A Model-Based Cost-Minimization Analysis as a Decision Tool in Obstetric Care in Helgeland, Northern Norway
Bibliographic record
Abstract
BACKGROUND: Quality of care is of utmost importance in maternity care. Today, we base the choice of institution on risk factors. Recently, a Norwegian national plan introduced new guidelines concerning quality and staffing. Consequently, the hospital trusts had to increase the number of obstetricians and midwives and handle raised costs. One way to meet such challenges is to reduce the number of delivery units.OBJECTIVES: We aimed to clarify the costs and benefits of two alternative strategies in obstetric care in Helgeland hospital trust using a model-based cost-minimization analysis (CMA).METHODS: The consequences, in terms of cost/savings and mothers´ time of travelling, by closing two midwife-administered maternity units (MAMUs) and keeping the two departments of obstetrics (DOGs) running was analyzed. We implemented data from the Helgeland hospital trust and the Medical Birth Registry of Norway (MBRN) and the selected period was 2010-2012. The comparator was today’s organization. Costs were converted into Euros at the rate of € 1 = NOK 9.527.RESULTS: The model concluded the closing of two MAMUs created an annual net saving of € 584,346. The mothers´ mean time of travelling increased by 11 minutes and by 91 minutes for those directly affected by the closure. The organizational changes were concluded safe and of low risk with regard to quality of care. A sensitivity analysis revealed the number of midwives dismissed being the most important variable. CONCLUSION: A model-based CMA may be a supportive tool when evaluating maternity care.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".