Applying the hospital administrative data quality scoring tool in 15 countries
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Hospital administrative data serves as a rich source of information for resource allocation, surveillance, and international comparisons. Differences in coding practices and guidelines lead to variations in hospital administrative data. This study outlines our team's process of creating a standardised tool, the Hospital Administrative Data Quality (HADQ) scoring tool, which will allow countries to assess their HADQ. METHOD: We previously developed 24 indicators through a Delphi consensus method. To test the applicability of these indicators, we approached 50 countries with an online survey comprised of qualitative and quantitative questions based on the 24 indicators. An overall score out of 20 for data quality was calculated for each country based on the survey responses. The score was classified into three categories: high data quality, moderate data quality and low data quality. RESULTS: Of the 50 countries invited, 17 responded. Surveys from two countries were excluded due to insufficient data. Country responses were evaluated and scored by dimension. The data quality indicators showed positive face validity and were applicable for most countries providing comparative information for development of the tool with good discrimination. Canada, United States of America, New Zealand, United Kingdom, and Spain were among the countries with an overall high data quality score. Most countries scored high in 3 out of 5 dimensions of data quality. A few counties scored 0 in "Relevance" and "Timeliness" resulting in a lower score.ConclusionThe HADQ tool developed in this study will support the assessment and comparison of HADQ by applying the same standard within and across countries.Implications for health information management practice:The HADQ tool can be used by diverse users such as the researchers, government bodies and policymakers interested in improving hospital administrative data quality following standardised indicators that can applied globally.
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 | 0.124 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| 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".