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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it