IV Pump Phone Alert System for Medical Surgical Nurses Improving Patient Satisfaction
Bibliographic record
Abstract
According to the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) patient satisfaction survey of the medical surgical unit in a Metro Atlanta hospital, 50%, out of a sample size of 300, reported that the area around their room was always quiet at night, which is below the national average of 70%. This project aims to increase medical-surgical patients’ satisfaction by reducing nighttime noise by implementing an IV pump phone alert system for nurses 10 minutes before the completion of an infusion. The rationale for this approach is the reduction of IV pumps beeping during the night, improving nighttime sleep, and increasing patient satisfaction. A study using the HUSH Protocol, including reducing IV pump alarms on a medical-surgical unit, showed improvement in HCAHPs' patient satisfaction results, with a 13% increase from 72% to 85% from quarter two to quarter three. This project will be implemented for six months at a Metro Atlanta Hospital 40-bed medical-surgical unit. Approval will be obtained from the unit clinical manager and quality committee. The IT team will be consulted to implement the alert call system on nurses' phones. We will create an eye-catching poster and educate staff with help from the manager. Nurses will educate patients. The HCAHPs survey questions on the quietness of the hospital environment will be used to evaluate patient satisfaction in comparison to previous results the year prior, as well as nurse interviews to obtain satisfaction and effectiveness with the alert system. The goal is to increase the percentage of patients who responded Always to the HCAHPs question, how often was the area around your room quiet at night? From the baseline of 50% to at least 20% to meet the national percentage. We will follow up after six months to reevaluate HCAHP results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".