Implementation of National Health Informatization in China: Survey About the Status Quo
Bibliographic record
Abstract
BACKGROUND: The National Health and Family Planning Commission (NHFPC) in China organized a nationwide survey to investigate the informatization in hospitals and regional Health and Family Planning Commissions (HFPCs) in 2017. The survey obtained valid results from 79.69% (2021/2536) of major hospitals and 81% (26/32) of provincial and 73.1% (307/420) of municipal HFPCs. The investigated topics covered hardware infrastructure, information resources, applications, systems, and organizations in health informatics. OBJECTIVE: This study aimed to provide evidence collected from the survey regarding China's health informatization and assist policy making regarding health informatics in the 13th Five-Year Plan of China. METHODS: Based on the survey, the paper presented the status quo of China's health informatization and analyzed the progress and potential problems in terms of the country's health information development policies. RESULTS: Related policies have helped to construct 4-level information platforms and start converging the regional data to the 3 centralized databases. The principle of informatics has been transiting from finance-centered to people-centered. Alternatively, the quality, usability, and interoperability of the data still need to be improved. CONCLUSIONS: The nationwide survey shows that China's health informatization is rapidly developing. Current information platforms and databases technically support data exchanging between all provinces and cities. As China is continuing to improve the infrastructure, more advanced applications are being developed upon it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".