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Record W4411846588 · doi:10.3899/jrheum.2025-0314.48

Optimizing Inpatient Kawasaki Disease Care - A Quality Improvement Project to Reduce Length of Stay

2025· article· en· W4411846588 on OpenAlexaffvenue
Audrea Chen, Beth Gamulka, Hanof Alkhdher, Macarena Palomer, Rae S. M. Yeung

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsHospital for Sick ChildrenUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineKawasaki diseaseQuality (philosophy)Quality managementEmergency medicineMEDLINEIntensive care medicinePediatricsInternal medicineOperations management

Abstract

fetched live from OpenAlex

Objectives Kawasaki Disease (KD), an acute pediatric vasculitis, requires timely inpatient management to prevent coronary artery disease.[1] The Hospital for Sick Children (SickKids) sees about 100 cases of KD annually. Hospitalization and lack of reliable educational resources negatively impact on the patient and family experience.[2,3] By eliminating inefficiencies, reducing length of stay (LOS) and improving patient education, we aim to optimize patient experience and conserve healthcare resources. The objective is to optimize inpatient care for uncomplicated KD by reducing LOS by 33% at SickKids by December 2024. Methods This is a quality improvement project using the Model for Improvement Framework. The project includes all patients diagnosed and treated at SickKids. Stakeholders include Pediatric Medicine, Rheumatology, KD clinic, Transfusion Medicine and parent representatives. The outcome measure is LOS. Outcome measurement excluded patients with: shock, prolonged antibiotics, IVIG-resistance and coronary artery lesions. Process and balancing measures were collected for all patients. Balancing measures included rate of re-presentation to emergency care, untreated fever and patient satisfaction. Cost savings were estimated by calculating reductions in hours of admission as well as unnecessary bloodwork. A chart review of 26 patients in 2023 demonstrated a baseline average LOS of 56 hours. A new clinical pathway was developed to standardize care and eliminate unnecessary testing. Updated educational materials for caregivers were developed. Patient satisfaction surveys collected anonymous feedback on the inpatient experience as a balancing measure and were compared to hospital-wide patient satisfaction data. Results Thirty-two patients were admitted with KD since implementation of change ideas in December 2023, with outcome measures collected for 15 patients. The average LOS was 48 hours (14% reduction from baseline) (Figure 1). Process measures among 32 patients showed excellent uptake in change ideas following 3 PDSA cycles. Patient satisfaction remained high and there were no increased rates of healthcare utilization or safety issues between discharge and first clinic follow-up. There was an estimated $14,725 in cost savings over 32 admissions. Conclusion Although a 14% reduction in LOS did not meet our target, there was a significant downward shift in LOS due to a strong uptake of the new KD clinical pathway. Adherence to the KD pathway was achieved through stakeholder engagement, standardization and use of automation. Cost and resource savings were achieved through reduction in LOS and unnecessary laboratory testing. Further data collection is required to demonstrate sustainability; however, this project and pathway may be of interest to other institutions that care for patients with KD. [1.] Money NM. Hosp Pediatr 2022;e2021006364. [2.] Robinson C. Paediatr Child Health 2022;27(3):160-8. [3.] Chahal N. J Pediatr Health Care Off Publ Natl Assoc Pediatr Nurse Assoc Pract 2010;24(4):250-7.

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)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.354
Teacher spread0.327 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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