Digital Innovation and Integrated Care in People With Diabetes in Western Sydney: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic catalyzed the adoption of digital technologies in health care. This study assesses a digital-first integrated care model for type 2 diabetes management in Western Sydney, using continuous glucose monitoring (CGM) and virtual Diabetes Case Conferences (DCC) involving the patient, general practitioner (GP), diabetes specialist, and diabetes educator at the same time. OBJECTIVE: This study aims to assess the effectiveness of the innovative diabetes clinics in Western Sydney. METHODS: In 2020, a total of 833 new patients with type 2 diabetes were seen at Western Sydney Diabetes (WSD) clinics. An early cohort of 103 patients was evaluated before and after participation in virtual DCC, incorporating CGM data analysis, digital educational resources, and remote consultations with a diabetes multidisciplinary team. Assessments were conducted at baseline and 3-4 months post DCC. RESULTS: ) levels decreased notably from 9.6% to 8.2% (average reduction of 1.4%; 95% CI 1.03-1.82; P<.001). Time in range (TIR) as measured by CGM increased substantially from 46% to 73% (95% CI 20-32; P<.001), and the glucose management indicator (GMI) improved from 7.9% to 7% (average reduction of 0.9%; 95% CI 0.55-1.2; P<.001). Despite no significant change in the total daily insulin dose, the proportion of patients on insulin therapy rose from 27% to 39% (P<.001), indicating more targeted and effective diabetes management. CONCLUSIONS: Our findings demonstrate the effectiveness of a digitally enabled integrated care model in managing type 2 diabetes. The use of CGM technology, complemented by virtual DCCs and digital educational tools, not only facilitated better disease management and patient engagement but also empowered primary care providers with advanced management capabilities. This digital approach addresses traditional barriers in diabetes care, highlighting the potential for scalable, technology-driven solutions in chronic disease management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".