Improving Quality and Safety in Thoracostomy Management: A Digital Chest Drainage System Intervention
Bibliographic record
Abstract
Background: Trauma care across Vancouver Island is delivered at two trauma centers which serves a population of 864,000. As part of program planning and delivery we routinely monitor Patient Safety Learning System (PSLS) reports, survey staff, and conduct case (MTPRC) reviews. Using these mechanisms, we identified the need to standardize care practices, improve staff training, and simplify the management of thoracostomy tubes. Our intervention introduced a digital chest drainage system to replace the gravity drainage system. Methods: We used a mixed-methods approach to identify practice issues and design and analyse quality improvement (QI) efforts. We analyzed open and directed staff surveys, text parsed bulk PSLS reports, and manual selected MTPRC cases. PSLS and MTPRC responses were coded thematically using conventional content analysis. Staff satisfaction with the QI work was assessed using Wilcoxon signed-ranks testing. Ongoing longitudinal assessment of the QI intervention will be used to describe the clinical impact of the QI intervention. Results Surveys identified that most nurses, 57.5% (n=40), wanted chest tube training. The least understood aspects of care were assessing air leaks, tidalling and excess negativity; and changing the collection canisters. We screened PSLS reports between 2022 and 2024 (n=4300), limited them by catchment area (n=1945), text-parsed them as chest-tube related (n=116), then manually screened them to identify 11 trauma-related chest tube events. Coding of PSLS (n=11) and MTPRC cases (n=14) identified two causal themes: i) management inconsistency (in physician ordering and clinical governance) and, ii) devices issues (chest tubes, securement, collection canisters and space). These cases resulted in delayed care (including prolonged stay) in three cases, unnecessary tube (re)placement (n=12), and clinical deterioration (n=3). Our QI initiative implemented a digital drainage system. Digital systems automatically modulate thoracic negativity, digitally display air-fluid leaks and tidalling, and provide audio-visual alarms and prompts to address pump and collection canister issues. Orientation sessions trained 76.5% of staff. Postimplementation surveys (n=18) showed 61.1% or respondents had used the digital system. Most rated the digital system as safer (z=3.67, r=0.61, p<0.01), easier (z=3.66, r=0.61, p<0.01), superior (z=3.78, r=0.63, p<0.01), and preferable (z=3.87, r=0.64, p<0.01) to gravity drainage. The response was not attributable to difference in the perceived level of training (z=1.41, p=0.16). Ongoing analysis on the clinical effect of the system will be available by the time of the conference. Implications and lessons learned We used small-scale surveys to assess staff, large-scale PSLS surveillance to identify rare safety events, and a mixed-methods approach to identify opportunities for QI. We used digital chest drainage system to address the most cited challenges in chest tube care. As a result, we have seen an increase in staff perceptions of patient safety and ease of care. Ongoing analyses will determine if this initiative is correlated with changes in patient outcomes and safety events.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".