Assessment of Cost-Effectiveness on Remote Monitoring for Cardiovascular Devices in Japan
Bibliographic record
Abstract
BACKGROUND: 2018 revision of the health insurance reimbursement in Japan brought additional fee for Cardiovascular Implantable Electronic Devices (CIEDs) Management by Remote Monitoring. The adaption of CIEDs Remote Monitoring has already been recommended by the societies, but the cost-effectiveness evaluation about the system has not been enough. This research was designed, therefore, to evaluate the cost-effectiveness about CIEDs Remote Monitoring in Japan. METHODS: A systematic review was conducted along with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Certain criteria and search strategy were pre-defined to identify studies that could be included into this research. The process of the quality assessments was planned by Critical Appraisal Skills Programme (CASP). Quality Adjusted Life Years (QALYs) that were extracted from the included studies would be calculated into Incremental Cost-Effectiveness Ratio (ICER) with the reimbursement amount in Japan. RESULTS: Three studies met the systematic review criteria after the selection along with PRISMA flow diagram. The quality of the included studies was assured by CASP Checklists designed for RCT and Cohort Study. ICERs from the selected studies were provided as 569,697 JPY, 1,220,000 JPY, and 311, 111 JPY for the patient groups enrolled with Remote Monitoring system. CONCLUSION: ICERs for CIEDs Remote Monitoring were demonstrated as the cost-effective under the threshold set by Central Social Insurance Medical Council (Chuikyo). As this study put the validity of the cost-effectiveness approach in a certain field in Japan, this kind of evaluation should be performed on more areas along with the guideline by Chuikyo.
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 | 0.037 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".