US Food and Drug Administration Novel Healthcare Devices and Apps: A Microreview
Notice bibliographique
Résumé
FigureThe advancing array of biomedical technologies developed by medical device manufacturers include, but are not limited to, the use of microchip technology that readily adapts to computer-driven miniature motors, sensors, actuators, and transducers. In addition, these small technologies comprise parts of medical devices, including “flexible robots” and sensors that are woven into fabrics and smart dressings.1,2 It is now commonplace to couple the driver for these devices into “smart phones” or tablets. The miniaturization of computers allows for “point-of-service” application. Cell phones and mobile (handheld) devices are no longer limited to communication; they now incorporate computational devices specifically designed not only to drive but also to control healthcare applications (devices). These small devices now have the capability of using data collected from cameras, ultrasound, and near-infrared scanners. Specifically, in the wound care realm, there are opportunities to monitor wound healing in real time and even measure treatment outcomes. The Food and Drug Administration (FDA) categorizes the devices under the following framework3: Mobile platforms (handheld), commercial off-the-shelf computing platforms, with or without wireless connectivity. Examples include smart phones, tablet computers, or other portable computers. Mobile application (app) is defined as a software application that can be executed (run) on a mobile platform (ie, commercial off-the-shelf) or a web-based software application that is tailored to a mobile platform but is executed on a server. Mobile medical app: For purposes of this guidance, a “mobile medical app” meets the definition of device in section 201(h) of the Federal Food, Drug, and Cosmetic Act and is intended to be used as an accessory to a regulated medical device or to transform a mobile platform into a regulated medical device. The FDA guidance document for medical mobile apps and indications for regulatory approval was updated in February 2015.3 In general, the FDA regulates those apps that meet the definition of a medical device and that pose a risk to patient safety if they do not function as intended. Medical apps that provide medical information or content are generally not required to seek regulatory approval. However, medical apps that incorporate diagnostics or primary prevention and disease prevention recommendations are generally subject to individualized FDA enforcement discretion. If those devices measure healing and/or capture diagnostic images, they can be subject to disclosure in a medical malpractice case, especially if they cause or document a safety risk. The FDA now encourages input about these devices from the end user as well—the patient.4 The application/approval 510(k) process for mobile device and mobile medical app developers is much shorter than the application/clearance process for medical devices, with most 510(k) submissions cleared within 1 year.5 We are beginning to see more handheld wound care diagnostic devices published in our journal and other peer-reviewed media, including the following examples: FDA-approved “Scout”—an image capture device (WoundVision, LLC, Indianapolis, Indiana) to measure wound length and width and wound perimeter.6 A handheld, Portable Real-time Optical Detection Identification and Guide for Intervention (MolecuLight, Toronto, Ontario, Canada) that enables noncontact, real-time, high-resolution visualization and differentiation of key pathogenic bacteria through their endogenous autofluorescence, as well as connective tissues in wounds.7 An ultraportable near-infrared optical scanner has been developed at the Optical Imaging Laboratory that can perform noncontact 2-dimensional area imaging of the wound site.8 This research was supported by the National Institutes of Health (R15CA119253) and Florida International University Division of Research, Miami, Florida. This month’s continuing education article (page 567) gives us a systematic review and meta-analysis on the efficacy of monitoring devices in support of prevention of pressure injuries.Figure
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».