Virtual Endocrinology Care Emphasizing Data-Driven Insights and Continuous Engagement and Its Impact on Glycemic Outcomes in Patients With Uncontrolled Diabetes: A Real-world Retrospective Case Series
Bibliographic record
Abstract
Background Steady Health’s novel virtual care model incorporates continuous glucose monitoring (CGM) and a multidisciplinary approach to timely person-centered diabetes care. Objective This real-world retrospective case series explores the early glycemic outcomes of its patients with uncontrolled diabetes. Methods All patients of Steady Health who had an initial time in range (TIR) below 70% from their first 4 weeks of available CGM data and who had completed onboarding by February 2021 were included in this analysis. We compared the change in TIR, time below range, and average blood glucose from their first 4 weeks with their latest 4 weeks of available CGM data. Hemoglobin A1c (HbA1c) values at baseline and at the end of the study were also compared. Patients completed a questionnaire assessing their satisfaction with Steady Health’s intervention. Results A total of 53 patients (n=35, 66% with type 1 diabetes; n=44, 83% treated with insulin) were included in this analysis. This cohort had a median baseline TIR of 53.0% (IQR 40.9%, 61.7%) and saw a median change in TIR of +16.6% (IQR +6.0%, +27.9%; P<.001) over a median duration of care of 11 months, amounting to nearly 4 more hours spent between 70 to 180 mg/dL a day. Of the 27 patients who had both baseline and follow-up HbA1c results, their median baseline HbA1c was 8.6% (IQR 7.5%, 11.4%; 70 mmol/mol), while their median change in HbA1c was –1.2% (IQR –2.6%, –0.2%; P=.001). Importantly, these glycemic improvements were achieved with a median decrease in the time below range by –0.3% (IQR –1.1%, 0.0%; P<.001), regardless of whether patients were started on an automated insulin delivery system. A total of 40 (75.5%) patients improved TIR by ≥5%, and 27 (50.9%) achieved TIR≥70% by the end of the study. Glycemic improvements were greatest among patients with the lowest baseline TIR and those who collaborated most intensively with Steady Health’s clinicians. A total of 25 of these patients responded to a questionnaire assessing levels of satisfaction with their care, and all of them agreed that Steady Health had a positive impact on their diabetes management. Conclusions Our findings suggest that patients with uncontrolled diabetes can achieve significant glycemic improvements by working with a virtual multidisciplinary care team that uses CGM to provide continuous clinical feedback and support.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".