Constructing episodes of inpatient care: How to define hospital transfer in hospital administrative health data?
Bibliographic record
Abstract
ABSTRACT ObjectivesHospital administrative data creates a separate record for each hospital stay of patients. Treating a hospital transfer as a readmission could lead to biased results in health service research, resource planning, and quality of patient care. This study is to identify the optimal time gaps between two hospitalizations to identify hospital transfer cases. ApproachThis is a cross-sectional study. We used the hospital discharge abstract database (DAD) in 2013 from Alberta, Canada to define transfer cases. Institution code and transfer indicators of “institution to” and “institution from” are mandatory in Canadian DAD and have high reliability. We defined transfer cases by transfer institution indicators and used it as the reference standard. Different time gaps between two hospitalizations (6, 9, 12 and 24 hours) were used to identify transfer cases. We compared the sensitivity and positive predictive value (PPV) of different transfer case definitions across different categories of sex, age, and location of residences. Readmission rate within 30 days was also compared after the episode of care were defined by combining transfer cases at the different time gaps. ResultsSensitivity increased with an increase of time gap between two hospitalizations while PPV decreased. Use of ≤ 6 hours lead to low sensitivity for patients under the age of 50 or living in the rural area; Use of ≤ 24 hours lead to low PPV for patients under the age of 50 or living in urban area. Use of ≤ 12 hours overestimated the 30 days readmission rate compared with the reference standard. The time gap of 9 hours between two hospitalizations is the optimal way to identify transfer cases with the sensitivity of 0.97 and the PPV of 0.95. ConclusionsWe recommend the use of a time gap of up to 9 hours between two hospitalizations to define hospital transfer in inpatient databases. This validated definition provides a foundation for research in health service and for outcomes such as readmission.
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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 | 0.102 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".