Implementation of Surgical Site Infection (SSI) Gap Analysis and Data Visualization Dashboards to Drive Organizational Change
Bibliographic record
Abstract
Background: Surgical site infections (SSIs) are a major healthcare quality issue; they lead to increased morbidity and mortality rates. They also prolong the length of stay and increase the cost to the patient and the healthcare system. Depending on the procedure, the risk of death is 2 to 11 times greater for patients with an SSI than for patients without an SSI. Additionally, the financial burden and patient burden is considerable; it ranks as the most common and costly of hospital-acquired infections (HAIs) and can extend a patient’s length of stay by 11.2 days. The risk of developing an SSI is affected by multiple factors at the patient, operative, and institutional levels. Methods: A Midwestern healthcare system conducted a review of the recommended best-practice guidelines that are currently accepted as the standards of care in US healthcare facilities. A gap analysis instrument for colorectal SSI prevention was drafted and reviewed for content validity and accuracy by field experts. Hospital infection preventionists worked in conjunction with operating room leaders to disseminate the survey to staff. Responses were collected from June 5 to June 30, 2019. Concurrently, the system infection preventionist team developed a standardized SSI dashboard template that could be used at the hospital, regional, and system level to visualize SSI infection counts, standardized infection ratios (SIRs) as well as procedure count data. These dashboard reports are updated and distributed on a monthly basis to each hospital’s campus executive team and other leaders. Federal- and state-required procedures were included and additional procedures were included based on hospital risk. Results: In total, 35 responses were recorded from 8 ministries across the system. Infection preventionists, operating room directors, physicians, nurses, and surgical technologists were represented among the respondents. The following areas were identified areas for improvement: use of chlorhexidine gluconate (CHG) bathing kit, mechanical bowel preparation with preoperative oral antibiotics, hair removal practices, use of fascial wound protector, maintenance of patients’ blood glucose levels, glove and gown changing procedures, and use of antimicrobial-coated sutures. The development and distribution of the SSI dashboard increased awareness and knowledge of SSIs by hospital and system-level leaders. Conclusions: The implementation of both the gap analysis and dashboard reports improved the awareness areas needed for improvement and knowledge of the burden of SSIs. These findings will drive discussions within the hospitals and at the system-level to implement evidence-based practice to improve care and decrease infections as well as guide the development of SSI patient care bundles. Funding: None Disclosures: None
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".