Analysis of Requirements for Developing an mHealth-Based Health Management Platform
Notice bibliographique
Résumé
BACKGROUND: Studies have consistently shown that mobile and Web-based apps have positive impacts on people's daily lifestyles, health management, and disease treatment. As the development of medical and health informatization in China has evolved, different kinds of mobile-based apps for individuals and hospitals have been developed by software vendors. However, doubts and challenges posed by the media have prevented these apps from having a stable and substantial user base. Analyses of user requirements have not typically been performed prior to the design of such mobile apps. The health information government authority in Liaoning Province, China, was planning to establish a mobile health (mHealth)-based health management platform, aiming to alleviate the difficulties citizens have in seeking hospital services. OBJECTIVE: The goal of this study was to determine the actual health and medical needs of citizens that may be addressed by medical information technologies. The results may contribute to the functional design and development of health management and appointed treatment-oriented mobile apps. METHODS: In this study, a semi-structured questionnaire on mHealth requirements was designed and tested, and 240 questionnaires were given to the outpatients of the First Hospital of the China Medical University in Shenyang, Liaoning Province, China; of these, 228 valid responses were collected, for a response rate of 95%. We discussed the current development of mHealth with 50 related experts and engineers from health authorities and a medical information company. SPSS 13.0 was used for statistical analyses. RESULTS: After detailed analyses of the questionnaire data, several findings were evident: first, most citizens and patients were unclear about their health conditions (64.5%, 147/228) and were interested in receiving a mobile app as a tool to manage their health and medical needs (71.1%, 162/228). Patients in different outpatient departments had different opinions regarding online registration. Conversely, the main problems for outpatients were long waiting times (66.4%, 148/223) and difficulties in making appointments (46.5%, 106/228), and they also worried about payments and Internet problems when using a mobile app for appointment reservations. Furthermore, as the main service target of mHealth is the health management of the general population, we first need to solve the associated interoperability and data security problems associated with such apps. CONCLUSIONS: This study provides insight into the health and medical requirements of smartphone apps, and draws attention to some of the challenges and opportunities of mHealth. We suggest several value-added features and characteristics that app developers should take into consideration when developing health and medical-related apps. The findings also highlight some major challenges that require further consideration and research to ensure that these apps meet the core needs of patients and aid the development of the health information system in Liaoning Province, China.
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 | 0,013 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».