A hybrid approach to hospital quality monitoring based on google maps reviews: Integrating p-control charts and bidirectional encoder representations from transformers (BERT)
Bibliographic record
Abstract
This study investigates the utilization of Google Maps reviews to assess hospital service quality. Patient-generated reviews were analyzed using a sentiment analysis framework incorporating the Bidirectional Encoder Representations from Transformers (BERT) classification model. The p control chart was employed to monitor the distribution of negative sentiment. The results of the sentiment analysis revealed a predominance of positive reviews over negative ones. The BERT classifier achieved excellent performance, with AUC values of 99.95% and 93.72% for training and testing data, respectively. However, the p control chart indicated that the hospital's performance still requires improvement, as several observations fell outside the statistically controlled range. Common patient complaints centered on lengthy wait times and queues, highlighting areas for targeted quality enhancement initiatives. This research demonstrates the potential of leveraging patient feedback to inform hospital quality improvement efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".