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Record W2946976005 · doi:10.1093/pch/pxz066.034

35 Assessing the completeness of medication reconciliation documentation by resident physicians at hospital admission for pediatric asthma patients

2019· article· en· W2946976005 on OpenAlexaboutno aff
Ashley Martin, Joanna Holland

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAsthmaMedical recordAsthma medicationPediatricsHospital admissionEmergency medicineDocumentationIntensive care medicineMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

Medication errors at admission and discharge to hospital continue to be common and preventable. Medication discrepancies occur in 22–73% of hospitalized pediatric patients and are mostly related to admissions. The process of medication reconciliation (MedRec) has been identified as an important tool in reducing medication errors and is a patient safety standard in Canadian hospitals at transitions of care. Resident physicians in our tertiary pediatric hospital are responsible for completing the MedRec form at hospital admission. We hypothesized based on clinical observation that the strength of asthma inhalers is frequently omitted on the admission MedRec. Asthma is the leading cause of hospitalization in children, and incomplete information on patient’s home controller medications could impact the medical team’s ability to make an appropriate discharge plan. To describe and quantify the completeness of admission medication reconciliation documentation by resident physicians for pediatric asthma patients. This study was a single-centre, retrospective chart review of admission MedRec documentation for pediatric asthma patients from January 2016 – December 2017. Charts with an admitting diagnosis of asthma and home asthma medications on the admission MedRec were included for review. MedRec forms were deemed incomplete if asthma medications were missing drug name, strength, dose/frequency or not reconciled. Charts with an incomplete MedRec were further reviewed to determine if the reason for the incomplete medication history was documented, and if home medications were clarified prior to hospital discharge. A total of 241 charts of pediatric asthma admissions from the review period included home asthma medications on the admission MedRec. 40% of all MedRec forms reviewed had at least one medication incompletely documented. Inhaled corticosteroids were the most frequent with 40% incomplete for any reason, 37% missing the inhaler strength, 14% missing the dose/frequency, 2% not reconciled and 1% missing the drug name. Documentation of other inhalers were also more likely to be incomplete than oral asthma medications with 27% of short-acting bronchodilators and 24% of combination inhalers incomplete for any reason. No cause for the incomplete medication history was documented in 68% of charts, and 36% had no clarification order written for home asthma medications prior to hospital discharge. Documentation of inhalers on admission MedRec forms by resident physicians for pediatric asthma patients is often incomplete. Missing information on patient’s home asthma medications could result in medication errors at discharge. Future quality improvement interventions in our institution are required.

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)0.013
metaresearch head score (Gemma)0.061
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 score0.015
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.025
GPT teacher head0.370
Teacher spread0.345 · 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
Published2019
Admission routes1
Has abstractyes

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