Need for the Development of a Specific Regulatory Framework for Evaluation of Mobile Health Apps in Peru: Systematic Search on App Stores and Content Analysis
Notice bibliographique
Résumé
BACKGROUND: In Peru, there is an increase in the creation of mobile health (mHealth) apps; however, this situation could present problems related to the quality of information these apps share, data security and privacy, usability, and effectiveness, as there is no specific local regulation about their creation and use. OBJECTIVE: The objective of this study was to review mHealth apps created, uploaded, or used in Peru, and perform an analysis of the national regulatory framework that could be applied to evaluate whether there is a need to develop and implement a specific regulation to these apps. METHODS: A total of 3 reviews were performed. First, we reviewed information about Peruvian mHealth apps created up to May 2019 from scientific publications, news, government communications, and virtual stores, and evaluated their purpose, creator, and the available evidence of their usability and effectiveness. The second review was carried out by taking a sample of the 10 most commonly used mHealth apps in Peru (regardless of the country of creation), to evaluate the information they collect and classify them according to the possible risks that they could present in terms of security and privacy. In addition, we evaluated whether they refer to or endorse the information they provided. Finally, in the third review, we searched for Peruvian standards related to electronic health (eHealth) that involve information technology that can be applied to regulate these apps. RESULTS: A total of 66 apps meeting our inclusion criteria were identified; of these, 47% (n=31) belonged to government agencies and 47% (n=31) were designed for administrative purposes (private and government agencies). There was no evidence about the usability or effectiveness of any of these apps. Concerning the 10 most commonly used mHealth apps in Peru, about the half of them gathered user information that could be leaked, changed, or lost, thus posing a great harm to their users or to their related patients. In addition, 6/10 (60%) of these apps did not mention the source of the information they provided. Among the Peruvian norms, the Law on the Protection of Personal Data, Law on Medical Devices, and administrative directives on standards and criteria for health information systems have some regulations that could be applied to these apps; however, these do not fully cover all aspects concerning the evaluation of security and privacy of data, quality of provided information, and evidence of an app's usability and effectiveness. CONCLUSIONS: Because many Peruvian mHealth apps have issues related to security and privacy of data, quality of information provided, and lack of available evidence of their usability and effectiveness, there is an urgent need to develop a regulatory framework based on existing medical device and health information system norms in order to promote the evaluation and regulation of all the aforesaid aspects, including the creation of a national repository for these apps that describes all these characteristics.
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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 | 0,115 | 0,301 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,007 |
| Bibliométrie | 0,025 | 0,017 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».