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THE ROLE OF THE PAEDIATRIC CLINICAL PHARMACIST IN REDUCING MEDICATION ERRORS: A SYSTEMATIC LITERATURE REVIEW

2015· article· en· W2172930987 on OpenAlexaboutno aff
Sharon Conroy, Ahmed Alsenani, Helen Sammons

Bibliographic record

VenueArchives of Disease in Childhood · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMEDLINEPharmacistPsychological interventionClinical pharmacyPopulationPediatricsSystematic reviewFamily medicineIntensive care medicineEmergency medicinePharmacyNursing

Abstract

fetched live from OpenAlex

Aim Clinical pharmacists in some countries are a recognised primary source for providing evidence based (where possible) information and advice, to ensure delivery of the correct, safest and most effective medication to patients.1 We wished to determine from the literature: the effect of paediatric clinical pharmacists9 activities on reducing medication error rates; the nature of pharmacists9 interventions to minimise or prevent medication errors in children; other pharmacists9 contributions to patient care. Methods Systematic search of five databases: EMBASE, International Pharmaceutical Abstracts, Ovid MEDLINE(R), Allied and Complementary Medicine and Cumulative Index to Nursing and Allied Health Literature to end of July 2013, using 74 keywords. Inclusion criteria: original research studies identifying the effect of pharmacists9 activities on reducing/detecting medication errors in neonatal or paediatric patients, or in the general population, where neonatal or paediatric data was separately identified. Results Twenty-five relevant studies were identified in 13 different countries (mostly the US followed by the UK). Studies used three methods of data collection to detect medication errors: chart review, review of incident reports and direct observation of nurses and parents administering medications. Pharmacists identified prescribing, administration and medication errors in general (where the type of error was not specified). Nine studies provided only the number of errors intercepted with no denominator. The remaining 16 studies identified the error rate using seven different denominators. These methodological variations made studies very difficult to compare. Pharmacists intercepted 29 types of medication errors, most commonly: wrong dose (21 studies); wrong drug (11); wrong route of administration (8); incomplete prescriptions (8) and omission of medications (8). Antibiotics were the most common group of medications where interventions were made. The acceptance rate of pharmacists9 recommendations by doctors was identified by nine studies and ranged between 24% (in US) and 98% (in Canada). Pharmacists made 15 different types of contributions. The most common were: provision of information in response to other healthcare professionals9 queries (7 studies); cost savings (e.g. €532 per patient saved in a German paediatric neurology ward as a result of preventing wastage of medications) (5); medication reconciliation (3); education of patients/parents in drug administration to facilitate adherence (3). Education was another successful initiative highlighted in six studies e.g. education of doctors decreased the prescribing dosing error rate from 61.8 to 1.3% of all orders in an Indian specialist children9s hospital.2 It decreased the nurse administration error rate from 40.4% to 7.9% of all administrations and parents from 96.6% to 5.6% of all administrations in Germany.3 Conclusion Paediatric clinical pharmacists use a wide range of initiatives to reduce and prevent medication errors mainly in the US and the UK. Little information is available from other parts of the world where such activities are also likely to be valuable.

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)0.021
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0130.013
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.388
Teacher spread0.347 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations3
Published2015
Admission routes1
Has abstractyes

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