Evaluation of the Use of a Novel Intelligent Diagnosis and Cost Control System on Pediatric Bronchopneumonia Outcomes: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Health care systems face challenges of inconsistent quality, inefficiency, and rising costs. Fragmented applications of clinical decision support systems (CDSSs), clinical pathways (CPs), and diagnosis-related group (DRG) payment systems have limited their synergistic potential. Objective: This study proposed a CDSS-CP-DRG closed-loop model enabled by digital health technologies; specifically, the CDSS optimized CP execution through real-time data, the CP standardized workflows to support DRG cost control, and DRG payment pressures drove iterative improvements in both technology and processes. This research aimed to validate the model's effectiveness in clinical efficacy, cost control, and standardized diagnosis and treatment of bronchopneumonia in children and provide evidence for value-based health care transformation. Methods: A total of 4543 children with bronchopneumonia were selected and divided into the experimental or control group based on whether the intelligent diagnosis and cost control system was used in the diagnostic process. Chi-square test, 1-way analysis of variance, paired t test, multiple regression analysis, and other mathematical statistical methods were used to verify the difference between the outcomes of the two groups of patients. Results: This study demonstrated comparably high cure rates in both groups (P>.05). However, the experimental group exhibited a 0.4-day reduction in average length of stay, 12.3% lower total hospitalization costs, RMB 135.3 (US $19) higher medical insurance reimbursement surplus, and a reduction of 0.16 defined daily doses of antibiotic use intensity versus the control group (P<.05 for all significant differences). Conclusions: The novel intelligent diagnosis and cost control system demonstrated significant improvement in clinical effect, cost control, and standardized treatment for pediatric bronchopneumonia, but the CP for pediatric pneumonia requiring intensive care still needs further attention and adjustment.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".