Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

Application of Big Data in Decision Making for Emergency Healthcare Management

Application of big data in healthcare has enhanced efficiency and decision making. This is of critical benefit to patients, healthcare professionals and the healthcare institution. Although various research studies have examined the application of big data analytics in healthcare, few studies have explored its application in emergency medicine. This research study explored the application of big data in emergency medicine in facilitating decision making among paramedics and other healthcare practitioners. Appropriate research studies were identified and reviewed systematically to explore the theme of the study. The study found that big data promoted decision making in emergency medicine through the predictor models, which enabled the healthcare practitioners make informed judgments concerning patient care

Identifying Key Factors Associated with High Risk Asthma Patients to Reduce the Cost of Health Resources Utilization

Asthma is associated with frequent use of primary health services and places a burden on the United States economy. Identifying key factors associated with increased cost of asthma is an essential step to improve practices of asthma management. The aim of this study was to identify factors associated with over utilization of primary health services and increased cost via claims data and to explore the effectiveness of case management program in reducing overall asthma related cost. Claims data analysis for Medicaid insured asthma patients in Louisiana was conducted. Asthma patients were identified using their ICD-9 and ICD-10 codes, forward variable selection was used to identify significant factors in the regression model with total cost as the dependent variable, multivariate regression was used to identify patients’ factors associated with frequent utilization of primary health services, and finally, a T-test was used to compare the difference in cost over time for case managed and non-case managed patients. Cost of four claims categories was significant to the total cost variable: primary physician visits, pharmacy prescriptions, emergency room visits and urgent care clinics visits. Median income and enrollment in case management were significant in predicting number of emergency room visits. Patients who had higher income were more likely to utilize urgent care clinics. As a side finding, this study built a prediction model for total cost, the linear regression model accuracy was compared to neural networks and the proposed threshold point in which neural network outperforms the regression model is around 6,000 data points. Patients with a history of utilization of certain health services are more likely to need case management for better health outcomes and controlled cost. future work is to perform analysis on a larger scale and include more patients related factors to identify a more holistic definition of high-risk patients

Racial and gender concordance: Effects on utilization of health services among individuals enrolled in a primary care case management delivery system

Very few studies to date have directly examined the impact of race or gender patient-provider concordance on the utilization of health services. This is particularly noteworthy given the role that the linkage between concordance and health service utilization may play in the eradication of race- and gender-based health disparities. This dissertation, grounded on the theory of Andersen’s (1995) Emerging Model of Health Services Utilization (Phase Four), used data collected from a stratified random sample of adult beneficiaries enrolled in North Carolina Medicaid’s primary care case management managed care delivery system to study this phenomenon. The data were obtained from two sources: (1) a computer assisted telephone survey of 2,815 respondents that used the North Carolina Medicaid Consumer Assessment of Healthcare Providers and Systems (CAHPS) 3.0 Adult Survey 2006 as the survey instrument and, (2) enrollment data provided by plan administrators. Propensity score matching techniques were used to sort respondents on their propensity for race concordance and gender concordance, respectively, to establish a post-test only comparison research design. The utilization of five different forms of health services – primary care, specialty care, emergency care, inpatient care, and prescription drugs – were analyzed using factor analysis, ordinary least squares linear regression, and logistic regression methodologies. The key findings are that race and gender patient-primary care provider concordance did not directly impact the utilization of primary, specialty, emergency, or inpatient care. However, concordance between patient and primary care provider was demonstrated to decrease the likelihood of using prescription drugs. The research, which is unique in its ability to control for socioeconomic and health insurance status, informs policymakers and other stakeholders tasked with allocating resources that impact the utilization of health services and other health outcomes in the quest to eliminate race- and gender-based health disparities

Understanding Health Risks for Adolescents in Protective Custody

Children in child welfare protective custody (e.g., foster care) are known to have increased health concerns compared to children not in protective custody. The poor health documented for children in protective custody persists well into adulthood; young adults who emancipate from protective custody report poorer health, lower quality of life, and increased health risk behaviors compared to young adults in the general population. This includes increased mental health concerns, substance use, sexually transmitted infections, unintended pregnancy, and HIV diagnosis. Identifying youth in protective custody with mental health concerns, chronic medical conditions, and increased health risk behaviors while they remain in custody would provide the opportunity to target prevention and intervention efforts to curtail poor health outcomes while youth are still connected to health and social services. This study leveraged linked electronic health records and child welfare administrative records for 351 youth ages 15 and older to identify young people in custody who were experiencing mental health conditions, chronic medical conditions, and health risk behaviors (e.g., substance use, sexual risk). Results indicate that 41.6% of youth have a mental health diagnosis, with depression and behavior disorders most common. Additionally, 41.3% of youth experience chronic medical conditions, primarily allergies, obesity, and vision and hearing concerns. Finally, 39.6% of youth use substances and 37.0% engage in risky sexual behaviors. Predictors of health risks were examined. Those findings indicate that women, those with longer lengths of stay and more times in custody, and those in independent living and conjugate care settings are at greatest risk for mental health conditions, chronic medical conditions, and health risk behaviors. Results suggest a need to ensure that youth remain connected to health and mental health safety nets, with particular attention needed for adolescents in care for longer and/or those placed in non-family style settings. Understanding who is at risk is critical for developing interventions and policies to target youth who are most vulnerable for increased health concerns that can be implemented while they are in custody and are available to receive services

Machine Learning in Population Health: Frequent Emergency Department Utilization Pattern Identification and Prediction

Emergency Department (ED) overcrowding is an emerging risk to patient safety and may significantly affect chronically ill people. For instance, overcrowding in an ED may cause delays in patient transportation or revenue loss for hospitals due to hospital diversion. Frequent users with avoidable visits play a significant role in imposing such challenges to ED settings. Non-urgent or "avoidable" ED use induces overcrowding and cost increases due to unnecessary tests and treatment. It is, therefore, valuable to understand the pattern of the ED visits among a population and prospectively identify ED frequent users, to provide stratified care management and resource allocation. Although most current models use classical methods like descriptive analysis or regression modelling, more sophisticated techniques may be needed to increase the accuracy of outcomes where big data is in use. This study focuses on the Machine Learning (ML) techniques to identify the ED usage pattern among frequent users and to evaluate the predicting ability of the models. I performed an extensive literature review to generate a list of potential predictors of ED frequent use. For this thesis, I used Korean Health Panel data from 2008 to 2015. Individuals with at least one ED visit were included, among whom those with four or more visits per year were considered frequent ED users. Demographic and clinical data was collected. The relationship between predictors and ED frequent use was examined through multivariable analysis. A K-modes clustering algorithm was applied to identify ED utilization patterns among frequent users. Finally, the performance of four machine learning classification algorithms was assessed and compared to logistic regression. The classification algorithms used in my thesis were Random Forest, Support Vector Machine (SVM), Bagging, and Voting. The models' performance was evaluated based on Positive Predictive Value (PPV), sensitivity, Area Under Curve (AUC), and classification error. A total of 9,348 individuals with 15,627 ED visits were eligible for this study. Frequent ED users accounted for 2.4% of all ED visits. Frequent ED users tended to be older, male, and more likely to be using ambulance as a mode of transport than non‐frequent ED users. In the cluster analysis, we identified three subgroups among frequent ED users: (i) older patients with respiratory system complaints, the highest discharged rates who were more likely to visit in Spring and Winter, (ii) older patients with the highest rate of hospitalization, who are also more likely to have used ambulance, and visited ED due to circulatory system complaints, (iii) younger patients, mostly female, with the highest rate of ED visits in summer, and lowest rate of using an ambulance, who visited ED mostly due to damages such as injuries, poisoning, etc. The ML classification algorithms predicted frequent ED users with high precision (90% - 98%) and sensitivity (87% - 91%), while showed high AUC scores from 89% for SVM to 96% for Random Forest, as well. The classification error varied among algorithms; logistic regression had the highest classification error (34.9%) while Random Forest had the least (3.8%). According to the Random Forest Importance Score, the top 5 factors predicting frequent users were disease category, age, day of the week, season, and sex. In this thesis, I showed how ML methods applies to ED users in population health. The study results show that ML classification algorithms are robust techniques with predictive power for future ED visit identification and prediction. As more data are collected and the amount of data availability increases, machine learning approaches is a promising tool for advancing the understanding of such ‘Big’ data

Health Impacts of Power-Exporting Plants in Northern Mexico

In the past two decades, rapid population and economic growth on the U.S.–Mexico border has spurred a dramatic increase in electricity demand. In response, American energy multinationals have built power plants just south of the border that export most of their electricity to the United States. This development has stirred considerable controversy because these plants effectively skirt U.S. environmental air pollution regulations in a severely degraded international airshed. Yet to our knowledge, this concern has not been subjected to rigorous scrutiny. This paper uses a suite of air dispersion, health impacts, and valuation models to assess the human health damages in the United States and Mexico caused by air emissions from two power-exporting plants in Mexicali, Baja California. We find that these emissions have limited but nontrivial health impacts, mostly by exacerbating particulate pollution in the United States, and we value these damages at more than half a million dollars per year. These findings demonstrate that power-exporting plants can have cross-border health effects and bolster the case for systematically evaluating their environmental impacts.electricity, air pollution, Mexico

Health Impacts of Power-Exporting Plants in Northern Mexico

In the past two decades, rapid population and economic growth on the U.S.–Mexico border has spurred a dramatic increase in electricity demand. In response, American energy multinationals have built power plants just south of the border that export most of their electricity to the United States. This development has stirred considerable controversy because these plants effectively skirt U.S. environmental air pollution regulations in a severely degraded international airshed. Yet to our knowledge, this concern has not been subjected to rigorous scrutiny. This paper uses a suite of air dispersion, health impacts, and valuation models to assess the human health damages in the United States and Mexico caused by air emissions from two power-exporting plants in Mexicali, Baja California. We find that these emissions have limited but nontrivial health impacts, mostly by exacerbating particulate pollution in the United States, and we value these damages at more than half a million dollars per year. These findings demonstrate that power-exporting plants can have cross-border health effects and bolster the case for systematically evaluating their environmental impacts.electricity, air pollution, Mexico