Establishing a sorting protocol for healthcare databases

Background: Health information records in many countries, especially developing countries, are still paper based. Compared to electronic systems, paper-based systems are disadvantageous in terms of data storage and data extraction. Given the importance of health records for epidemiological studies, guidelines for effective data cleaning and sorting are essential. They are, however, largely absent from the literature. The following paper discusses the process by which an algorithm was developed for the cleaning and sorting of a database generated from emergency department records in Lebanon.Design and methods: Demographic and health related information were extracted from the emergency department records of three hospitals in Beirut. Appropriate categories were selected for data categorization. For health information, disease categories and codes were selected according to the International Classification of Disease 10th Edition.Results: A total of 16,537 entries were collected. Demographic information was categorized into groups for future epidemiological studies. Analysis of the health information led to the creation of a sorting algorithm which was then used to categorize and code the health data. Several counts were then performed to represent and visualize the data numerically and graphically.Conclusions: The article describes the current state of health information records in Lebanon and the associated disadvantages of a paper-based system in terms of storage and data extraction. Furthermore, the article describes the algorithm by which health information was sorted and categorized to allow for future data analysis using paper records

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

Can Multimedia Tools Promote Big Data Learning and Knowledge in a Diverse Undergraduate Student Population?

Background and Purpose: Multimedia tools are an integral part of teaching and learning in today’s technology-driven world. The present study explored the role of a newly-developed video introducing the emerging field of big data to a diverse undergraduate student population. Particularly, we investigated whether introduction of a multimedia tool would influence students’ self-perceived knowledge related to various big data concepts and future interest in pursuing the field, and what factors influence these. Methods: Students (n = 331) completed a survey on-line after viewing the video, consisting of Likerttype and quantitative questions about students’ learning experience, future interest in big data, and background. The dataset was analyzed via ANOVA and multiple linear regression methods. Results: Gender, major, and intended degree were significantly associated with students’ learning experience and future interest in big data. Moreover, students who had no prior exposure to big data reported a better learning experience, although they also reported less likelihood to pursue it in the future. Conclusion: Multimedia tools may serve as an effective learning tool in introducing and creating interest in a diverse group of students related to introductory big data science concepts. Both similarities and differences were observed regarding such behaviors among different student sub-groups

Big Data and its application in Biomedical Domain

Big data [1,2,3]  redefined the analytical space with its features such as Volume, Velocity, Variety, Veracity and Value. Due to advancement in the technology, storing, retrieving of data is becoming easier than in the previous decades. Due to this fact, the volume of data in the form of text, image, sound is increasing at a rapid pace in all the fields including the biomedical field [4, 5]. Patient records in the form of electronic medical records are being stored in the data cloud which includes data in different forms. To analyse the data of such magnitude and variety, traditional analytical tools and methods are not sufficient. The Big Data analytical techniques help to analyse these kinds of data and helps us to arrive at decisions quickly. Managing the privacy, security and government regulations related to patient data remains as a challenge [6] in implementing Big Data analytical tools in biomedical domain. This paper starts with the overview of Big Data architecture and moves on to explaining the tools and technologies used in Big Data and the uses of Big Data in Biomedical Field

Big Data Analytics: A Survey

Internet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology

Towards Responsible Data Analytics: A Process Approach

The big data movement has been characterised by highly enthusiastic promotion, and caution has been in short supply. New data analytic techniques are beginning to be applied to the operational activities of government agencies and corporations. If projects are conducted in much the same carefree manner as research experiments, they will inevitably have negative impacts on the organisations conducting them, and on their employees, other organisations and other individuals. The limited literature on process management for data analytics has not yet got to grips with the risks involved. This paper presents an adapted business process model that embeds quality assurance, and enables organisations to filter out irresponsible applications

Large spatial datasets: Present Challenges, future opportunities

The key advantages of a well-designed multidimensional database is its ability to allow as many users as possible across an organisation to simultaneously gain access and view of the same data. Large spatial datasets evolve from scientific activities (from recent days) that tends to generate large databases which always come in a scale nearing terabyte of data size and in most cases are multidimensional. In this paper, we look at the issues pertaining to large spatial datasets; its feature (for example views), architecture, access methods and most importantly design technologies. We also looked at some ways of possibly improving the performance of some of the existing algorithms for managing large spatial datasets. The study reveals that the major challenges militating against effective management of large spatial datasets is storage utilization and computational complexity (both of which are characterised by the size of spatial big data which now tends to exceeds the capacity of commonly used spatial computing systems owing to their volume, variety and velocity). These problems fortunately can be combated by employing functional programming method or parallelization techniques

Open Source Big Data Platforms and Tools: An Analysis

Big data is attracting an excessive amount of interest in the IT and academic sectors. On a regular basis, computer and digital industries generate more data than they have space to store. In the current situation, five billion people have their own mobile phone, and over two billion people are linked globally to exchange various types of data. By 2020, it is estimated that about fifty billion people will be connected to the internet. During2020, data generation, use, and sharing would be forty-four times higher than in previous years. A variety of sectors and organizations are using big data to manage various operations. As a result, a thorough examination of big data's benefits, drawbacks, meaning, and characteristics is needed. The primary goal of this research is to gather information on the various open-source big data tools and platforms that are used by various organizations. In this paper we use a three perspective methodology to identify the strength and weaknesses of the workflow in a open source big data arena. This helps to establish a pipeline of workflow events for both researcher and entrepreneur decision making

Integrating Wearable Devices and Recommendation System: Towards a Next Generation Healthcare Service Delivery

Researchers have identified lifestyle diseases as a major threat to human civilization. These diseases gradually progress without giving any warning and result in a sudden health aggravation that leads to a medical emergency. As such, individuals can only avoid the life-threatening condition if they regularly monitor their health status. Health recommendation systems allow users to continuously monitor their health and deliver proper health advice to them. Also, continuous health monitoring depends on the real-time data exchange between health solution providers and users. In this regard, healthcare providers have begun to use wearable devices and recommendation systems to collect data in real time and to manage health conditions based on the generated data. However, we lack literature that has examined how individuals use wearable devices, what type of data the devices collect, and how providers use the data for delivering solutions to users. Thus, we decided to explore the available literature in this domain to understand how wearable devices can provide solutions to consumers. We also extended our focus to cover current health service delivery frameworks with the help of recommender systems. Thus, this study reviews health-monitoring services by conglomerating both wearable device and recommendation system to come up with personalized health and fitness solutions. Additionally, the paper elucidates key components of an advanced-level real-time monitoring service framework to guide future research and practice in this domain