Evidence map›Paper›PMID 31313335›Full record

ArticleBritish journal of clinical pharmacology2019

An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting.

Alzana A Kumar, Marc Burgard, Sonya Stacey, Indy Sandaradura, Tony Lai, Christine Coorey, Marisol Cincunegui, Christine E Staatz, Stefanie Hennig

Abstract readComparative StudyMulticenter Study
In one paragraph

Article in British journal of clinical pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. When and How to Use MIC in Clinical Practice?Antibiotics (Basel, Switzerland) · 2022
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  17. Challenges of Vancomycin Dosing and Therapeutic Monitoring in Neonates.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Alzana A KumarSchool of Pharmacy, The University of Queensland, Brisbane, QLD, Australia.
Marc BurgardSchool of Pharmacy, The University of Queensland, Brisbane, QLD, Australia.
Sonya StaceySchool of Pharmacy, The University of Queensland, Brisbane, QLD, Australia.
Indy SandaraduraWestmead Hospital, Westmead, NSW, Australia.
Tony LaiThe Children's Hospital at Westmead, Westmead, NSW, Australia.
Christine CooreyWestmead Hospital, Westmead, NSW, Australia.
Marisol CincuneguiWestmead Hospital, Westmead, NSW, Australia.
Christine E StaatzSchool of Pharmacy, The University of Queensland, Brisbane, QLD, Australia.
Stefanie HennigSchool of Pharmacy, The University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0001-5972-3711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTo evaluate 3 Bayesian forecasting (BF) programs-TDMx, InsightRx and DoseMe-on their user-friendliness and common liked and disliked features through a survey of hospital pharmacists.

methodsClinical pharmacists across 3 Australian hospitals that did not use a BF program were invited to a BF workshop and complete a survey on programs they trialled. Participants were given 4 case scenarios to work through and asked to complete a 5-point Likert scale survey evaluating the program's user-friendliness. Liked and disliked features of each program were ascertained through written responses to open-ended questions. Survey results were compared using a χ

resultsTwenty-seven pharmacists, from hospitals, participated. BF programs were rated overall as user-friendly with 70%, 41% and 37% (P = .02) of participants recording a Likert score of 4 or 5 for DoseMe, TDMx and InsightRx, respectively. Participants found it easy to access all required information to use the programs, understood dosing recommendations and visualisations given by each program, and thought programs supported decision-making with >50% of participants scoring a 4 or 5 across the programs in these categories. Common liked features across all programs were the graphical displays and ease of data entry, while common disliked features were related to the units, layout and information display.

conclusionAlthough differences exist between programs, all 3 programs were most commonly rated as user-friendly across all themes evaluated, which provides useful information for healthcare facilities wanting to implement a BF program.

Indexed as

Decision Support TechniquesAdultAustraliaBayes TheoremCross-Sectional StudiesDrug MonitoringEducation, Pharmacy, ContinuingFemaleHumansMalePharmacistsPharmacy Service, HospitalSurveys and QuestionnairesBayesian forecastingLikert scalesurveytherapeutic drug monitoringuser-friendliness

Identifiers

PMID31313335
PMCPMC6783600

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.