ICES Reports: Nursing Skill Mix and Experience Reduce Patient Mortality
Bibliographic record
Abstract
Nurse and other healthcare researchers have not, for the most part, focused their efforts on investigating the effects that nursing care has on commonly recognized quality of care outcomes, such as mortality and readmission for hospitalized patients -the rationale being that the determinants of these outcomes are generally believed to be the patients' own characteristics and the medical process of care.However, there is mounting evidence that nursing care affects these patient outcomes and that these effects can be quantified (Aiken, Smith and Lake 1994;Hunt and Hagen 1998;Tourangeau et al. 2002).The rationale for studying the relationship between nursing care structures and processes, and patient mortality and readmission rates, starts with the knowledge that there is wide variation among hospitals on risk and case-mix adjusted mortality and readmission rates.For example, in a sample of 75 Ontario teaching and community hospitals, the 30-day risk adjusted and weighted mortality rates for a homogeneous group of medical patients ranged from 10.5 to 21.5% (Tourangeau 2001).Several years earlier during 1993-94, in an ICES study of the Patterns of Healthcare in Ontario, the unplanned 30-day readmission rates for acute myocardial infarction in teaching and medium sized hospitals ranged from 6.7 to 24.5%.For this same group of Ontario acute care hospitals, the unplanned 30-day readmission rates for patients undergoing laparoscopic cholecystectomy surgery ranged from 0.7 to 8.8% (Goel et al. 1996).One must ask why is there such a variation in mortality and readmission rates across hospitals?While it is important to acknowledge that death and readmission to hospital are unpreventable outcomes for some patients, the persistent wide variation in risk-adjusted rates across hospitals suggests that some portion of these outcomes is in excess and is preventable.A necessary first step in preventing unnecessary patient deaths and readmissions is determining the characteristics of hospitals with lower risk-adjusted 30-day mortality and readmission rates, so that these can be more broadly adopted.Since nurses provide most of the ongoing care for hospitalized patients, it is reasonable to propose that the nursing care structures and processes are related to both the 30-day mortality and readmission rates for their hospitalized patients.If we find evidence to support relationships between these outcomes and the nursing care structures and processes for specific patient subpopulations, we can appropriately modify the related nursing care to decrease mortality and readmission to hospital.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".