Transforming Patient Feedback Into Actionable Insights Through Natural Language Processing: Knowledge Discovery and Action Research Study
Notice bibliographique
Résumé
Background: Patient feedback has emerged as a critical measure of health care quality and a key driver of organizational performance. Traditional manual analysis of unstructured patient feedback presents significant challenges as data volumes grow, making it difficult to extract meaningful patterns and actionable insights efficiently. Objective: We attempted to develop and evaluate a comprehensive methodology for analyzing patient feedback data using natural language processing and Knowledge Discovery in Databases (KDD) approaches, aiming to identify key patterns, themes, and variations in patient experiences across different demographic groups and care settings, and to translate these insights into actionable improvements in health care delivery. Methods: This study applied an integrated KDD-action research framework to analyze 126,134 patient feedback entries collected at Alexandra Hospital, Singapore, in 2023. A comprehensive suite of text mining techniques, including sentiment analysis, topic modeling, emotion detection, and aspect-based sentiment analysis, was employed to uncover patterns in patient-reported experiences. The dataset included 92,578 (73.4%) entries containing free-text comments, comprising 1,568,932 tokens with an average comment length of 16.9 words. Multiple analytical techniques were used to ensure the validity and reliability of the findings. Stakeholder engagement throughout the research process facilitated the translation of analytical insights into practical improvements. The study was granted an exemption from ethical review by the National Healthcare Group Domain Specific Review Board (number: NUS-IRB-2025-087E), with a waiver of informed consent granted for this retrospective analysis of deidentified patient feedback data. Results: Text mining analysis revealed a moderately positive overall sentiment across the feedback corpus (average polarity score: 0.42), with 68.8% (63,685/92,578) of comments classified as positive, 25.4% (23,515/92,578) as neutral, and 5.8% (5378/92,578) as negative. Topic modeling identified 10 distinct topics, including staff attitude and service (9443/92,578, 10.2%), health care staff professionalism (9350/92,578, 10.1%), hospital environment (9258/92,578, 10.0%), and waiting time (9258/92,578, 10.0%). Aspect-based sentiment analysis highlighted nurse attitude (sentiment score: 0.65), staff helpfulness (0.61), and doctor expertise (0.58) as the most positive aspects, while waiting time (-0.42) and billing transparency (-0.28) emerged as the most negative. Demographic segmentation revealed significant variations in patient priorities, with younger patients (<35 years) expressing 37% more concerns about digital accessibility and efficiency than older patients who valued face-to-face interactions 42% more highly. Implementation of targeted interventions based on these findings resulted in measurable improvements, including an 18% increase in waiting time satisfaction, a 15% improvement in doctor-patient communication ratings, and a 23% reduction in billing-related complaints. Conclusions: The integration of natural language processing techniques with KDD and action research principles provides a powerful framework for transforming unstructured patient feedback into actionable insights for health care improvement. This approach enables health care organizations to understand the complex patterns and drivers of patient experience, identify targeted improvement opportunities, and implement evidence-based initiatives that enhance care quality and patient-centeredness.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,053 | 0,097 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».