Mobile Health Projects in a High-Complexity Reference Hospital: Case Study
Notice bibliographique
Résumé
BACKGROUND: The widespread adoption of mobile and wearable devices and apps makes it essential to assess their possible impact on the management of health and diseases. Health care providers (HCPs) find themselves faced with a new situation in their setting with the proliferation of mobile health (mHealth) intervention tests. Few studies have addressed the development of mHealth and the methodologies to manage these apps in a tertiary hospital. OBJECTIVE: The aim of this study was to evaluate the mHealth projects implemented in the Hospital Clínic of Barcelona to increase awareness of the context in which they are used and to develop policies for the development of good practice in mHealth innovation. METHODS: A prospective, descriptive cross-sectional study was conducted in a highly specialized university hospital with 850 beds for adults and a reference population of 520,000 inhabitants. A specific questionnaire was developed based on the Mobile Health 5 Dimensions European (MOHE 5D-EU) theoretical model to find mHealth projects. Apps, telemedicine, and wearable devices were included in the systematic search. For that purpose, a vertical (top-down) email-based snowball process was conducted. Data were collected from February to December 2018 by conducting personal interviews with HCPs using a structured questionnaire. RESULTS: During the study period, 45 interviews were conducted; 35 mHealth initiatives were found, with 25 targeted to patients and 10 to health professionals. Most mHealth initiatives (34/35, 97%) were related to the software field (apps and telemedicine initiatives), and one was related to wearable devices. Among the projects, 68% (24/35) were classified as medical devices or developments at the edge (developments susceptible to limitations depending on the intended use). In relation to data protection, 27 initiatives managing personal data (27/35, 77%) considered data protection legislation. Only 9% (3/35) of the initiatives had foreseen the use of interconnectivity standards. Most of the initiatives were funded by grants (14/35, 40%), sponsorships (5/35, 14%), or the hospital itself (5/35, 14%). In terms of clinical management, most projects were developed in the field of research, followed by professional tools, clinical information, and therapeutic education. Only 6 projects were involved with health care; all were led by either the industry or small and medium enterprises. CONCLUSIONS: This study helped create the design of a map of the mHealth projects conducted in our hospital that showed the stages of development of the different ongoing projects. This will allow monitoring of mHealth projects and construction of tools to reinforce areas with detected deficiencies. Our theoretical approach using a modified MOHE 5D-EU model was found to be useful for analyzing the characteristics of mHealth projects.
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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| 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 ».