Zhijun Yan - School of Management and Economics, Beijing Institute of Technology, China
Roberta Bernardi - School of Economics, Finance and Management, University of Bristol, UK
Nina (Ni) Huang - W.P. Carey School of Business, Arizona State University, US
Younghoon Chang - School of Management and Economics, Beijing Institute of Technology, China
Overview of Special Issue
Digital technology has been transforming how individuals, organizations, and societies use information to improve their decision making on their daily lives and daily operations. In recent years, the healthcare industry has also actively engaged in the adoption of digital technology and enabled the formation of digital health. The digital health covers lots of advanced technologies, such as mobile health (mHealth), health information technology (HIT), wearable devices, telehealth and telemedicine, health data analytics and personalized medicine (Lupton 2018). These technologies offer new exciting opportunities to improve medical outcomes, enhance efficiency and balance health resources.
In particular, digital health can better collect, process and analyze health-related information, and provide decision support for patients, doctors, healthcare organizations, public health management and medical research (Guha and Kumar 2018). There are many positive and negative issues associated with the use of digital health by these stakeholders. On the one hand, digital health can empower patients to make better decisions about their own health and provide new options for facilitating prevention, early diagnosis, surveillance, management and prediction of chronic conditions outside traditional healthcare settings (Lin et al. 2017). Doctors can also get a more holistic view of patient health through access to data and improve quality of care (Lin et al. 2019). Pharmaceutical companies and digital health companies can also benefit from patient-generated knowledge for the advancement of medical research (Kallinikos and Tempini 2014) and the design of personalized healthcare interventions (Bernardi 2019). On the other hand, the integration of digital technology in the healthcare industry presents risks such as the spread of misinformation (e.g. anti-vax communities, Doty 2015), the disclosure of patients' privacy that could be used by health insurance companies to make discriminatory pricing (McFall and Moor 2018), increased doctors' technical anxiety and slow acceptance of digital health innovation (Bernardi and Exworthy 2019), and health inequalities due to the digital exclusion of patients (Latulippe et al. 2017; Halford and Savage 2010).
The healthcare industry is one of the largest and also one of the most important industries for citizens’ wellbeing. Addressing the complexities of today’s various negative and positive healthcare issues requires more than one perspective and needs more interdisciplinary collaboration and research. The rapid development of advanced technologies and methodologies such as social media, Internet of things (IoT) data analytics, machine learning, artificial intelligence (AI) brings lots of opportunities to handle the complicated problems in the healthcare industry. It makes it possible to improve people’s health conditions smartly and comfortably. However, the adoption of digital technology in health care usually lags behind other industries, as some major technological and managerial obstacles still remain (Bunduchi et al. 2015). Obstacles include the lack of health data integration, data overload issues, data privacy and security, and limited or inefficient data visualization (Agarwal et al. 2010). At the same time, academics need to address the issues related to the dark side and potential risks of digital health. This special issue aims to serve as a forum in which healthcare, computer science, management and social science scholars can come together to discuss new emerging issues related to the bright side and the dark side of digital health. It invites submissions from a variety of methodological, theoretical, and multidisciplinary perspectives. Theoretical work that engages critically with the debate about the bright and dark sides of digital health is also welcome. In bringing technical, behavioral, clinical, and managerial perspectives together, this special issue hopes to generate new insights into the design, adoption, utilization, and management of digital health as well as an understanding of its risks and adverse consequences for individuals, organizations, and societies.
Topics of interest include, but are not limited to:
• Participating behavior of digital health
• Knowledge sharing and knowledge seeking of online health communities
• Knowledge discovery and decision support based on online health communities and clinical decision making systems
• Social and economic return of digital health
• Data privacy, trust and security in digital health
• Fake information and information fraud in online health communities
• Online-offline data integration and analytics
• Organizational, operational, clinical and financial implications of digital health
• Health, social and economic impact of digital health
• Big data analytics and artificial intelligence application
• Theories, models and classification frameworks that shed light on the bright side and dark side of digital health
• Methods for studying the bright side and dark side of digital health and its impact on individuals, communities (societies) and organizations
• Understanding how individuals, communities and organizations can minimize, prevent or respond to the dark side of digital health
• Understanding what motivates individuals, communities and organizations to deliberately engage in digital health
• Examining the dark side (outcomes, behaviors and practices) that accidently or unintentionally emerge in digital health
• The ethics of the dark sides of digital health (especially with recent AI developments and uses in digital health)
• Region, sector and industry-focused studies on the bright side and dark side of digital health
• Economic impact of digital health on the healthcare industry
- Submission due: 2020, November 1
- 1st round review decision: 2021, January 30
- Revised submission due: 2021, February 28
- 2nd round final review decision 2021, March 31
- Publication: 2021
Editorial Review Board
Spyros Angelopoulos - Tilburg University, Netherlands
Petros Chamakiotis - ESCP Business School (Madrid), Spain
Ben Choi - Nanyang Technological University, Singapore
Qianzhou Du - Nanjing University, China
Juyeon Ham - Beijing Institute of Technology, China
Kevin Yili Hong - Arizona State University, USA
Liqiang Huang - Zhejiang University, China
Yi-cheng Ku - Fu Jen Catholic University, Taiwan
One-Ki Daniel Lee - University of Massachusetts Boston, USA
Weizi Li - University of Reading, UK
Christian Libaque-Saenz - Universidad del Pacífico, Peru
Benjamin Marent - University of Sussex, UK
Yang Pan - Louisiana State University, USA
Jae Hyun Park - Kyoto Institute of Technology, Japan.
Dimitra Petrakaki - University of Sussex, UK
Niccolò Tempini - University of Exeter, UK
Yichuan Wang - University of Sheffield, UK
Siew Fan Wong - Sunway University, Malaysia
Jiayin Zhang - Tsinghua University, China
Minhao Zhang - University of Bristol, UK
Xiaofei Zhang - Nankai University, China
Kang Zhao - University of Iowa, USA
Yuxiang Zhao - Nanjing University of Science & Technology, China
Agarwal, R., Gao, Guodong (Gordon), DesRoches, C., Jha, A. K. 2010. “The Digital Transformation of Healthcare: Current Status and the Road Ahead.” Information Systems Research, (21:4), pp. 796-809.
Bernardi, R. 2019. “Online Health Communities as Social Spaces for Experimentation: Individual and Collective Epistemic Practices of Knowledge Co-production.” OKLC Conference, Brighton, 24-26 April 2019.
Bernardi, R., and Exworthy, M. 2019. "Clinical Managers' Identity at the Crossroad of Multiple Institutional Logics in IT Innovation: The Case Study of a Health Care Organization in England." Information Systems Journal, pp. 1– 30.
Bunduchi, R., Smart, A., Charles, K., McKee, L., and Azura-Blanco, A. 2015. "When innovation fails: An institutional perspective of the (non)adoption of boundary spanning IT innovation." Information & Management (52:5), pp. 563-576.
Doty, C. 2015. “Social epistemology and cognitive authority in online comments about vaccine safety.” iConference 2015 Proceedings.
Gianchandani, E. P. 2011. "Toward smarter health and well-being: an implicit role for networking and information technology." Journal of Information Technology (26:2), pp. 120-128.
Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., and Donaldson, L. 2013. "Harnessing the cloud of patient experience: using social media to detect poor quality healthcare." BMJ Quality & Safety (22:3), pp. 251-255.
Guha S. and Kumar S. 2018. “Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap.” Production & Operations Management 27(9): 1724-1735.
Halford, S., Savage, M. 2010. “Reconceptualizing digital social inequality.” Information, Communication & Society, 13 (7), 937–955.
Kallinikos, J., and Tempini, N. 2014. “Patient data as medical facts: Social media practices as a foundation for medical knowledge creation.” Information Systems Research 25(4) 817-833.
Latulippe K., Hamel C., Giroux D. 2017. “Social Health Inequalities and eHealth: A Literature Review With Qualitative Synthesis of Theoretical and Empirical Studies.” Journal of Medical Internet Research, 19(4):e136.
Lin Y. K., Chen H. C., Brown R. A., Li S. H. and Yang H. J. 2017. “Healthcare Predictive Analytics for Risk Profiling in Chronic Care: A Bayesian Multitask Learning Approach.” MIS Quarterly 41(2): 473-A473.
Lin Y. K., Lin M. F. and Chen H. C. 2019. “Do Electronic Health Records Affect Quality of Care? Evidence from the Hitech Act.” Information Systems Research 30(1): 306-318.
Lupton, D. 2018. Digital Health: Critical and Cross-Disciplinary Perspectives, Oxon-New York: Routledge.
McFall, L., Moor, L. 2018. “Who, or what, is insurtech personalizing?: persons, prices and the historical classifications of risk.” Distinktion: Journal of Social Theory, 19(2): 193-213.