Effectiveness for introducing nurse practitioners in six long-term care facilities in Québec, Canada: A cost-savings analysis
Bibliographic record
Abstract
BACKGROUND: Internationally, most studies have focused on quality and safety in long-term care. However, studies focusing on the economic evaluation of quality and security in long-term care are sparse. Moreover, the economic evaluation of nurse practitioner care in long-term care is lacking, particularly in Québec Canada where roles are new. PURPOSE: To evaluate the effectiveness of introducing nurse practitioners in six long-term care facilities in Québec using a cost-savings analysis in terms of reduction of nurse practitioner sensitive events (NPSEs). METHODS: A cost savings analysis was completed using a prospective observational study. All residents (n = 538) under the care of teams that included nurse practitioners who experienced at least one of the following NPSEs: falls, pressure ulcers, short-term transfers, and a change in the time needed to administer the medications consumed were included. Data were collected from September 1st 2015 to August 31st 2016. Descriptive statistics identified numbers of cases for falls, pressure ulcers, short-term transfers, and the number of medications consumed. A literature analysis was used to estimate excess median long-term care facility related costs of these NPSEs. Costs were calculated in 2016 Canadian dollars. The cost savings with the reductions that occurred for falls, pressure ulcers, short term transfers, and the time needed to administer medications after the implementation of a primary healthcare nurse practitioner role in the six long term care facilities were also estimated. FINDINGS: The median cost of 341 cases of falls, 32 cases of pressure ulcers and 53 cases of short-term transfers in the six long-term facilities would range between CAD 4,516,337.8 and CAD 5,281,824.4. Moreover, the total costs savings from the reduction of adverse events including the reduction of nursing administration time for medications would be between CAD 1,942,533.6 and CAD 3,254,403.4. DISCUSSION: This is the first study to present the financial consequence of adverse events sensitive to nurse practitioner care in long-term care. Important cost savings were generated from the reduction of adverse events after the implementation of nurse practitioner roles in long-term care. Government should consider these results for prevention and improvements in quality and safety in long-term 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".