Digital Disruption and Big Data in Healthcare - Opportunities and Challenges

Factors influencing the use of big data within healthcare services: a systematic review

Background: The emergence of big data holds the promise of aiding healthcare providers by identifying patterns and converting vast quantities of data into actionable insights facilitating the provision of precision medicine and decision-making. Objective: This study aimed to investigate the factors influencing use of big data within healthcare services to facilitate their use. Method: A systematic review was conducted in February 2024, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searches for articles published between 01 January 2020 and 18 February 2024 and included PubMed, Scopus, ProQuest and Cochrane Library. The Authority, Accuracy, Coverage, Objectivity, Date, Significance ( AACODS) checklist was used to evaluate the quality of the included articles. Subsequently, a thematic analysis was conducted on the findings of the review, using the Boyatzis approach. Results: A final selection of 46 studies were included in this systematic review. A significant proportion of these studies demonstrated acceptable quality, and the level of bias was deemed satisfactory. Thematic analysis identified seven major themes that influenced the use of big data in healthcare services. These themes were grouped into four primary categories: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors associated with "effort expectancy" were the most highly cited in the included studies (67%), while those related to "social influence" received the fewest citations (15%). Conclusion: This study underscored the critical role of "effort expectancy" factors, particularly those under the theme of "data complexity and management," in the process of using big data in healthcare services. Implications: Results of this study provide groundwork for future research to explore facilitators and barriers to using big data in health care, particularly in relation to data complexity and the efficient and effective management of big data, with significant implications for healthcare administrators and policymakers.

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Introduction

Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.

Objective

This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS.

Method

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.

Results

This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed.

Discussions

Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements.

Conclusion

The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

Enterprise marketing strategy using big data mining technology combined with XGBoost model in the new economic era

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

Impact of Using the Internet of Medical Things on e-Healthcare Performance: Blockchain Assist in Improving Smart Contract

Background

This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance.

Methods

A quantitative approach used an online survey from public and private hospital administrative departments in Dubai, United Arab Emirates (UAE). ANOVA, t-test, correlation, and regression analysis were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).

Patients and Methods

A mixed method was used in this research, a quantitative approach for data analysis utilizing online surveys from public and private hospitals' administrative departments in Dubai, UAE. A correlation, regression through ANOVA, and independent two-sample t-test were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).

Results

Blockchain application in smart contracts has proven to be significant in the healthcare sector. Results highlight the importance of integrating smart contracts and blockchain technology in the IoMT infrastructure to improve efficiency, transparency, and security. The study provides empirical evidence to support the implementation of smart contracts in the e-healthcare sector and suggests improved e-healthcare performance through this transition.

Conclusion

The emergence of e-healthcare systems with upgraded smart contracts and blockchain technology brings continuous health monitoring, time-effective operations, and cost-effectiveness to the healthcare sector.

Health Information System and Health Care Applications Performance in the Healthcare Arena: A Bibliometric Analysis

There have been several studies centred on health information systems with many insights provided to enhance health care applications globally. These studies have provided theoretical schemes for fortifying the enactment and utilisation of the Health Information System (HIS). In addition, these research studies contribute greatly to the development of HIS in alignment with major stakeholders such as health practitioners and recipients of health care. Conversely, there has been trepidation about HIS' sustainability and resilience for healthcare applications in the era of digitalization and globalization. Hence, this paper investigates research on HIS with a primary focus on health care applications to ascertain its sustainability and resilience amidst the transformation of the global healthcare space. Therefore, using a bibliometric approach, this paper measures the performance of health information systems and healthcare for health care applications using bibliometric data from the web of science database. The findings reveal solid evidence of the constructive transformation of health information systems and health care applications in the healthcare arena, providing ample evidence of the adaptation of HIS and health care applications within the healthcare arena to the fourth industrial revolution and, additionally, revealing the resilient alignment of health care applications and health information systems.