables.

Significance. If the GPT extraction step proves accurate and unbiased, the work illustrates how LLM-based crowdsourcing of online reviews can scale analysis of patient priorities beyond traditional surveys and yield actionable insights for urgent care operations. The multivariate adjustment for multiple CBG covariates is a methodological strength relative to purely descriptive approaches. At present, however, the absence of any validation or specification details prevents evaluation of whether these associations are reliable.

major comments (2)

[Abstract] Abstract: the reported multivariate results (interpersonal factors and operational efficiency as strongest determinants; no independent effects for technical quality, finances, and facilities; only population density significant) are presented without sample sizes, regression model type, covariate list, coefficient magnitudes, standard errors, or p-values, rendering it impossible to assess the statistical support for the headline claims.
[Abstract] Abstract: the prompt-engineered GPT extraction of the five aspect sentiments is the sole input to all geospatial and regression analyses, yet the abstract supplies no validation metrics, inter-rater agreement with human coders, error analysis by aspect or review type, or discussion of potential systematic misclassification, which is the load-bearing assumption for every downstream conclusion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract below and commit to revisions that improve transparency without altering the core claims.

read point-by-point responses

Referee: [Abstract] Abstract: the reported multivariate results (interpersonal factors and operational efficiency as strongest determinants; no independent effects for technical quality, finances, and facilities; only population density significant) are presented without sample sizes, regression model type, covariate list, coefficient magnitudes, standard errors, or p-values, rendering it impossible to assess the statistical support for the headline claims.

Authors: We agree the abstract is too terse on statistical support. The full manuscript reports a sample of reviews from the DMV and Florida areas, uses multivariate linear regression with the listed CBG covariates, and provides coefficients, standard errors, and p-values in the results. We will revise the abstract to state the sample size, confirm the model type, and note the significance levels and directions for the key findings on interpersonal factors, operational efficiency, and population density. revision: yes
Referee: [Abstract] Abstract: the prompt-engineered GPT extraction of the five aspect sentiments is the sole input to all geospatial and regression analyses, yet the abstract supplies no validation metrics, inter-rater agreement with human coders, error analysis by aspect or review type, or discussion of potential systematic misclassification, which is the load-bearing assumption for every downstream conclusion.

Authors: We acknowledge that the abstract omits any mention of validation for the GPT aspect extraction, which is a central methodological step. The manuscript describes the prompt engineering but does not currently report quantitative validation metrics or error analysis. We will revise the abstract to note the validation approach and expand the methods section with inter-rater agreement, aspect-specific error rates, and discussion of potential misclassification biases. revision: yes

Circularity Check

0 steps flagged

No significant circularity in observational LLM-assisted analysis

full rationale

The paper describes an observational pipeline: collect Google Maps reviews, apply prompt-engineered GPT for aspect-based sentiment extraction, then run geospatial and multivariate statistical analyses on the resulting labels. No equations, fitted parameters renamed as predictions, self-citations, or uniqueness theorems appear in the provided text. The reported associations (interpersonal/operational factors as strongest drivers, etc.) are outputs of standard regression on the extracted features rather than quantities defined by construction from the same fitted values. The LLM extraction step is a methodological assumption whose accuracy is external to the paper, but it does not create a self-referential loop within the derivation chain.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The study rests primarily on domain assumptions about data quality rather than new free parameters or invented entities. The five sentiment aspects are chosen by the authors and treated as given.

free parameters (1)

choice of five aspects
Interpersonal factors, operational efficiency, technical quality, finances, and facilities are selected as the analysis dimensions; their definition and weighting are not derived from data.

axioms (2)

domain assumption Google Maps reviews provide a representative sample of patient experiences at urgent care facilities
The analysis treats the collected reviews as valid input for public perception without adjustment for selection or demographic biases in who posts reviews.
domain assumption GPT prompt engineering produces unbiased aspect-level sentiment labels
The method assumes the LLM output faithfully reflects review content across all aspects and regions.

pith-pipeline@v0.9.0 · 5794 in / 1297 out tokens · 48441 ms · 2026-05-22T21:56:28.536352+00:00 · methodology

Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction

Core claim

What carries the argument

If this is right

Where Pith is reading between the lines

Load-bearing premise

What would settle it

discussion (0)