Simulation-Based Electronic Health Record Usability Evaluation: A Proof of Concept

Poor usability of Electronic Health Records (EHR) solutions is directly associated with physician burnout. While the survey and observational methods have been utilized widely in the usability evaluation of EHRs, it does not seem to be helping with the continuous improvement of EHR design and user satisfaction. We address this gap by presenting a discrete event simulation-based model that can add objectivity to the extant EHR usability methods. Evaluating EHR usability from the perspective of operations and workflow can help vendors design and develop better systems. This short paper presents a proof-of-concept simulation model with assumed task-time distributions. Our main research question is how we can use simulation techniques to objectively evaluate EHR usability? The simulation model results in terms of resource (clinician) utilization metrics can serve as a proxy to evaluate the efficiency component of the EHR usability at the departmental leve

Predicting the Outcome of a Football Game: A Comparative Analysis of Single and Ensemble Analytics Methods

As analytical tools and techniques advance, increasingly large numbers of researchers apply these techniques on a variety of different sports. With nearly 4 billion followers, it is estimated that association football, or soccer, is the most popular sports for fans across the world by a large margin. The objective of this study is to develop a model to predict the outcomes of soccer (or association football) games (win-loss-draw), and determine factors that influence game outcomes. We used 10 years of comprehensive game-level data spanning the years 2007-2017 in the Turkish Super League, and tested a variety of classifiers to identify the most promising methods for outcome predictions

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Data Quality in Very Large, Multiple-Source, Secondary Datasets for Data Mining Applications

The data mining research community is increasingly addressing data quality issues, including problems of dirty data. Hand, Blunt, Kelly and Adams (2000) have identified high-level and low-level quality issues in data mining. Kim, Choi, Hong, Kim and Lee (2003) have compiled a useful, complete taxonomy of dirty data that provides a starting point for research in effective techniques and fast algorithms for preprocessing data, and ways to approach the problems of dirty data. In this study we create a classification scheme for data errors by transforming their general taxonomy to apply to very large multiple-source secondary datasets. These types of datasets are increasingly being compiled by organizations for use in their data mining applications. We contribute this classification scheme to the body of research addressing quality issues in the very large multiple-source secondary datasets that are being built through today’s global organizations’ massive data collection from the Internet

Development of an Expert System Based Experimental Frame for Modeling of Manufacturing Systems

Industrial Engineering and Managemen

Adjusting COVID-19 reports for countries' age disparities: a comparative framework for reporting performances

Objectives: The COVID-19 outbreak has impacted distinct health care systems differently. While the rate of disease for COVID-19 is highly age-variant, there is no unified and age/gender-inclusive reporting taking place. This renders the comparison of individual countries based on their corresponding metrics, such as CFR difficult. In this paper, we examine cross-country differences, in terms of the age distribution of symptomatic cases, hospitalizations, intensive care unit (ICU) cases, and fatalities. In addition, we propose a new quality measure (called dissonance ratio) to facilitate comparison of countries’ performance in testing and reporting COVID-19 cases (i.e., their reporting quality). Methods: By combining population pyramids with estimated COVID-19 age dependent conditional probabilities, we bridge country-level incidence data gathered from different countries and attribute the variability in data to country demographics. Results: We show that age-adjustment can account for as much as a 22-fold difference in the expected number of fatalities across different countries. We provide case, hospitalization, ICU, and fatality breakdown estimates for a comprehensive list of countries. Also, a comparison is conducted between countries in terms of their performance in reporting COVID-19 cases and fatalities. Conclusions: Our research sheds light on the importance of and propose a methodology to use countries’ population pyramids for obtaining accurate estimates of the healthcare system requirements based on the experience of other, already affected, countries at the time of pandemics