University hospitals in Turkey: Structural crisis in financing or consequence of mismanagement?
Bibliographic record
Abstract
University hospitals in Turkey have a prominent role especially in treatment of complex cases and research. However, despite their indispensible place in the health care system, their financial status has long been on the agendas of the Turkish health policy-makers and is seen as a major threat to the financial sustainability of the whole system. It has been reported that the total debt of university hospitals has reached to 4.5 billion TRY (1.1 billion €) in 2016. This debt is to the third parties that provide medical devices, pharmaceuticals or services to these hospitals. There is also an increasing trend in university hospital debts calling for an urgent attention from the government. This article aims at exploring the financial status of university hospitals and showing that with a new management approach focusing on efficiency and effectiveness measures, the problem could be overcome. The example from Hacettepe University Hospital showed that a problem solving management approach and a reformist vision between December 2011-January 2016 has resulted in major improvements in the financial status of the hospital. The debts of the hospital were stabilized in this period by policies focusing on increasing number of patients and procedures, by decreasing the cost of purchasing goods and materials and by following the Social Security Institution’s (SSI) payment procedures. Starting from January 2016 a new management took the office and abandoned the measures taken by the previous administration. This led to an increase in hospital debts again. The article concluded that despite a volatile reimbursement environment, good management practices could help university hospitals to sustain their financial status.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".