Zhongshan Hospital Affiliated to Fudan University: Where Smart Healthcare Meets the Future
Bibliographic record
Abstract
复旦大学附属中山医院是中国最好的医院之一，综合实力常年在国内各类医院榜单名列前茅。中山医院从1992年起就开始借助信息系统管理日常工作，近年来在席卷中国医院的数字化转型浪潮中力争前沿，加大投入、建立组织机制，围绕“患者管理人性化”“资源管理功能化”“业务管理智能化”三大主题，探索打造智慧医院样板，得到医疗卫生监管部门、患者和社会的广泛认可。 2022年2月，上海市赋予中山医院探索打造未来医院的重任，从方案发布到完成场景建设，只有4个半月时间。这构成了本案例的主要决策场景。中山医院需要在整理既有成果的基础上，尽快解决三个问题： 第一，如何定义自己的未来医院？该定义还应具备一定的普适性，以利于将来的推广。 第二，中山医院目前的工作主线是“高质量发展”，如何将未来医院建设目标与“高质量发展”目标整合起来，相互赋能、一体推动？ 第三，如何借由智慧医院建设，使数字化转型上升到文化层面，即数字化改变人的认知和行为方式的高度？
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".