Cost-effectiveness of remote patient monitoring for First Nations peoples living with diabetes in regional Australia
Bibliographic record
Abstract
The aim of this study was to determine the cost-effectiveness of remote patient monitoring (RPM) with First Nations peoples living with diabetes. This study was set at the Goondir Health Service (GHS), an Aboriginal and Torres Strait Islander Community-Controlled Health in South-West Queensland. Electronic medical records and RPM data were provided by the GHS. Clinical effectiveness was determined by comparing mean HbA1c before and after enrolment in the RPM service. Our analysis found no statistically significant effect between the mean HbA1c before and after enrolment, so this analysis focused on net-benefit and return on investment for costs from the perspective of the GHS. The 6-month RPM service for 84 clients cost AUD $67,841 to cover RPM equipment, ongoing technology costs, and a dedicated Virtual Care Manager, equating to $808 per client. There were 199 additional client-clinician interactions in the period after enrolment resulting in an additional $4797 revenue for the GHS. Therefore, the program cost the GHS $63,044 to deliver, representing a return on investment of around 7 cents for every dollar they spent. Whilst the diabetes RPM service was equally effective as usual care and resulted in increased interactions with clients, the cost for the service was substantially more than the additional revenue generated from increased interactions. This evidence highlights the need for alternative funding models for RPM services and demonstrates the need to focus future research on long-term clinical effects and the extra-clinical benefits resulting from services of this type.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".