Patient-Centered Outpatient Process Optimization System Based on Intelligent Guidance in a Large Tertiary Hospital in China: Implementation Report
Notice bibliographique
Résumé
Background: In large tertiary hospitals across China, outpatient patients often encounter the "three longs and one short" (long registration time, long waiting time, long time for medicine collection, and short time for medical treatment) phenomenon. This scenario contributes to suboptimal patient experiences and declining satisfaction with health care services. To address the issue of long waiting times, many hospitals in China have implemented a range of measures. However, these measures have only improved individual aspects of the patient experience, with limited overall impact. Currently, there is a lack of comprehensive, intelligent reform for the entire patient service process in the medical system. Therefore, there is an urgent need to integrate and optimize the entire patient service process, providing real-time intelligent guidance within hospitals. This would help reduce waiting times for patients and enhance their satisfaction. Objective: This study aims to introduce a patient-centered intelligent guidance system and report on the impact of its implementation on outpatient waiting times and patient satisfaction in hospitals. Methods: The intelligent guidance system was designed with a patient-centered approach, leveraging internet and big data technologies. The system seamlessly connects various steps of the outpatient medical process, facilitating functions including automated check-in and comprehensive intelligent guidance for patients' medical visits, thus enhancing the efficiency and quality of health care delivery. This system has been implemented in a tertiary hospital in China. To assess the system's effectiveness, we compared outpatient visit data, waiting time data, and patient satisfaction levels between the preimplementation and postimplementation periods from 2019 to 2022. We analyzed the changes in patients' average waiting times and satisfaction levels after the system was implemented. Results: One year after the introduction of the intelligent guidance system, the number of outpatient visits increased from 5,067,958 to 5,456,151. The waiting time for outpatient patients was significantly reduced. The waiting time for consultation decreased by 2.84 minutes (mean 41.14, SD 2.31 min vs mean 38.30, SD 1.89 min; P<.001). The waiting time for examination decreased by 3.35 minutes (mean 47.83, SD 1.10 min vs mean 44.48, SD 1.67 min; P<.001). Consultation time increased to 3.43 minutes (mean 2.85, SD 0.03 min vs mean 3.43, SD 0.26 min; P<.001). After the system was launched, patient satisfaction increased from 89.99% (SD 2.78%) in 2021 to 92.72% (SD 0.18%) in 2022 (P=.005). Conclusions: The patient-centered intelligent guidance system reported in this study proved beneficial for tertiary medical institutions striving to alleviate the outpatient burden caused by prolonged waiting times. Through continuous transformation and upgrading of the outpatient service process centered on patients, the efficiency of outpatient services and patient satisfaction improved. Therefore, the patient-centered principle method and process integration concepts for the system can be further promoted and implemented.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».