Approach to Safety Improvement: Focusing on Better Care (Fall Prevention in Medical Surgical/Intermediate Care Unit)
Bibliographic record
Abstract
Patient safety is one of the major concern of any healthcare provider during their patient’s hospital stay. This project addressed the steady trend of fall incidences compared to last fiscal years’ data of an average of 4 falls per month. This trend created urgency to envisioned a plan for solutions to prevent this circumstance from happening. This Clinical Nurse Leader led a project in creating a process in identifying all patients that are high risk to fall (HRTF) prior to their admittance or transfer to Medical Surgical/Intermediate Care Unit and throughout their hospital stay until they are discharged. In addition, a fall prevention action plan was generated for all staff members to follow to ensure the safety of our patients. Kotter’s Eight-Step Process for leading change was utilized for this project. Several literature reviews revealed that incorporating patient-centered hourly rounding, discussion of HRTF patient during huddle time along with utilization of fall prevention methods were evident practices that should be implemented by all staff members to decrease falls within the microsystem. Since the implementation of fall prevention action plans, the microsystem remained on track in achieving its goal by decreasing fall episodes by 25% by the end of the 1st quarter (September 2016) compared from the previous quarters (July 2015 to June 2016). Fall Prevention Survey was conducted to evaluate the understanding and how the staff members were engaged in preventing falls. Eighty-five percent of staff members participated in the survey which had a positive perspective of the process in preventing falls. Through multiple cycles of PDSA, changes will be implemented accordingly in decreasing falls in the unit which led to improving patient care and efficiency and ultimately improved patient outcomes.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".