Lessons from Covid-19 and the potential benefit of the implementation of Axon’s personal electronic health records (PEHR) into aesthetic care, plastic and reconstructive surgery
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Résumé
Abstract Background Covid-19 pandemic highlighted the need for implementing Personal Electronic Health Records (PEHR) for patients’ data management. Furthermore, this pandemic underscored the relevance for integrated and interoperable Electronic Health Records (EHR) to support disease surveillance, hospital capacity planning and resource management (Peek N, Sujan M, Scott P (2020) Digital health and care in pandemic times: impact of COVID-19. BMJ Health Care Inf 27(1):e100166. https://doi.org/10.1136/bmjhci-2020-100166 ). Due to the lack of comprehensive patients’ record in plastic, reconstructive and aesthetic surgery, Axon’s myHealth app offers a break-through patient-centric design allowing patients to be in control of their records and updating them in real-time for their plastic and aesthetic care providers to have a clearer understanding of patients’ history and progress from pre-op to post-op. Methods The Axon Dublin survey took place during Covid-19 pandemic in two phases: Phase 1 aimed to assess the feasibility of patients integrating the Axon myHealth application into their clinical visits. Testing occurred in a clinical environment, where patients were encouraged to download and use the Axon system with a health practitioner (HP) present. Phase 2 focused on home testing, evaluating patients’ willingness to manage their health remotely with HP assistance. This phase included self-testing activities such as performing rapid Covid-19 antigen tests, recording medical history, and measuring blood pressure at home. Results The Axon Dublin Study aimed to assess patient engagement, clinical impact, and cost-effectiveness of the Axon myHealth application. Over 85% of patients showed interest in owning a Personal Electronic Health Record. Notably, 36% continuously monitored chronic conditions. Clinical decisions, informed by patient data, saw 61.9% compliance. Noteworthy, 23% of hypertensive participants required immediate medication changes. Patient self-capture of data reduced consultation time. Public health implications were significant, with 39% vaccinated and 31% reporting complications. High user satisfaction (97%) demonstrated the app’s effectiveness in infection control and chronic care. Conclusions Offering patients the ability to update and control their data is a growing interest, with a clear need in plastic and aesthetic surgery to have a better understanding of a patient’s medical past and progress throughout the surgical process and period. This platform, which is time and cost efficient, can only facilitate personalised care and improve outcomes while maintaining patient’s confidentiality. Level of evidence Not gradable.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découle