Wireless Smart Infusion Pumps: A Proposed Continuous Quality Improvement Data Analysis Process
Bibliographic record
Abstract
Purpose:Errors associated with the use of infusion pumps can cause serious harm or death in hospitalized patients and increased costs to the health care system. Our study reviewed the continuous quality improvement (CQI) data collected from a wireless smart infusion pump device implemented at one of the largest Canadian teaching hospitals in a one year period. Methods:We reviewed the CQI summary data usage between April 27, 2014 and April 25, 2015 to assess Dose Error Reduction Software (DERS) compliance with the infusion pump’s master drug library (MDL) and the frequency of drug alerts. Also, we proposed a CQI data analysis process to audit the DERS compliance and frequency of pump alerts.Results:The CQI data indicated that DERS compliance with the infusion pump ranged from 72.14% to 100% depending on pre-defined clinical care areas (CCAs). The birthing unit, oncology, and critical care areas had the largest proportions of pumps alerts compared with the other CCAs. A CQI data analysis process was designed to monitor the performance of the wireless infusion pump system. Conclusions:The study findings provided information on patterns of use and risk reduction opportunities to inform the hospital’s goal to enhance the delivery of quality care and patient safety. We presented a CQI data analysis process to monitor the performance of the wireless infusion pump system, and a plan to evaluate the effectiveness, acceptability and safe implementation of the process at the 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 | 0.095 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".