Facility versus unit level reporting of quality indicators in nursing homes when performance monitoring is the goal
Bibliographic record
Abstract
OBJECTIVES: To demonstrate the benefit of defining operational management units in nursing homes and computing quality indicators on these units as well as on the whole facility. DESIGN: Calculation of adjusted Resident Assessment Instrument - Minimum Data Set 2.0 (RAI-MDS 2.0) quality indicators for: PRU05 (prevalence of residents with a stage 2-4 pressure ulcer), PAI0X (prevalence of residents with pain) and DRG01 (prevalence of residents receiving an antipsychotic with no diagnosis of psychosis), for quarterly assessments between 2007 and 2011 at unit and facility levels. Comparisons of these risk-adjusted quality indicators using statistical process control (control charts). SETTING: A representative sample of 30 urban nursing homes in the three Canadian Prairie Provinces. MEASUREMENTS: Explicit decision rules were developed and tested to determine whether the control charts demonstrated improving, worsening, unchanging or unclassifiable trends over the time period. Unit and facility performance were compared. RESULTS: In 48.9% of the units studied, unit control chart performance indicated different changes in quality over the reporting period than did the facility chart. Examples are provided to illustrate that these differences lead to quite different quality interventions. CONCLUSIONS: Our results demonstrate the necessity of considering facility-level and unit-level measurement when calculating quality indicators derived from the RAI-MDS 2.0 data, and quite probably from any RAI measures.
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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 | 0.048 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".