IT Adoption, Diffusion, and Evaluation in Healthcare

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    Identity Change through Affordances Actualization: Evidence from Healthcare Workers
    ( 2023-01-03) Lv, Yangyang ; Liu, Feng ; Kong, Deli ; Qi, Jiayin
    As more and more digital technologies are used in healthcare organizations, the way healthcare workers work and doctor-patient communication are changing. These changes will lead to identity change of healthcare workers. Some scholars try to understand technological changes in terms of the affordance theory. However, there are few relevant studies that incorporate specific application scenarios. In this paper, we explore the specific performance of the digital technology affordance and the impact on healthcare workers’ identity in China. We conducted in-depth interviews with 14 healthcare workers and used grounded theory to summarize three kinds of digital technology affordance, namely functional affordance, process affordance and performance affordance. The findings suggest that on the one hand, digital technology affordance increase the efficiency of healthcare workers and enhance collaboration among colleagues, thus reinforcing the healthcare workers’ identity. On the other hand, over-reliance on digital technology may also lead to unnecessary hassles that worsen healthcare workers’ identity. Our study enriches the affordance theory and identity theory, and has constructive implications for the quality of healthcare services in a digital context.
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    Introduction to the Minitrack on IT Adoption, Diffusion, and Evaluation in Healthcare
    ( 2023-01-03) Spil, Ton ; Kamis, Arnold ; Bozan, Karoly ; Kapoor, Akshat
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    Fitness Technology and Exercise Engagement: How Technology Affordances Facilitate Fitness Goal Attainment
    ( 2023-01-03) Li, Yafang ; Carter, Michelle ; Crossler, Robert
    To realize desired health returns, fitness technology providers, users, and corporate wellness program managers need to understand how individuals’ different uses of fitness technologies influence their fitness experience and fitness goal achievements. Thus, this study draws on the theory of affordances and the concept of engagement to develop and empirically test a model of fitness technology use as goal-directed behavior. Doing so highlights the relationship between trying to use fitness technologies and trying to perform fitness activities with fitness goal attainment. Our results show that while actualized self-appraisal affordance amplifies users’ cognitive exercise engagement, cognitive exercise engagement does not significantly influence fitness goal attainment. Furthermore, actualized self-appraisal and social appraisal affordances enhance users’ emotional exercise engagement, positively influencing fitness goal attainment. Thus, facilitating the actualization of self-appraisal and social appraisal affordances that increase individuals’ emotional exercise engagement is essential to the effective use of fitness technologies.
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    Trust and Willingness to Share Health Related Information: A Quantitative Study of Danish Students’ Use of mHealth Applications
    ( 2023-01-03) Müller, Sune ; Frydensbjerg, Signe ; Overgaard, Emilie
    mHealth technology has the potential to transform healthcare and realize the goal of precision medicine through systematic data collection and use. Meanwhile, mHealth applications developed during COVID-19 have had limited effect, as people have been reluctant to adopt them due to a lack of trust and willingness to share data. The aim of this empirical study is to provide insights into young people’s use, trust, and willingness to share data through mHealth apps as future users of healthcare services. A survey comprising 484 Danish students was conducted. It focuses on mHealth app use, willingness to share data, and trust. The findings show that the trustworthiness of the technology and data requesting organization is important for establishing trust in mHealth apps. These insights indicate how young people can be motivated to trust mHealth apps, which can be used to develop future apps and exploit the untapped potential of the collected data.
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    Humanistic vs. instrumental goals — how mindfulness about goal-conflicts impact IT-related change endeavors in healthcare
    ( 2023-01-03) Weeger, Andy ; Dietz, Alyssa ; Wagner, Heinz-Theo
    Health IT is expected to support both humanistic and instrumental goals, by, e.g., improving both quality and efficiency in healthcare. However, health IT is also triggering or reinforcing conflicts between these goals. These conflicts then often result in failure to achieve intended outcomes such as improved healthcare quality, safety, and efficiency. By proposing an activity theoretical perspective on IT-related change, this study assumes that outcomes of such endeavors are dependent on actors being mindful of these conflicts and how the IS and complementary resources. Analyzing data collected in four IT-related change projects, supports this assumption: data shows that outcome relate to the ability of actors to understand the interplay between humanistic and instrumental goals among the diverse stakeholders. Mindfulness regarding these conflicts is necessary to efficiently develop and implement changes to IS that balance conflicting perspectives and realize expected outcomes. The implications encourage research and practice alike to develop interventions that help to increase understanding and mindfulness of the interplay between IS and the other elements of collective activities in healthcare.
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    Prospective Physicians’ Intention to Adopt Artificial Intelligence: A Configurational Perspective
    ( 2023-01-03) Pare, Guy ; Raymond, Louis ; Castonguay, Alexandre ; Wagner, Gerit
    Artificial intelligence (AI) drives transformation across medical specialities, requiring current and future generations of physicians to navigate ever-changing digital environments. In this context, prospective physicians will play a key role in adopting and applying AI-based health technologies, underlining the importance of understanding their knowledge, attitudes, and intentions toward AI. To dissociate corresponding profiles, we adopted a configurational perspective and conducted a two-stage survey study of 184 (t_0) and 138 (t_1) medical students at a Canadian medical school. Our principal findings corroborate the existence of distinct clusters in respondents’ AI profiles. We refer to these profiles as the AI unfamiliar, the AI educated, and the AI positive, showing that each profile is associated with different intentions towards future AI use. These exploratory insights on the variety of AI profiles in prospective physicians underline the need for targeted and adaptive measures of education and outreach.
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    Does the Electronic Medical Record (EMR) Adoption Matter? Exploring Patterns of EMR Implementation and its Impact on Hospital Performance
    ( 2023-01-03) Lee, Joonghee ; Kim, Jin Sik ; Shin, Soo Il
    We aimed to explore the patterns of electronic medical records (EMR) adoption and its effects on hospital performance. We analyzed hospital-level panel data from 2008 to 2013 using Bayesian regression and the Naïve Bayes model. Our research analysis revealed 38 different adoption patterns for 1,919 hospitals that completed EMR implementation (having all of the four components) and 42 different investment patterns for 1,341 hospitals that could not complete the EMR implementation. We examined the hospitals’ EMR adoption patterns that were not completed; but predicted as completed using the Naïve Bayes model. Our results revealed that the hospitals that completed EMR adoption showed higher performance in terms of patient recommendation and net patient revenue than those that did not complete EMR adoption. More importantly, most of hospitals that observed as “not completed” but predicted as “completed” showed lower performance in terms of patient recommendation as well as net patient revenue.
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    The State of Ehealth Research across Information Technologies: A Longitudinal Analysis Using Topic Modeling to Address Future Scholarship
    ( 2023-01-03) Britt, Rebecca ; Chou, Suyu ; Omah, Ozioma ; Chakraborty, Ananya
    eHealth research has been marked by the last two decades of scholarship spurred by technological advances and the potential of health promotion and behavior change. The study examined the state of eHealth scholarship across social, behavioral and information technologies through a systematic, machine-based learning approach of the last 19 years across 811 articles. The study analyzes topics that were published using latent Dirichlet allocation of studies from 2002 to 2021; it also raises ethical challenges for researchers related to those in prior health initiatives by the CDC and in current scholarship. Results show the common topics, terms, and linguistic attributes within the state of eHealth scholarship and disparities in other areas based on topics published. Suggestions are offered for interdisciplinary collaboration to facilitate the growth and the optimal, practical use of eHealth and directions for the future.
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    Revealing the Root Causes of Digital Health Data Quality Issues: A Qualitative Investigation of the Odigos Framework
    ( 2023-01-03) Eden, Rebekah ; Syed, Rehan ; Makasi, Tendai ; Andrews, Robert ; Ter Hofstede, Arthur ; Wynn, Moe ; Donovan, Raelene ; Eley, Robert ; Staib, Andrew
    Digital health data quality is a critical concern in the healthcare industry, jeopardizing the secondary use of data for revolutionizing population health, and hindering patient care and organizational outcomes. Limited published evidence exists for explaining why these data quality issues emerge. The Odigos framework is a notable exception asserting that data quality issues emerge from three worlds: material world (e.g., technology artifact), personal world (e.g., technology users/use), and social world (e.g., organizations/ institutions) but has yet to systematically unpack the elements within these worlds. Through deductive and inductive analysis of interview data from a case study of the Emergency Department of Australia’s first large digital hospital, we apply and extend the Odigos framework by identifying elements emanating from the three worlds and their interrelationships as root causes of data quality issues. These elements can then be used by hospitals to develop strategies to proactively improve their digital health data quality.