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Record W4308052674 · doi:10.1002/jhm.12979

Implementation of the I‐PASS handoff program in diverse clinical environments: A multicenter prospective effectiveness implementation study

2022· article· en· W4308052674 on OpenAlexaff
Amy J. Starmer, Nancy D. Spector, Jennifer K. O’Toole, Zia Bismilla, Sharon Calaman, Maria‐Lucia Campos, Maitreya Coffey, Lauren Destino, Jennifer L. Everhart, Jenna Goldstein, Dionne A. Graham, Jennifer Hepps, Eric Howell, Nicholas Kuzma, Greg Maynard, Patrice Melvin, Shilpa J. Patel, Alina Delia Popa, Glenn Rosenbluth, Jeffrey L. Schnipper, Theodore C. Sectish, Rajendu Srivastava, Daniel C. West, Clifton E. Yu, Christopher P. Landrigan

Bibliographic record

VenueJournal of Hospital Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersAgency for Healthcare Research and QualityIntermountain Healthcare
KeywordsMedicineQuality managementCoachingAdverse effectHandoverPatient safetyEmergency medicineInternal medicineHealth carePsychologyOperations management

Abstract

fetched live from OpenAlex

BACKGROUND: Handoff miscommunications are a leading source of medical errors. Harmful medical errors decreased in pediatric academic hospitals following implementation of the I-PASS handoff improvement program. However, implementation across specialties has not been assessed. OBJECTIVE: To determine if I-PASS implementation across diverse settings would be associated with improvements in patient safety and communication. DESIGN: Prospective Type 2 Hybrid effectiveness implementation study. SETTINGS AND PARTICIPANTS: Residents from diverse specialties across 32 hospitals (12 community, 20 academic). INTERVENTION: External teams provided longitudinal coaching over 18 months to facilitate implementation of an enhanced I-PASS program and monthly metric reviews. MAIN OUTCOME AND MEASURES: Systematic surveillance surveys assessed rates of resident-reported adverse events. Validated direct observation tools measured verbal and written handoff quality. RESULTS: 2735 resident physicians and 760 faculty champions from multiple specialties (16 internal medicine, 13 pediatric, 3 other) participated. 1942 error surveillance reports were collected. Major and minor handoff-related reported adverse events decreased 47% following implementation, from 1.7 to 0.9 major events/person-year (p < .05) and 17.5 to 9.3 minor events/person-year (p < .001). Implementation was associated with increased inclusion of all five key handoff data elements in verbal (20% vs. 66%, p < .001, n = 4812) and written (10% vs. 74%, p < .001, n = 1787) handoffs, as well as increased frequency of handoffs with high quality verbal (39% vs. 81% p < .001) and written (29% vs. 78%, p < .001) patient summaries, verbal (29% vs. 78%, p < .001) and written (24% vs. 73%, p < .001) contingency plans, and verbal receiver syntheses (31% vs. 83%, p < .001). Improvement was similar across provider types (adult vs. pediatric) and settings (community vs. academic).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.019
GPT teacher head0.409
Teacher spread0.391 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2022
Admission routes1
Has abstractyes

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