Reducing sitter use in acute medicine while maintaining safety and quality
Bibliographic record
Abstract
Background and objective: Sitters are commonplace in acute care facilities throughout the country. Sitters are used to provide close observations and ensure safety for patients who are at risk for falls. These patients suffer from cognitive impairment, inability to follow instructions, and causing harm to themselves or others. The literature shows that one requirement for an effective sitter process is the use of an assessment tool. Sitter usage at the Louis Stokes Cleveland VA Medical Center (LSCVAMC) has escalated to the point that sitters are causing an overall shortage of nursing staff. This shortage causes excessive overtime, staff burnout, and reduces the quality of patient care provided to non-sitter patients. The purpose of this case-control descriptive pilot study was to reduce sitter usage on an inpatient acute medicine unit, by implementing the Patient Attendant Assessment Tool (PAAT), without reducing patient safety and quality of patient care. The PAAT was developed and utilized by a Midwest hospital for data collection and to assess the need for sitters.Methods: Patients were placed into the Pre- and Post-implementation cohorts, according to the order of their admission. Pre-interventional data was collected from the study group, using the Sitter Justification Form, the 24-hour nursing report and the electronic medical record. The intervention consisted of staff education on the use of a new tool, the PAAT. Following implementation of the tool, the data was collected and analyzed using the SPSS 20 for windows (SPSS.INC), over an 8-month period.Results and conclusions: Sitter usage was reduced without reducing patient safety or quality of care, with the implementation of the PAAT. Among individuals having at least one sitter day, patients in the post intervention group, were less likely to have as many sitter shifts (n = 343, 58.0%) as compared to patients in the Pre-interventional group (n = 451, 75.9%) (Chi Square = 42.88; df = 1, p < .001). As can be seen, there was a significant decrease in the number of sitter shifts after the implementation of the PAAT, as compared to the pre-interventional group. There was a slight increase in the quality of patient 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".