Economic Evaluation of Remote Patient Monitoring System in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Abstract Introduction: Nowadays, an alternative model for evolution of health care is required to reduce the chronic illness burden notably diabetes; to this end, using the remote patient monitoring system is recommended. This system virtually eliminates distance barriers and constantly monitors the patients' information on urban and rural areas. Moreover, in case of trouble, patients are immediately supported and quick warnings are sent to the health care provider and the patient, if necessary. This study aimed to investigate the economic evaluation of the remote type 2 diabetes monitoring for controlling the blood glucose (glycosylated hemoglobin) compared to routine type 2 diabetes care.Methods: Economic evaluation was carried out using the finished cost of the remote type 2 diabetes monitoring technology and the routine treatment, incremental cost-effectiveness ratio as well as one-way and multiple sensitivity analysis using the key variables such as population, cost items, the minimum, maximum and average population size. In this study, the remote type 2 diabetes monitoring technology was compared with the routine treatment.Results: The results showed that, considering the incremental cost-effectiveness ratio in the base model, the remote type 2 diabetes monitoring system in comparison with routine treatment of type 2 diabetes was placed in the second quarter (more effective and affordable technology) of the graph as the most dominant alternative. The results of the two-way sensitivity analysis revealed that the research findings are consistent in terms of cost and population variables and in all cases were included in the second quarter (more effective and affordable technology) in the incremental cost-effectiveness ratio graph and were dominant compared to the routine treatment.Conclusion: Remote patient monitoring is a dominant alternative compared to routine treatment. Further evidence on long-term remote patient monitoring experience is needed for future studies. Results indicated that remote type 2 diabetes monitoring interventions play an effective role in reducing HbA1c that may be considered the rationale for policy makers on domestication of this technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".