Track 2

Health and wellbeing, two of the United Nations’ sustainable development goals, suffered serious adverse impacts from the COVID-19 pandemic. With the rapid pivot to digital interactions precipitated by the global crisis, it became increasingly clear that the wellbeing of individuals and nations is tightly interwoven with their capacity to use digital technologies. For example, digital tools such as contact tracing apps (Pandl et al. 2021; Trang et al. 2020) diffused in many areas of the world helping some countries to slowly return to a form of life resembling pre-pandemic times. Likewise, digitally generated and managed data became central for policies to respond to COVID-19 in many countries. Furthermore, digital health data provided the critical fuel for rapid innovations in diagnostics, treatments, vaccination, and clinical processes. Even though policy makers and practitioners alike had recognized the criticality of digital health for wellbeing before COVID-19, the crisis has amplified the importance of digital health data and tools to improve the safety, efficiency, quality, and accessibility of healthcare. In short the pursuit of novel digital data objects (Faulkner and Runde 2019) has became central for effective interventions into the pandemic within and beyond core healthcare applications.

Our aim in this track is to both take stock of and advance our knowledge of digital health. Traditionally, literature related to digital health has explored implementation of information systems in hospitals (Baird et al. 2018) and the role of electronic medical records in transforming health care (Agarwal et al. 2010; Hansen and James Baroody 2020; Oborn et al. 2011). Beyond hospital delivery settings, research is increasingly also focusing on a more consumer-centric view of digital health (Agarwal et al. 2020). More recently, dynamics linked to platforms (Barrett et al. 2016; Fürstenau et al. 2021), technologies such as robotics (Barrett et al. 2012), self-management of health by patients (Dadgar and Joshi 2018; Wessel et al. 2019), and the role of increasing amounts of data for innovating in health care services have been brought to the fore (Jarvenpaa and Markus 2018; Rothe et al. 2019; Thiebes et al. 2020; Vassilakopoulou et al. 2018). The increased digitization of health data and developments in analytics and artificial intelligence have unleashed rapid innovation in a wide variety of health care application domains. As healthcare delivery shifts from traditional care delivery settings such as hospitals to the “home,”and as services are increasingly utilized through digital connections and channels such as telemedicine, the demands for coordination and standards are increasing. This dynamic transformation that healthcare systems all over the world are experiencing highlights the need for additional research that is able to provide evidence-based insights into the design, deployment, and outcomes associated with tools for digital health delivery.

This track seeks research that sheds new light on the use of digital data and technologies in healthcare practice and delivery. We welcome all submissions with the potential to contribute to our understanding of the relationship between digital data objects, digital tools and their potentially transformational impacts. Papers may be focused on original design-oriented work, theory-oriented research, empirical studies, or conceptual. We are agnostic in terms of methodologies applied. Submissions may address the following topics, but are not limited to them:

• The role and management of digital health data for digital innovation
• The influence of digital health tools and data objects in- and outside of healthcare, e.g., in times of crisis
• A process perspective on digital transformation in healthcare
• The role of traditional professionals’ roles, identities, and institutions for value creation in health
• Design of digital innovations for improving patients’ self-management of chronic conditions
• The role of data for promoting prevention that challenges a disease-based health care system
• Explorations of novel revenue models, operational models, and business models in the context of digital innovation in health care
• Measuring patient-centric value and improving public health care through datafication strategies
• Usage of patient-centric information systems, sensor and wearables technologies, and digital health apps and their implications for patient value creation
• Supporting elderly people and people in need for care through digital technologies
• The role of XR technologies in creating health data and value from health data
• The role of virtual coaching in enabling patients to take care of their own health
• New modes of capturing value from digital health data, e.g., reimbursement strategies
• Comparisons between outcomes of data-driven strategies to improve health care delivery
• Ethics, privacy and security in the era of digital health
• Design and evaluations of emerging technologies (e.g., AI, Blockchain, Wearables) for creating value in health care

References:

• Agarwal, R., Dugas, M., Gao, G. (Gordon), and Kannan, P. K. 2020. “Emerging Technologies and Analytics for a New Era of Value-Centered Marketing in Healthcare,” Journal of the Academy of Marketing Science (48:1). (https://doi.org/10.1007/s11747-019-00692-4).
• Agarwal, R., Gao, G., DesRoches, C., and Jha, A. K. 2010. “Research Commentary—The Digital Transformation of Healthcare: Current Status and the Road Ahead,” Information Systems Research (21:4), INFORMS, pp. 796–809. (https://doi.org/10.1287/isre.1100.0327).
• Baird, A., Angst, C. M., and Oborn, E. 2018. “Research Curation: Health Information Technology,” MIS Ouarterly.
• Barrett, M., Oborn, E., and Orlikowski, W. 2016. “Creating Value in Online Communities: The Sociomaterial Configuring of Strategy, Platform, and Stakeholder Engagement,” Information Systems Research (27:4), pp. 704–723. (https://doi.org/10.1287/isre.2016.0648).
• Barrett, M., Oborn, E., Orlikowski, W. J., and Yates, J. 2012. “Reconfiguring Boundary Relations: Robotic Innovations in Pharmacy Work,” Organization Science (23:5), pp. 1448–1466. (https://doi.org/doi:10.1287/orsc.1100.0639).
• Dadgar, M., and Joshi, K. D. 2018. “The Role of Information and Communication Technology in Self-Management of Chronic Diseases: An Empirical Investigation through Value Sensitive Design,” Journal of the Association for Information Systems (19:2), Association for Information Systems, pp. 86–112. (https://doi.org/10.17705/1jais.00485).
• Faulkner, P., and Runde, J. 2019. “Theorizing the Digital Object.,” MIS Quarterly (43:4).
• Fürstenau, D., Klein, S., Vogel, A., and Auschra, C. 2021. “Multi-Sided Platform and Data-Driven Care Research: A Longitudinal Case Study on Business Model Innovation for Improving Care in Complex Neurological Diseases,” Electronic Markets. (https://doi.org/10.1007/s12525-021-00461-8).
• Hansen, S., and James Baroody, A. 2020. “Electronic Health Records and the Logics of Care: Complementarity and Conflict in the U.S. Healthcare System,” Information Systems Research (31:1), pp. 57–75. (https://doi.org/10.1287/ISRE.2019.0875).
• Jarvenpaa, S. L., and Markus, M. L. 2018. “Data Perspective in Digital Platforms: Three Tales of Genetic Platforms,” Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 4574–4583.
• Oborn, E., Barrett, M., and Davidson, E. 2011. “Unity in Diversity: Electronic Patient Record Use in Multidisciplinary Practice,” Information Systems Research (22:3), pp. 547–564. (https://doi.org/doi:10.1287/isre.1110.0372).
• Pandl, K. D., Thiebes, S., Schmidt-Kraepelin, M., and Sunyaev, A. 2021. “How Detection Ranges and Usage Stops Impact Digital Contact Tracing Effectiveness for COVID-19,” Scientific Reports, pp. 11, 9414. (https://doi.org/10.1101/2020.12.08.20246140).
• Rothe, H., Jarvenpaa, S. L., and Penninger, A. A. 2019. “How Do Entrepreneurial Firms Appropriate Value in Bio Data Infrastructures: An Exploratory Qualitative Study,” in Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden.
• Thiebes, S., Toussaint, P. A., Ju, J., Ahn, J. H., Lyytinen, K., and Sunyaev, A. 2020. “Valuable Genomes: Taxonomy and Archetypes of Business Models in Direct-to-Consumer Genetic Testing,” Journal of Medical Internet Research (22:1). (https://doi.org/10.2196/14890).
• Trang, S., Trenz, M., Weiger, W. H., Tarafdar, M., and Cheung, C. M. K. 2020. “One App to Trace Them All? Examining App Specifications for Mass Acceptance of Contact-Tracing Apps,” European Journal of Information Systems (29:4). (https://doi.org/10.1080/0960085X.2020.1784046).
• Vassilakopoulou, P., Skorve, E., and Aanestad, M. 2018. “Enabling Openness of Valuable Information Resources: Curbing Data Subtractability and Exclusion,” Information Systems Journal, Wiley Online Library.
• Wessel, L., Davidson, E. J., Barquet, A. P., Rothe, H., Peters, O., and Megges, H. 2019. “Configuration in Smart Service Systems: A Practice-Based Inquiry,” Information Systems Journal (29:6), pp. 1256–1281. (https://doi.org/10.1111/isj.12268).