Literature DB >> 31313335

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

Alzana A Kumar1, Marc Burgard1, Sonya Stacey1,2, Indy Sandaradura3,4, Tony Lai5, Christine Coorey3, Marisol Cincunegui3, Christine E Staatz1, Stefanie Hennig1.   

Abstract

AIMS: To 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.
METHODS: Clinical 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 χ2 test of equal or given proportions to identify significant differences in response.
RESULTS: Twenty-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.
CONCLUSION: Although 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.
© 2019 The British Pharmacological Society.

Entities:  

Keywords:  Bayesian forecasting; Likert scale; survey; therapeutic drug monitoring; user-friendliness

Mesh:

Year:  2019        PMID: 31313335      PMCID: PMC6783600          DOI: 10.1111/bcp.14066

Source DB:  PubMed          Journal:  Br J Clin Pharmacol        ISSN: 0306-5251            Impact factor:   4.335


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  9 in total

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

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