Shaping Workflows in Digital and Remote Diabetes Care During the COVID-19 Pandemic via Service Design: Prospective, Longitudinal, Open-label Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic poses new challenges to health care providers and the delivery of continuous care. Although many diabetes technologies, such as insulin pumps and continuous glucose monitors, have been established, the data from these devices are rarely assessed. Furthermore, telemedicine has not been sufficiently integrated into clinical workflows. OBJECTIVE: We sought to remotely support children with type 1 diabetes and their caregivers, enhance the clinical outcomes and quality of life of children with diabetes, increase multiple stakeholders' engagement with digital care via a participatory approach, evaluate the feasibility of using an interoperable open-source platform in a university hospital setting, and analyze the success factors and barriers of transitioning from conventional care to digital care. METHODS: Service design methods were used to adapt clinical workflows. Remote consultations were performed on a monthly and on-demand basis. Diabetes device data were uploaded from patients' homes to an open-source platform. Clinical and patient-reported outcomes were assessed before, during, and after the COVID-19 lockdown period in Germany. RESULTS: A total of 28 children with type 1 diabetes and their caregivers enrolled in this study and completed 6 months of remote visits. Of these 28 participants, 16 (57%) also opted to attend at least one of their regular visits remotely. After 3 months of remote visits, participants' time in range (P=.001) and time in hyperglycemia (P=.004) significantly improved, and their time in hypoglycemia did not increase. These improvements were maintained during the COVID-19 lockdown period (ie, between months 3 and 6 of this study). Participants' psychosocial health improved after 6 months. CONCLUSIONS: Remote consultations and commonly shared data access can improve the clinical outcomes and quality of life of children with type 1 diabetes, even during challenging circumstances. A service design approach helped with the delivery of comprehensive and holistic solutions that accounted for the needs of multiple stakeholders. Our findings can inform the future integration of digital tools into clinical care during and beyond the pandemic. TRIAL REGISTRATION: German Clinical Trials Register DRKS00016170; https://tinyurl.com/skz4wdk5.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".