Using data analytics for telehealth utilization: A case study in Arkansas.

INTRODUCTION: Many patients used telehealth services during the COVID-19 pandemic. In this study, we evaluate how different factors have affected telehealth utilization in recent years. Decision makers at the federal and state levels can use the results of this study to inform their healthcare-related policy decisions. METHODS: We implemented data analytics techniques to determine the factors that explain the use of telehealth by developing a case study using data from Arkansas. Specifically, we built a random forest regression model which helps us identify the important factors in telehealth utilization. We evaluated how each factor impacts the number of telehealth patients in Arkansas counties. RESULTS: Of the 11 factors evaluated, five are demographic, and six are socioeconomic factors. Socioeconomic factors are relatively easier to influence in the short term. Based on our results, broadband subscription is the most important socioeconomic factor and population density is the most important demographic factor. These two factors were followed by education level, computer use, and disability in terms of their importance as it relates to telehealth use. DISCUSSION: Based on studies in the literature, telehealth has the potential to improve healthcare services by improving doctor utilization, reducing direct and indirect waiting times, and reducing costs. Thus, federal and state decision makers can influence the utilization of telehealth in specific locations by focusing on important factors. For example, investments can be made to increase broadband subscriptions, education levels, and computer use in targeted locations.

Clustering high throughput biological data with B-MST, a minimum spanning tree based heuristic.

To address important challenges in bioinformatics, high throughput data technologies are needed to interpret biological data efficiently and reliably. Clustering is widely used as a first step to interpreting high dimensional biological data, such as the gene expression data measured by microarrays. A good clustering algorithm should be efficient, reliable, and effective, as demonstrated by its capability of determining biologically relevant clusters. This paper proposes a new minimum spanning tree based heuristic B-MST, that is guided by an innovative objective function: the tightness and separation index (TSI). The TSI presented here obtains biologically meaningful clusters, making use of co-expression network topology, and this paper develops a local search procedure to minimize the TSI value. The proposed B-MST is tested by comparing results to: (1) adjusted rand index (ARI), for microarray data sets with known object classes, and (2) gene ontology (GO) annotations for data sets without documented object classes.

Clustering of High Throughput Gene Expression Data.

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community.