Dataset Information

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

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 ?2 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.

SUBMITTER: Kumar AA

PROVIDER: S-EPMC6783600 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Kumar Alzana A AA Burgard Marc M Stacey Sonya S Sandaradura Indy I Lai Tony T Coorey Christine C Cincunegui Marisol M Staatz Christine E CE Hennig Stefanie S

British journal of clinical pharmacology 20190806 10

<h4>Aims</h4>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.<h4>Methods</h4>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 prog ...[more]

PMID: 31313335

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Project description:BackgroundIn case of a cardiac arrest, start of cardiopulmonary resuscitation by a bystander before the arrival of the emergency personnel increases the probability of survival. However, the steps of high-quality resuscitation are not known by every bystander or might be forgotten in this complex and time-critical situation. Mobile phone apps offering real-time step-by-step instructions might be a valuable source of information.ObjectiveThe aim of this study was to examine mobile phone apps offering real-time instructions in German or English in case of a cardiac arrest, to evaluate their adherence to current resuscitation guidelines, and to test their usability.MethodsOur 3-step approach combines a systematic review of currently available apps guiding a medical layperson through a resuscitation situation, an adherence testing to medical guidelines, and a usability evaluation of the determined apps. The systematic review followed an adapted preferred reporting items for systematic reviews and meta-analyses flow diagram, the guideline adherence was tested by applying a conformity checklist, and the usability was evaluated by a group of mobile phone frequent users and emergency physicians with the system usability scale (SUS) tool.ResultsThe structured search in Google Play Store and Apple App Store resulted in 3890 hits. After removing redundant ones, 2640 hits were checked for fulfilling the inclusion criteria. As a result, 34 apps meeting all inclusion criteria were identified. These included apps were analyzed to determine medical accuracy as defined by the European Resuscitation Council's guidelines. Only 5 out of 34 apps (15%, 5/34) fulfilled all criteria chosen to determine guideline adherence. All other apps provided no or wrong information on at least one relevant topic. The usability of 3 apps was evaluated by 10 mobile phone frequent users and 9 emergency physicians. Of these 3 apps, solely the app "HELP Notfall" (median=87.5) was ranked with an SUS score above the published average of 68. This app was rated significantly superior to "HAMBURG SCHOCKT" (median=55; asymptotic Wilcoxon test: z=-3.63, P<.01, n=19) and "Mein DRK" (median=32.5; asymptotic Wilcoxon test: z=-3.83, P<.01, n=19).ConclusionsImplementing a systematic quality control for health-related apps should be enforced to ensure that all products provide medically accurate content and sufficient usability in complex situations. This is of exceptional importance for apps dealing with the treatment of life-threatening events such as cardiac arrest.

Project description:ObjectiveTo illustrate the benefits of deploying individual headache forecasting models using continuous updating with Bayesian methods.BackgroundThe ability to reliably forecast headache attacks within an individual over time would enhance the study of attacks and allow preemptive treatment. However, deploying a suitable forecasting model in a clinical setting will likely involve several unique challenges related to heterogeneity in the predictor weights, limited or sparse data, and the need for a quick "warm-up." The use of Bayesian methods offers solutions to each of these specific challenges.MethodsThis was a post hoc analysis of a cohort study of individuals with episodic migraine attacks. Individuals completed daily diaries that allowed the estimation of several forecasting models, each using different types of ancillary information incorporated into formal prior probability distributions. An in silico analysis was conducted that mimicked the deployment of these models in a clinical-like setting where the parameters of the models were continuously updated and evaluated each day using root mean square error (RMSE).ResultsIndividuals (N = 95) were followed for 50 days and contributed 3359 days of nonmissing diary data. During the observation period, there were 1293/3359 (38.5%) days with a headache attack. Self-reported baseline headache frequency was associated with the corresponding predicted probability of future attacks, r = 0.15-0.39. At Day 25, the correlation between baseline information and predicted attack likelihood was r = 0.29 (95% CI: 0.09-0.47). Additionally, the use of prior probability distributions for model parameters improved the model fit, especially early in the deployment of the models (e.g., Day 5 RMSE 0.45 vs. 0.43). Models using informative prior probability distributions outperformed the models estimated without this information during the first 42 days of observation, although performance became more similar as more data were collected.ConclusionsThis analysis demonstrates the value of Bayesian methods in using additional available information to improve forecasting model performance, especially early in the deployment of a forecasting model. To obtain the full value of such models or to apply any model in clinical settings, a model with adequate discrimination and calibration will be needed.

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

Publications

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

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