The cost‐effectiveness of substituting physicians with diabetes nurse specialists: a randomized controlled trial with 2‐year follow‐up
Bibliographic record
Abstract
AIMS: To evaluate the cost-effectiveness of an intervention substituting physicians with nurse specialists. BACKGROUND: Increasing populations of people with diabetes in most Western countries require creative solutions that give high-quality chronic care while controlling costs. Instigating nurse specialists as a substitute for physicians yields positive results in this area. Research about such interventions in a hospital-based setting is limited. METHODS: This paper is a report of a study of a randomized, non-blinded clinical trial including people with diabetes mellitus types 1 and 2. In the intervention group nurse specialists were the central carers, providing care that conformed to a preset protocol. Patients were included between 2004 and 2007. Costs, quality of life and adverse events were measured, cost-effect ratios and incremental cost-effect ratios were calculated based on health-resource utilization rates, corresponding market prices and national tariffs from 2007. RESULTS: Health related quality of life scores did not differ significantly between the control and the intervention group. In the intervention group, fewer patients were hospitalized and fewer side effects from drugs were reported compared to controls. Nurse specialists as central care givers generated a modest reduction in costs per quality adjusted life year gained compared to usual care. CONCLUSION: Nurse specialists give diabetes care that is similar to care provided by physicians in terms of quality of life and economic value. Instigating a nurse specialist as central carer yields opportunities to generate cost savings. Developing interventions which also focus on prevention of complications is recommended when aiming for long-term organisational cost savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".