Cost-effectiveness of Automated Medical Systems implementation in hospital setting: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: This systematic review summarized and synthesized the available evidence to examines the cost-effectiveness of the implementation of Automated Medical Systems. Method: PubMed, Embase, Web of Science, The Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Full-text Database (VIP), WanFang Database, China Biopharmaceutical Scientific Literature Database (CBM) were searched from inception to February 2020. The reference lists of eligible studies were hand searched. After two investigators independently screened the literature and extracted the data, the quality of the included articles was evaluated by using the Cochrane Intervention Risk of Bias Assessment Tool, the Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality Scale. Revman5.3 software was used for meta-analysis. Results: Sixteen articles (9 interventional studies, 6 cohort studies, 1 cross-sectional study) were finally included, 92,576 patients were included in analysis. Meta-analysis showed that: 1) compared with the traditional method, the incidence of adverse events (such as potential adverse drug reactions, deep vein thrombosis, etc.) was reduced after the implementation of the Automated Medical System (OR = 0.43, 95% CI = [0.20, 0.93]; P = 0.03); 2) the average medical costs incurred during the use of the Automated Medical System were lower than those of the traditional method (OR = 1.13, 95% CI = [1.02, 1.24]; P = 0.02), which was cost-effective (OR = 2.03, 95% CI = [1.34, 3.07]; P = 0.0008); 3) the quality-adjusted life years obtained by patients observed during the implementation of the Automated Medical System were significantly higher than those of the conventional medical system (OR = 1.13, 95% CI = [1.02, 1.24]; P = 0.02). Conclusion: A multicenter, large-sample randomized controlled trial is needed to comprehensively explore the cost-effectiveness of Automated Medical Systems using a unified economic evaluation model and considering all costs associated with Automated Medical Syst
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".