scholarly journals Quality of Big Data in health care

2015 ◽  
Vol 28 (6) ◽  
pp. 621-634 ◽  
Author(s):  
Sreenivas R. Sukumar ◽  
Ramachandran Natarajan ◽  
Regina K. Ferrell
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Quality ◽  
Health Data ◽  
Data Sets ◽  
Processing Technologies ◽  
Content Type ◽  
Road Map ◽  
Quality Issues ◽  
Health Care Analytics

Purpose – The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. Design/methodology/approach – The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Findings – Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. Practical implications – The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Originality/value – Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

scholarly journals Thinking about police data: Analysts’ perceptions of data quality in Canadian policing

2021 ◽  
pp. 0032258X2110214
Author(s):  
Christopher D O’Connor ◽  
John Ng ◽  
Dallas Hill ◽  
Tyler Frederick
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Quality ◽  
Research Culture ◽  
Police Services ◽  
Police Data ◽  
Data Collection And Analysis ◽  
Quality Issues

Policing is increasingly being shaped by data collection and analysis. However, we still know little about the quality of the data police services acquire and utilize. Drawing on a survey of analysts from across Canada, this article examines several data collection, analysis, and quality issues. We argue that as we move towards an era of big data policing it is imperative that police services pay more attention to the quality of the data they collect. We conclude by discussing the implications of ignoring data quality issues and the need to develop a more robust research culture in policing.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Improving Data Quality in Health Care

2010 ◽  
pp. 1877-1881
Author(s):  
Karolyn Kerr ◽  
Tony Norris
Keyword(s):  
Health Care ◽  
New Zealand ◽  
Data Quality ◽  
Good Practice ◽  
Health Data ◽  
Strategic Approach ◽  
The Right ◽  
Data Collections ◽  
And Control ◽  
Care Demands

The increasingly information intensive nature of health care demands a proactive and strategic approach to data quality to ensure the right information is available to the right person at the right time in the right format. The approach must also encompass the rights of the patient to have their health data protected and used in an ethical way. This article describes the principles to establish good practice and overcome practical barriers that define and control data quality in health data collections and the mechanisms and frameworks that can be developed to achieve and sustain quality. The experience of a national health data quality project in New Zealand is used to illustrate the issues.

Real Time Analysis Based on Intelligent Applications of Big Data and IoT in Smart Health Care Systems

2020 ◽  
pp. 1839-1857
Author(s):  
Mamata Rath
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Treatment Plan ◽  
Big Data Analytics ◽  
Data Sets ◽  
Care Services ◽  
Real Time Analysis ◽  
Health Care Applications ◽  
Care Systems

Currently, there is an expanding interest for additional medical data from patients about their healthcare choices and related decisions, and they further need investment in their basic health issues. Big data provides patients presumptuous data to help them settle on the best choice and align with their medicinal treatment plan. One of the very advanced concepts related to the synthesis of big data sets to reveal the hidden pattern in them is big data analytics. It involves demanding techniques to mine and extract relevant data that includes the actions of piercing a database, effectively mine the data, query and inspect the data and is committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage data that can influence the business. In this way, the primary goal of big data analytics is to help business relationships to have enhanced comprehension of data, and subsequently, settle on proficient and very much educated decisions. Big data analytics empowers data diggers and researchers to examine an extensive volume of data that may not be outfit utilizing customary apparatuses. Big data analytics require advances and statistical instruments that can change a lot of organized, unstructured, and semi-organized data into more reasonable data and metadata designed for explanatory procedures. There is tremendous positive potential concerning the application of big data in human health care services and many related major applications are still in their developmental stages. The deployment of big data in health service demonstrates enhancing health care results and controlling the expenses of common people due to treatment, as proven by some developing use cases. Keeping in view such powerful processing capacity of big data analytics in various technical fields of modern civilization related to health care, the current research article presents a comprehensive study and investigation on big data analytics and its application in multiple sectors of society with significance in health care applications.

Improving Data Quality in Health Care

2011 ◽  
pp. 218-225 ◽  
Author(s):  
Karolyn Kerr ◽  
Tony Norris
Keyword(s):  
Health Care ◽  
New Zealand ◽  
Data Quality ◽  
Good Practice ◽  
Health Data ◽  
Strategic Approach ◽  
The Right ◽  
Data Collections ◽  
And Control ◽  
Care Demands

The increasingly information intensive nature of health care demands a proactive and strategic approach to data quality to ensure the right information is available to the right person at the right time in the right format. The approach must also encompass the rights of the patient to have their health data protected and used in an ethical way. This article describes the principles to establish good practice and overcome practical barriers that define and control data quality in health data collections and the mechanisms and frameworks that can be developed to achieve and sustain quality. The experience of a national health data quality project in New Zealand is used to illustrate the issues.

scholarly journals Big data in the Danish industry: application and value creation

2017 ◽  
Vol 23 (3) ◽  
pp. 645-670 ◽  
Author(s):  
Sune Dueholm Müller ◽  
Preben Jensen
Keyword(s):  
Big Data ◽  
Value Creation ◽  
Online Survey ◽  
Organizational Context ◽  
Business Processes ◽  
Research Question ◽  
Business Strategies ◽  
Processing Technologies ◽  
Content Type ◽  
Analytical Skills

Purpose The development within storage and processing technologies combined with the growing collection of data has created opportunities for companies to create value through the application of big data. The purpose of this paper is to focus on how small and medium-sized companies in Denmark are using big data to create value. Design/methodology/approach The research is based on a literature review and on data collected from 457 Danish companies through an online survey. The paper looks at big data from the perspective of SMEs in order to answer the following research question: to what extent does the application of big data create value for small and medium-sized companies. Findings The findings show clear links between the application of big data and value creation. The analysis also shows that the value created through big data does not arise from data or technology alone but is dependent on the organizational context and managerial action. A holistic perspective on big data is advocated, not only focusing on the capture, storage, and analysis of data, but also leadership through goal setting and alignment of business strategies and goals, IT capabilities, and analytical skills. Managers are advised to communicate the business value of big data, adapt business processes to data-driven business opportunities, and in general act on the basis of data. Originality/value The paper provides researchers and practitioners with empirically based insights into how the application of big data creates value for SMEs.

scholarly journals Big data analysis for studying spatiotemporal trends in the sustainable development of large cities

2021 ◽  
Vol 258 ◽  
pp. 01006
Author(s):  
Kseniya Mulyukova ◽  
Viktor Kurejchik
Keyword(s):  
Big Data ◽  
Real Estate ◽  
Large City ◽  
Graphical Form ◽  
Primary Study ◽  
Practical Implementation ◽  
Data Sets ◽  
Mathematical Basis ◽  
Processing Technologies ◽  
Spatiotemporal Trends

The article covers the analysis of big data in urban planning. The purpose of this work is to study modern problems of processing big data containing information about real estate objects and prospects for solving these problems, as well as the possibility of practical implementation of the methodology for processing such data sets by designing and filling a special graphic abstraction “metahouse” using a practical example. The relevance of the study lies in identifying a number of advantages in the presentation of data in graphical form. The mathematical basis of the technique is the use of multidimensional spaces, where measurements are the characteristics of individual objects. In the course of the work, the specifics of big data sets, consisting of information about real estate in a large city, were described. methods of effective solution of the set practical problem of processing and searching for patterns in a large data array were proposed: abstraction “metahouse”, data aggregator. In the course of the study, it was revealed that the presentation of groups of the obtained data in a graphical form has a number of advantages over the tabular presentation of data. The obtained results can be used both for the primary study of big data processing technologies, and as a basis for the development of real applications in the following areas: analysis of changes in the area of houses over time, analysis of changes in the number of storeys in urban development, etc.

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