Education increases decision-rule use: An investigation of education and incentives to improve decision making.

Neumann, Marvin; Hengeveld, Martijn; Niessen, A. Susan M.; Tendeiro, Jorge N.; Meijer, Rob R.

doi:10.1037/xap0000372

Cited by 11 publications

(10 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using data from studies like from Ferreyro et al or our study using Bayesian network analysis may further unravel how, why, and based on what variables healthcare providers make their predictions [ 30 , 31 ]. Eventually, insight into the drivers of clinical predictions could be valuable for developing and evaluating decision algorithms and may inform future healthcare providers' education [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

et al. 2021

View full text Add to dashboard Cite

Background Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students’, nurses’, and physicians’ estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up. Methods The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians. Results In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed. Conclusions Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts.

show abstract

Section: Discussionmentioning

confidence: 99%

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

et al. 2021

View full text Add to dashboard Cite

show abstract

“…According to the statistical indicators in these reports, the scientific and rational education management can be enhanced. e data collected in accordance with the educational management indicators are based on the problems that education managers care about, and the results can well reflect the relationship between various educational elements, which is convenient for education decision-makers to comprehensively and intuitively grasp the information hidden behind the educational data [13].…”

Section: Research Significancementioning

confidence: 99%

Analysis of University Education Management Based on Artificial Intelligence

Zhu

2022

Security and Communication Networks

View full text Add to dashboard Cite

With the rapid development of information technology, the process of informatization of education management has been accelerated. In this context, more and more education management information systems have been used in education management, providing a lot of data support for education decision-making. In addition, the development of artificial intelligence has greatly changed the way people work and live. Intelligence has emerged in various fields, bringing great convenience to people, especially the university education management. This study will integrate artificial intelligence and university classroom teaching and apply it in the field of education management. In particular, the proposed intelligent education management system mainly includes three submodules: preclass attendance, in-class state monitoring, and after-class online learning. The main function of the preclass attendance module is that half an hour before the class starts, the camera captures students’ video information and sends it back to the convolutional neural network (CNN) model for face recognition processing. In class, the state detection module is mainly based on face recognition to judge the state of students. The after-class module analyzes the evaluation information of students’ online learning to provide a teaching reference for the school. The system proposed in this study can improve the quality of students’ classroom learning and teachers’ monitoring of the quality of students’ classroom.

show abstract

“…Another possible explanation is the information provided to participants, stating that the personal statement was a poor predictor, and the other two predictors were good predictors of academic achievement. Providing participants with this information resulted in a stringent test of the hypotheses, and a prior study found that providing educational information can improve predictive accuracy (Neumann et al, 2021). Providing predictor information could have had similar effects.…”

Section: Discussionmentioning

confidence: 99%

Using narratives and numbers in performance prediction: Attitudes, confidence, and validity

Niessen

Kausel

Neumann

2021

Int J Selection Assessment

Self Cite

View full text Add to dashboard Cite

In a preregistered prediction-task experiment, we investigated the effect of narrative versus quantified information on decision-maker perceptions, confidence, predictor weighting, and predictive accuracy when making performance predictions. We also investigated the effect of who quantifies information (the decision maker or someone else).As expected, we found higher perceived informativeness and use intentions for narrative than quantified information. Information presented narratively was also weighted somewhat more heavily than quantified information. Using quantitative information quantified by decision makers themselves yielded higher perceived autonomy and use intentions than quantitative information quantified by someone else. However, no differences in prediction confidence were found and self-and other-produced quantifications received identical weight. Moreover, unexpectedly, differences in weighting did not translate to differences in predictive accuracy.

show abstract

Education increases decision-rule use: An investigation of education and incentives to improve decision making.

Abstract: Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Cited by 11 publications

References 95 publications

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

Analysis of University Education Management Based on Artificial Intelligence

Using narratives and numbers in performance prediction: Attitudes, confidence, and validity

Contact Info

Product

Resources

About