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Assessing the implementation of a tertiary care comprehensive pediatric asthma education program using electronic medical records and decision support tools

2024· article· en· W6902240888 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaMedical recordReferralEmergency departmentKnowledge translationPatient educationCertificationPsychological interventionPulmonologists

Abstract

fetched live from OpenAlex

Self-management education is integral for proper asthma management. However, there is an accessibility gap to self-management education following asthma hospitalizations. Most pediatric patients and their families receive suboptimal or no education. To implement a comprehensive pediatric asthma education program and evaluate subsequent self-management knowledge in patients as well as behavior change outcomes reflected in the frequency of asthma related repeat emergency department visits and hospitalization. The program implementation was informed by the Knowledge to Translation Action Framework and the i-PARIHS model for quality improvement and involved several iterative stages. We implemented a comprehensive asthma education program for the families of all children 0-18 years old who had been admitted for an asthma exacerbation to the Children’s Hospital of Eastern Ontario (CHEO), beginning on April 1, 2018. The program was adapted to the stages of the Knowledge Translation to Action Framework including undertaking an environmental scan, expert stakeholder feedback, reviews, addressing barriers, and tailoring the intervention, along with evaluating knowledge and health outcomes. Education was delivered over 1-2 h in personalized individual or small group settings, within 4 wk of hospital discharge. All education was provided by registered nurses or respiratory therapists who were also certified asthma educators. The EPIC electronic medical record was used to facilitate referral and scheduling of asthma education sessions, and to track subsequent acute asthma visits. We compared the frequency of a repeat asthma emergency department (ED) visit or hospitalization within 1-year following an initial asthma hospitalization for children who would have received comprehensive asthma education, to a historical cohort of children who were hospitalized between April 9, 2017 – Apr 8, 2018, and did not receive asthma education. The program had a high enrollment, capturing nearly 75% of the target population. Most families found the program to be acceptable and reported increased knowledge of how to manage asthma. We identified a crude overall 54% reduction in repeat hospitalizations among children 1 year after implementation of the asthma education program (i.e. 10.2% (23/225) repeat hospitalization rate pre- implementation versus 4.8% (11/227) post-implementation). In adjusted time-to event analysis, this reduction was prominent at 3 months among those who received comprehensive asthma education, relative to those who did not, but this improvement was not sustained by 1 year (HR =1.1, 95% CI =0.55- 2.05; p-value = 0.6). Although we did not find long-term improvements in ED visits, or hospitalizations, in children of caregivers who participated in comprehensive asthma education, the asthma education program holds potential given that most patients found it to be acceptable and that it increased asthma management knowledge. A future asthma education program should include multiple sessions to ensure that the knowledge and behavior change will be sustained, leading ultimately to long-term reductions in repeat ED visits and hospitalizations.

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)0.014
metaresearch head score (Gemma)0.034
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 score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.407
Teacher spread0.374 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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