Improving systems through the use of improvement and safety science: A case study in pain pump and patient transfer processes
Bibliographic record
Abstract
Introduction : Using a pain pump or patient controlled analgesia (PCA) has been shown to be an effective method of pain control. The goal for the bedside nurse is to maintain patient safety while allowing patient autonomy for pain control. Healthcare organizations define established processes, protocols and standards that are relevant to the safe performance of actionable tasks. This case study describes an opportunity for improvement in process variation associated with the use of pain pumps/PCAs and patient transport. Methods : In this case study, a perceived process variation was identified in the transport of hospitalized inpatients to different locations with pain pumps. A systematic approach to mitigating process variation was implemented using an inter-professional team approach including the use of a team charter, defined methodological framework incorporating the Model for Improvement, Plan-Do-Study-Act cycle, microsystem analysis, indirect event review, and evidence appraisal. Results : Process variation in the management of patients with a pain pump being transported throughout the healthcare organization was the result of system gaps in communicating and managing organizational policies and procedures. The results of the analysis of the pain pump transfer project highlighted the following: system-based improvement, leadership accountability, and education-practice partnerships. Conclusions : Managing the transport of patients with a pain pump within a healthcare organization is a process that should be standardized, communicated and well understood by all providers. From a broader perspective, this case study highlighted an organizational need to develop a standardized process for learning from process variation, but also for developing tools and competencies within the organization for this work.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".