Development of the Illinois Surgical Quality Improvement Collaborative (ISQIC)
Bibliographic record
Abstract
INTRODUCTION: In 2014, 56 Illinois hospitals came together to form a unique learning collaborative, the Illinois Surgical Quality Improvement Collaborative (ISQIC). Our objectives are to provide an overview of the first three years of ISQIC focused on (1) how the collaborative was formed and funded, (2) the 21 strategies implemented to support quality improvement (QI), (3) collaborative sustainment, and (4) how the collaborative acts as a platform for innovative QI research. METHODS: ISQIC includes 21 components to facilitate QI that target the hospital, the surgical QI team, and the peri-operative microsystem. The components were developed from available evidence, a detailed needs assessment of the hospitals, reviewing experiences from prior surgical and non-surgical QI Collaboratives, and interviews with QI experts. The components comprise 5 domains: guided implementation (e.g., mentors, coaches, statewide QI projects), education (e.g., process improvement (PI) curriculum), hospital- and surgeon-level comparative performance reports (e.g., process, outcomes, costs), networking (e.g., forums to share QI experiences and best practices), and funding (e.g., for the overall program, pilot grants, and bonus payments for improvement). RESULTS: Through implementation of the 21 novel ISQIC components, hospitals were equipped to use their data to successfully implement QI initiatives and improve care. Formal (QI/PI) training, mentoring, and coaching were undertaken by the hospitals as they worked to implement solutions. Hospitals received funding for the program and were able to work together on statewide quality initiatives. Lessons learned at one hospital were shared with all participating hospitals through conferences, webinars, and toolkits to facilitate learning from each other with a common goal of making care better and safer for the surgical patient in Illinois. Over the first three years, surgical outcomes improved in Illinois. DISCUSSION: The first three years of ISQIC improved care for surgical patients across Illinois and allowed hospitals to see the value of participating in a surgical QI learning collaborative without having to make the initial financial investment themselves. Given the strong support and buy-in from the hospitals, ISQIC has continued beyond the initial three years and continues to support QI across Illinois hospitals.
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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 | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".