Predictors of Inappropriate Hospital Stay: Experience From Iran
Bibliographic record
Abstract
OBJECTIVE: Hospital services are the most expensive component of modern health care systems and inappropriate hospital stay is one of the most important challenges facing hospitals in many countries. The purpose of this study was to determine the extent of inappropriate hospital stay and investigate the related factors in Semnan city (Iran). METHODS: In this study, the Iranian version of Appropriateness Evaluation Protocol (AEP) was used in a representative sample of 300 hospital admissions and 905 hospital days. Data collection was performed during six weeks in January and February 2014 in four wards (two internal medicine and two surgical wards) of two hospitals in Semnan city (Iran). RESULTS: The results showed that 7.4% of admissions and 22.1% of stays have been inappropriate. Inappropriate stays were mainly concerned to the factors, including length of stay, inappropriate admissions, as well as factors related to hospitals. The most frequent causes of unjustifiable days were due to waiting for diagnostic or therapeutic procedures (35.1%), and 20.6% delay in discharge of patients by physicians due to conservative medical policy. CONCLUSION: In conclusion, this study confirms the existence of inappropriate hospital stays which may be due to patient characteristics and hospital factors. The most unjustifiable reasons for inappropriate hospital stay were related to internal processes of hospital, which mostly could be prevented through appropriate management Therefore, some steps must be taken to decrease inappropriate hospital stay and preserve hospital resources for patients who need them.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".