Industry 4.0 and healthcare: Context, applications, benefits and challenges

Abstract

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy.

Fusing talent horizons: the transformative role of data integration in modern talent management

“…In the realm of HRM, data fusion is increasingly lauded for its potential to enhance decision-making efficiency and support sustainability initiatives. Scholars highlight that integrating diverse data sources introduces a spectrum of complex challenges spanning technical, legal, organizational, and ethical dimensions [106][107][108][109][110][111][112][113][114][115], as thoroughly detailed in Table 5. Among these challenges, special attention must be given to the potential for algorithmic biases.…”

“…Furthermore, the ethical processing of data, a critical aspect underscored by Wijesekara and Gunawardena [114] and Doherty et al [115], is fundamental to ensuring equity and fairness in HR decision-making. In this context, safeguarding against data breaches becomes paramount, as security lapses can compromise sensitive employee information, undermining trust and organizational integrity [106]. This involves not only adhering to legal compliance but also fostering adaptable organizational standards responsive to evolving ethical considerations [108].…”

“…To effectively navigate and address these challenges, adopting a comprehensive approach to data fusion, as delineated in Table 6, is imperative. This approach should encompass robust data protection measures [106] and actively promote collaborative stakeholder engagement [109], thereby cultivating an organizational culture that steadfastly upholds legal and ethical standards. The inclusion of regular audits and transparency in algorithmic processes is vital to address these complex challenges, including mitigating biases and ensuring data privacy [95].…”