Strategies for Improving the Data Quality in National Hospital Discharge Data System: a Delphi Study
Bibliographic record
Abstract
INTRODUCTION: National hospital discharge data system can play a critical role in community health assessment, disease surveillance, strategic planning, policymaking, service quality control, and research. Moreover, the quality of hospital discharge data affects the usefulness of the data and is one of the prerequisites for effective utilization of the data. Thus, the present study aimed to identify the necessary actions for improving the data quality in the national hospital discharge data system and present a model for Iran based on the experiences of England, Canada, and New Zealand. METHODS: In doing so, the measures performed in these countries were investigated. The related data were organized in six categories of standards and procedures, training and coordination with the users, assurance from the capability of the system's software, data modification, data quality control, and documentation and reporting the data quality. According to the gathered data, the primary model was designed. Then, the model was assessed using a two-round Delphi technique by 33 and 31 experts, respectively. CONCLUSION: According to the findings, a model was presented in order to improve the data quality of Iran's national hospital discharge data system.
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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 | 0.099 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".