Efficiency of hospitals in COVID-19 era: a case study of an affected country
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has affected all aspects of human life and society and has damaged the global economy. Health systems and hospitals were not exempted from this situation. The performance of hospitals during the COVID-19 pandemic was affected by policies related to the pandemic and other factors. This study aimed to investigate hospital performance indicators such as admissions and revenue. METHODS: The medical records of patients with selected orthopedic and general surgical diseases were studied in two government hospitals in the capital city of Urmia in the second quarter of 2019, with the same period in 2020. Data were extracted based on the number of medical records, including length of stay, hospitalization type, sex, age, insurance, number of deaths, and readmissions from the medical records department. Payment amounts were collected from the revenue department and Hospital Information System. Two performance indicators, two result indicators, and two control indicators were used. Mean disease-specific revenue, total revenue, length of stay, and bed occupancy rate were calculated for both periods. Data were analyzed using SPSS (version 16) and the Mann-Whitney statistical test. RESULTS: 2140 cases were studied in the two disease groups. An increase was observed in the number of hospitalizations and average length of stay during the pandemic. The mean disease-specific revenue in the quarter of 2020 was higher than in 2019. However, total revenue decreased, and the difference in the mean of total revenue was significant for the two years (P = 0.00) in teaching center. The number of readmissions remained unchanged throughout in the pandemic. The number of deaths due to general surgery diseases in 2020 compared to the same period in 2019 was associated with a relative increase. CONCLUSIONS: The COVID-19 pandemic increased the slope of health care costs. The analysis of the studied variables as performance, result, and control indicators showed that hospitalization rate, bed occupancy rate, and total revenue followed a similar and decreasing pattern in the selected hospitals during the COVID-19 pandemic. Hospitals should adopt appropriate strategies so that, in conditions identical to the COVID-19 pandemic, their performance is accompanied by proper management of resources, efficiency, and minimal reduction in revenue.
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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 | 0.001 |
| 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 | 0.000 |
| 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".