A Mobile-Based Patient-Centric Passive System for Guiding Patients Through the Hospital Workflow: Design and Development
Notice bibliographique
Résumé
BACKGROUND: A hospital is an unfamiliar place to patients because of its style, atmosphere, and procedures. These hospital characteristics cause patients to become confused about responding to protocols, which slows down the procedural flows. Some additional information technology infrastructure facilities and human resources may be needed to solve these problems. However, this solution needs high investment and cannot guarantee an accuracy of information sent to patients. To handle this limitation, EasyHos has been developed to help patients recognize their status (for example, "waiting for an appointment at 11am") during their stay in a hospital using all existing infrastructure and hospital data and without changing existing hospital's process. OBJECTIVE: The objective of this study was to provide a design of the EasyHos system and the case study in hospitals in Thailand. The design is usable and repeatable for small- and medium-sized hospitals where internet infrastructure is in place. METHODS: The EasyHos system has been designed based on existing infrastructure, hospital data and hospital processes. The main components include mobile devices, existing hospital data, wireless communication network. The EasyHos was deployed at 2 hospitals in Thailand, one small and the other with a medium size. The experimental process was focused on solving the problem of unfamiliarity in the hospital. The criteria and pretest conditions regarding the unexpected problem have been defined before the experiment. RESULTS: The results are presented in terms of criteria, pretest conditions, posttest conditions in the hospitals. The posttest conditions show the experimental results and impact of the system on users such as hospital nurses/staff and patients. For example, the questions from patients were reduced by 83.3% after using EasyHos system while nurses/hospital staff had 5 min more to do their routine work each day. In addition, another impact is that hospitals can create new information values from existing data, which now can be visible and valuable to patients. CONCLUSIONS: Hospitals' unexpected problems have been reduced by the EasyHos system. The EasyHos system has been developed with self-service and patient-centered concepts to assist patients with necessary information. The system makes interaction easier for nurses/hospital staff members and patients working or waiting in the hospital. The nurses/hospital staff members would have more time to do their routine works. Hospitals can easily set up the EasyHos system, which will have a low or nearly zero implementation cost.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».