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Record W4322733672 · doi:10.1097/as9.0000000000000258

Development of the Illinois Surgical Quality Improvement Collaborative (ISQIC)

2023· article· en· W4322733672 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnnals of Surgery Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersNational Institute on Drug AbuseAgency for Healthcare Research and QualityNational Institutes of HealthPacira BioSciencesNational Heart, Lung, and Blood InstituteMallinckrodt PharmaceuticalsPacira Pharmaceuticals
KeywordsCoachingQuality managementMedicineMedical educationQuality (philosophy)NursingPsychologyEngineeringOperations management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.870
GPT teacher head0.710
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it