Does information disclosure among public hospitals stimulate medical cost change efforts? A pilot study in Shanghai
Bibliographic record
Abstract
BACKGROUND: In 2013, the Shanghai Hospital Development Center issued a policy to advocate public hospitals to report their information about costs on diseases. The objective was to evaluate the impact of interhospital disclosure of costs on diseases on medical costs and compare costs per case following information disclosure between hospitals of different rankings. METHODS: The study uses the hospital-level performance report issued by Shanghai Hospital Development Center in the fourth quarter of 2013, which covers quarterly aggregated hospital-level discharge data from 14 tertiary public hospitals participating in thyroid malignant tumors and colorectal malignant tumors information disclosure from the first quarter of 2012 to the third quarter of 2020. An interrupted time series model with segmented regression analysis is employed to examine changes in quarterly trends with respect to costs per case and length of stay before and after information disclosure. We identified high- and low-cost hospitals by ranking them on a costs per case basis per disease group. RESULTS: This research identified significant differences in cost changes for thyroid malignant tumors and colorectal malignant tumors between hospitals after disclosing information. A hospital's discharge costs per case for thyroid malignant tumors increased significantly among top-cost hospitals (1629.251 RMB, P = 0.019), while decreased for thyroid and colorectal malignant tumors among low-cost hospitals (-1504.189 RMB, P = 0.003; -6511.650 RMB, P = 0.024, respectively). CONCLUSION: Our findings indicate that information disclosure of costs on diseases results in changes in discharge costs per case. And low-cost hospitals continued to maintain their leading edge, whereas the high-cost hospitals changed their position in the industry by reducing discharge costs per case after information disclosure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".