Abstract QS27: Utilizing Statistical Process Control to Study the Progression of Institutional Situational Awareness Through Anonymous Incident Reporting
Bibliographic record
Abstract
PURPOSE: The impetus for widespread focus on patient safety reached its apex in the early 1990s when the Institute of Medicine sent shockwaves with their report, To Err is Human. They estimated nearly 100,000 deaths occurred from preventable medical errors every year. Bagian and colleagues took the lessons learned from industries such as aviation and introduced them to healthcare. As a result, anonymous incident reporting (AIR) was implemented in Healthcare to foster a culture of safety. Pioneers like Sutcliffe and Singh took this a step further by studying factors like situational awareness (SA) and its role in creating High Reliability Organizations (HRO). The purpose of this project is to determine if the application of statistical process control (SPC) can be applied to anonymous incident reporting to study institutional situational awareness. METHODS: Our institution’s AIR protocol begins with any employee filing an anonymous online safety report. This report is assessed and directed to the appropriate manager by patient safety officers. An action aimed at systemic safety improvement is undertaken, and the feedback is shared at workgroup meetings. The Veterans Administration National Center for Patient Safety (NCPS) maintains a database of all AIR reports. All AIR reports from our institution from December 2012 to October 2016 were collected and trended. Critical events were tracked utilizing VASQIP’s Critical Incident Tracking Notification System (CITNs). VASQIP defines CITNs as: death in operating room (OR), death from hemorrhage within 24hours, incorrect surgery, retained surgical item, OR fire, and OR burn. Data was evaluated by month and by quarter for percent change and compared to observed critical events (CITNs). Events were trended as a statistical process control (SPC) chart and a logarithmic regression was performed for progression of AIRs per month. RESULTS: There was an exponential increase in total AIRs (1st mo-1, 6th mo-6, 12th mo-706, 18th mo-914, 24th mo-1156). The reporting rate peaked at 9 months (1425% increase from prior quarter). In contrast, the highest number of CITNs were observed early and significantly decreased over time (1st year-5, 2nd year-2, 3rd year-1, 4th year-1). The course of our AIR program began slowly, but as feedback to reporters increased, reporting and situational awareness increased exponentially. This result demonstrates the fruits of a successful AIR program in establishing situational awareness. CONCLUSIONS: SPC analysis can be applied to anonymous incident reporting to study the progression of institutional situational awareness. Application of our model can give other institutions a method to evaluate not only their AIR program but also their situational awareness. S. Kurapati: None. T. King: 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".