Does Culture Affect the Usage of Digital Disruption Innovations: A Study Using Instagram

Online social networks are growing rapidly. As people from all over the world can use these sites without any geographic boundaries, it becomes difficult for organizations to predict user behavior. Prior literature revealed that most previous studies focused on a single culture and on a single social networking site, mainly Facebook. Also, even if there were cross-national cultural studies, they were primarily based on Hofstede’s cultural index scores. In this study, we focus on espoused national cultural values in the usage of Instagram. We propose a model and conduct a pilot test. Results indicate that people who espouse more collectivistic values tend to use Instagram more frequently and are more likely to share more posts

Inhaled medications for chronic obstructive pulmonary disease predict surgical complications and survival in stage I non-small cell lung cancer

BACKGROUND: Lung function is routinely assessed prior to surgical resection for non-small cell lung cancer (NSCLC). Further assessment of chronic obstructive pulmonary disease (COPD) using inhaled COPD medications to determine disease severity, a readily available metric of disease burden, may predict postoperative outcomes and overall survival (OS) in lung cancer patients undergoing surgery. METHODS: We retrospectively evaluated clinical stage I NSCLC patients receiving surgical treatment within the Veterans Health Administration from 2006-2016 to determine the relationship between number and type of inhaled COPD medications (short- and long-acting beta2-agonists, muscarinic antagonists, or corticosteroids prescribed within 1 year before surgery) and postoperative outcomes including OS using multivariable models. We also assessed the relationship between inhaled COPD medications, disease severity [measured by forced expiratory volume in 1 second (FEV1)], and diagnosis of COPD. RESULTS: Among 9,741 veterans undergoing surgery for clinical stage I NSCLC, patients with COPD were more likely to be prescribed inhaled medications than those without COPD [odds ratio (OR) =5.367, 95% confidence interval (CI): 4.886-5.896]. Increased severity of COPD was associated with increased number of prescribed inhaled COPD medications (P\u3c0.0001). The number of inhaled COPD medications was associated with prolonged hospital stay [adjusted OR (aOR) =1.119, 95% CI: 1.076-1.165), more major complications (aOR =1.117, 95% CI: 1.074-1.163), increased 90-day mortality (aOR =1.088, 95% CI: 1.013-1.170), and decreased OS [adjusted hazard ratio (aHR) =1.061, 95% CI: 1.042-1.080]. In patients with FEV1 ≥80% predicted, greater number of prescribed inhaled COPD medications was associated with increased 30-day mortality (aOR =1.265, 95% CI: 1.062-1.505), prolonged hospital stay (aOR =1.130, 95% CI: 1.051-1.216), more major complications (aOR =1.147, 95% CI: 1.064-1.235), and decreased OS (aHR =1.058, 95% CI: 1.022-1.095). When adjusting for other drug classes and covariables, short-acting beta2-agonists were associated with increased 90-day mortality (aOR =1.527, 95% CI: 1.120-2.083) and decreased OS (aHR =1.087, 95% CI: 1.005-1.177). CONCLUSIONS: In patients with early-stage NSCLC, inhaled COPD medications prescribed prior to surgery were associated with both short- and long-term outcomes, including in patients with FEV1 ≥80% predicted. Routine assessment of COPD medications may be a simple method to quantify operative risk in early-stage NSCLC patients

Two Essays on Leveraging Analytics to Improve Healthcare

The healthcare cost has continued to increase over the past few years despite various policies, efforts, and initiatives taken by the government. It is still projected to grow over the next few years by the Centers for Medicare and Medicaid Services (CMS). Readmissions have been a major contributor to the increase in costs and have always been a contributing factor. To get a perspective, considering the fact that at least 9% of individuals who had COVID-19 were likely to get readmitted shortly, according to a study by the Centers for Disease Control and Prevention (CDC) COVID-19 response team, along with their high estimated treatment cost, the problem of high healthcare costs will continue to grow. The implementation of the American Recovery and Reinvestment Act of 2009 has led to massive increase in digital health data facilitating various studies to utilize analytics to improve healthcare. The goal of the two essays in this dissertation is to address the identified research gaps in the literature in readmission analytics.In Essay 1, I deploy the term readmission in two different ways and then focus on building and identifying predictive models that are suitable for costs billed by hospitals for the identified readmission categories. By using a data-driven approach, my initial analysis revealed that 21% of readmitted individuals (regardless of the number of days to readmission) alone contributed to 48% of the healthcare cost. Apart from that, my analysis revealed that the readmission cost (for the identified readmission categories in this study) varied from the previous admission cost at both individual and aggregated levels. Deep learning-based models performed the best for all scenarios. In Essay 2, I focus on creating a multitask learning-based joint model for predicting different outcomes related to readmissions, namely, likelihood, cost, and length of stay. I then evaluate the performance of the joint model and analyze its usefulness. Analysis was done for the identified top three categories of readmission belonging to the same major diagnostic groups from Essay1. Results showed that the joint model performed slightly better than the single-task baseline model for specific scenarios. The joint model was also beneficial in determining predictors that were consistently important to predict all the outcomes related to readmissions regardless of not giving us a universally best model

Use of claims data to predict the inpatient length of stay among U.S. stroke patients

Background: The prediction of stroke inpatient length of stay (LOS) attracts much attention from researchers worldwide as it facilitates resource management and improves care. However, studies largely underutilized the claims data for predicting stroke LOS. The predictive models for the U.S. stroke population are understudied. Purpose: To evaluate the feasibility of using claims data for stroke LOS prediction and to identify new important predictors of LOS in U.S. stroke patients. Methods: Data preparation, analyses, and predictive modeling processes were conducted on a retrospective dataset, including claims and EHR data about acute care stroke admissions during 2010–2018. Two tree-based models (i.e., the eXtreme Gradient Boosting (XGBoost) model and Categorical Boosting (CatBoost) model) were trained through 10-fold cross-validation and compared with a baseline model that did not include any predictors. The predictive performance was evaluated on the holdout set using mean absolute error (MAE) and root mean squared error (RMSE). Importance plots and SHAP (SHapley Additive exPlanations) plots were used to identify the important predictors. Results: A total of 6102 stroke patients were included, with an average LOS of 6.4 days. The predictive models built using claims data (RMSE: 1.627; MAE: 0.530) performed similarly well as those built on the entire dataset, including additional variables from EHR (RMSE: 1.622; MAE: 0.533). Important predictors were admission channel and type, comorbidities (e.g., acute respiratory failure), medical services used (e.g., critical care, ambulance), facility characteristics (e.g., type, size), patient demographics, and patient socioeconomic status. Among these important predictors, admission channel and type, medical services including critical care and ambulance, facility type, and patient socioeconomic status were newly identified predictors not studied before. Conclusion: Claims data are suitable for stroke LOS prediction. The newly identified important predictors from this study could be integrated with other existing key predictors identified in previous research to improve the prediction, thereby aiding in better stroke care management