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

Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study

BACKGROUND

Linkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis.

METHODS

The difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as databaseIII (DBIII) and databaseDII (DBDII), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed.

RESULTS

The linkage rates for DBDII and DBIII were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was "none" to "very little." With respect to cervical cancer that had a relatively small sample size, analysis of DBIII resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DBIII vs. 1.80 [95% CI: 1.18-2.73] in DBDII). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DBIII vs. 1.92 [95% CI: 1.70-2.17] in DBDII for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DBIII vs. 2.05 [95% CI: 1.67-2.52] in DBDII for the regional stage).

CONCLUSIONS

To prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DBDII. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.

Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT)

AI is a contemporary methodology rooted in the field of computer science [...].

Rex: R-linked EXcel add-in for statistical analysis of medical and bioinformatics data

BACKGROUND

Microsoft Excel has substantial functionalities for data management and analyses, and has been the most popular software in this field. However, in spite of Excel's user-friendly interface and functionality for data management, it provides very few functions for in-depth statistical analyses, which has limited its wider application for this purpose.

OBJECTIVE

Here, we introduce Rex, an Excel add-in software implementing the powerful analytical and graphical functions of R within Excel.

METHODS

Rex was implemented using three types of programming software: R, JavaScript, and Microsoft VB.Net.

RESULTS

Rex provides a graphical user interface (GUI) through Excel, and statistical analysis can be conducted by pointing and clicking the menu without programming R. Rex covers a wide range of analyses from basic statistics to advanced analysis, including structural equation modeling, complex sampling design, and machine learning models, making it possible for researchers not skilled in using a command-line interface to conduct in-depth statistical analyses. Most Rex modules are available in a free version for non-commercial use, and it can be used for educational and public purposes.

CONCLUSION

In this article, we introduce the framework and features of Rex with illustrative examples of its implementation.

Measuring circularity of a manufacturing organization by using sustainable balanced scorecard

In recent years, circular economy has become a matter of great importance because of its ability to contribute toward economic, environmental, and social aspects of the sustainability. The circular economy approaches help in resource conservation by reducing, reusing, and recycling products/parts/components/materials. On the other hand, Industry 4.0 is coupled with emerging technologies, which support the firms in efficient resource utilization. These innovative technologies can transform the present manufacturing organizations by reducing resource extraction, CO2 emissions, environmental damage, and power consumption and improve it into a more sustainable manufacturing organization. Industry 4.0 along with circular economy concepts greatly improves the circularity performance. However, there is no framework found for measuring the circularity performance of the firm. Therefore, the current study aims to develop a framework for measuring performance in terms of circularity percentage. In this work, graph theory and matrix approach are employed for measuring the performance based on a sustainable balanced scorecard such as internal process, learning and growth, customer and financial with environmental and social perspectives. A case of an Indian barrel manufacturing organization is discussed for the illustration of proposed methodology. Based on "circularity index" of the organization and the maximum possible circularity index, the circularity was found to be 5.10%. It indicates that there is a huge potential for the improvement in the circularity of the organization. An in-depth sensitivity analysis and comparison are also performed to validate the findings. There are very few studies on measuring the circularity. The study developed the approach for measuring circularity, which may be utilized by industrialists and practitioners for improving the circularity.

Analysis of critical success factors for successful integration of lean six sigma and Industry 4.0 for organizational excellence

Purpose

Manufacturing industries are facing dynamic challenges in today’s highly competitive world. In the recent past, integrating Industry 4.0 with the lean six sigma improvement methodologies has emerged as a popular approach for organizational excellence. The research aims to explore and analyze critical success factors of lean six sigma integrated Industry 4.0 (LSSI).

Design/methodology/approach

This research study explores and analyzes the critical success factors (CSFs) of LSSI. A three-phase study framework is employed. At first, the CSFs are identified through an extensive literature review and validated through experts’ feedback. Then, in the second phase, the initial list of CSFs is finalized using the fuzzy DELPHI technique. In the third phase, the cause-effect relationship among CFSs is established using the fuzzy DEMATEL technique.

Findings

A dyadic relationship among cause-and-effect category CSFs is established. Under the cause category, top management commitment toward integrating LSSI, systematic methodology for LSSI and organizational culture for adopting changes while adopting LSSI are found to be topmost CSFs. Also, under the effect category, organizational readiness toward LSSI and adaptability and agility are found to be the uppermost CSFs.

Practical implications

The study offers a framework to understand the significant CSFs for LSSI implementation. Insights from the study will help industry managers and practitioners to implement LSSI and achieve organizational excellence.

Originality/value

To the best of the authors’ knowledge, CSFs of LSSI are not much explored in the past by researchers. Findings will be of great value for professionals in developing long-term operations strategies.

Towards the Use of Big Data in Healthcare: A Literature Review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.