Introduction Artificial intelligence (AI) is transforming healthcare through enhanced computational capabilities that process vast amounts of data with unprecedented speed and precision. AI-powered administrative systems hold significant promise for reducing the substantial documentation burden on healthcare professionals, potentially improving operational efficiency and job satisfaction. However, critical knowledge gaps exist regarding how AI implementation affects professional boundaries, work identities and the healthcare work environment. This study protocol describes the Project EFAAI (Efficient and Flexible Working Life in Healthcare with the Support of Digital Tools and AI), which aims to explore how the implementation of AI-based automation of administrative tasks affects healthcare professionals' work.Methods and analysis This research employs a single-embedded case study with a longitudinal approach, following the implementation of an AI-powered healthcare administration system across 12 primary care facilities and 20 specialist care facilities of a major private healthcare provider in Sweden. Data collection spans 18 months through three sequential waves at 6-month intervals. The project consists of three work packages (WP): WP1 explores changes in professional boundaries and AI literacy requirements using exploratory meetings with healthcare leaders and in-depth semistructured interviews with 20 physicians from primary care and 20 from specialist care (n=40); WP2 investigates implementation barriers and facilitators through in-depth semistructured interviews with the same 40 physicians as WP1 for longitudinal follow-up; and WP3 examines the impact on efficient and flexible work conditions using a mixed-methods approach integrating quantitative operational and administrative data from the healthcare provider's information systems and administrative databases (eg, documentation completion times, consultation duration, workload distribution metrics, operational efficiency ratios) with qualitative data from four focus groups and workshops with physicians, nurses, healthcare leaders and administrative staff.Ethics and dissemination The study adheres to the ethical guidelines of the Swedish Research Council and complies with the regulations of the Swedish Ethical Review Authority. Data will be collected with informed consent from all participants, anonymised and stored in accordance with General Data Protection Regulation (GDPR) requirements. Results will be disseminated through international publications, conferences, teaching activities and stakeholder engagement. © Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY. Published by BMJ Group.

Background: The rapid emergence of artificial intelligence (AI) has outpaced its formal adoption in health care organizations, contributing to the emergence of Shadow AI, defined here as the use of unauthorized AI tools by medical professionals. Under the European Union Medical Device Regulation, AI tools used for clinical purposes must undergo conformity assessment before use; general-purpose tools such as ChatGPT have not done so, rendering their clinical application unauthorized at the regulatory level. While Shadow AI offers potential efficiency gains and higher performance, it poses significant risks to data privacy, clinical safety, and regulatory compliance. Despite its growing prevalence, empirical research on the purposes for which physicians use Shadow AI remains scarce. Objective: This study explores the purposes for which physicians describe using Shadow AI in their work. Methods: We conducted a cross-sectional survey of physicians employed in Swedish health care organizations (N=357; response rate~64%). Data were collected between December 2023 and January 2024 via a verified online panel. We conducted a qualitative content analysis of free-text responses on the use of unauthorized AI tools. We applied theoretical lenses from the sociology of professions and paradox theory to interpret the empirical findings. Results: Physicians use Shadow AI for several purposes, which we grouped into 4 categories: clinical work and decision-making, administrative work, research and professional development, and technological interest and curiosity. More specifically, Shadow AI is used as a colleague and second opinion for clinical decision support (eg, differential diagnoses and rare cases), administrative tasks such as patient communication and documentation, and research aimed at staying up to date and exploring developments in generative AI. Physicians described using these tools compensated for perceived gaps in institutional systems, reducing workload, and accessing knowledge considered difficult to obtain through conventional channels. The findings reveal a tension between physicians’ drive to improve their practice and the regulatory and organizational constraints that render such use unauthorized. Conclusions: Shadow AI used by physicians presents both opportunities and risks for health care professionals and organizations. Shadow AI indicates gaps where formal hospital systems may fail to meet health care professionals’ needs and signals a way for physicians to strengthen their experience-based knowledge. It represents a renegotiation of professional boundaries, as physicians bypass institutional constraints to maintain professional efficacy. The findings highlight a paradox in which the same tools that pose regulatory and safety risks also address real gaps in clinical and administrative support, suggesting that governance approaches must account for this tension rather than relying on prohibition alone. © Lena Petersson, Luís Irgang, Ingela Mauritzon, Magnus Holmén.

Artificial intelligence (AI) is revolutionizing healthcare by introducing novel treatments and applications, thereby transforming the sector. However, the complexity, ambiguity, and inherent risks associated with AI can create tensions for healthcare workers that may result in stress, anxiety, and discomfort when they make decisions. These tensions are paradoxical in nature as they may present conflicting demands that can persist over time and develop into seemingly irrational situations. Understanding how these paradoxical tensions affect healthcare workers' responses to AI is crucial in addressing their concerns. This study investigates the role of paradoxical tensions and the paradoxical mindset in shaping healthcare workers' responses to AI. The study examines how these two factors influence individuals' intention to adopt AI systems and tools and evaluates the users' satisfaction with them. Using a quantitative survey design, data were collected from 357 healthcare workers. The results, based on regression analysis, indicate that paradoxical tensions positively influence both individuals' intention to adopt AI systems and tools and their satisfaction with the current use of AI systems and tools. The results also indicate that the paradoxical mindset positively mediates these relationships. © 2024 The Authors

Purpose: The utilization of virtual agents, particularly chatbots, within healthcare and medical contexts is witnessing exponential growth owing to their capacity to provide comprehensive support to patients throughout their healthcare journey, by reshaping the healthcare business processes. Such transformation in healthcare service delivery processes is enabled by those digital entities able to offer a preliminary screening and consultation platform, facilitating patients’ interactions with real medical professionals. However, when redesigning processes through the integration of new technologies, particular attention to the reactions of end users cannot be neglected. Thus, the purpose of this paper is investigating how both chatbots' features and patients' individual differences may shape a redesigned/renewed service in the healthcare sector.

Design/methodology/approach: Through two experimental studies (Study 1 and Study 2), we examined the impact of chatbot tone of voice (formal vs unformal) on patients’ behavioral responses, particularly their intention to use.

Findings: Our investigation shed light on chatbots’ characteristics in terms of perceived warmth, denoting the friendliness and empathy conveyed by the chatbot, and competence, reflecting its effectiveness in addressing user queries or tasks, when used to reshape the service delivery process; Moreover, we also shed light on the moderating role of emotional receptivity seeking, indicating that the emotionality and non-verbal communication between doctor and patient, cannot be overlooked even in innovative digital environments.

Practical implications: Managers and marketers could leverage insights from this study to tailor chatbot interactions, optimizing tone of voice to enhance patient engagement and satisfaction. By focusing on perceived warmth and competence, they can design more effective digital health solutions. Additionally, recognizing the role of emotional receptivity can guide strategies for integrating chatbots in a way that maintains a human touch in patient communications.

Social implications: Findings importantly underscore the relevance of chatbot in improving patient care, making digital interactions more empathetic and responsive. This relevance extends to business process management by demonstrating how integrating emotionally intelligent chatbots may contribute to better service delivery on the basis of personalized and effective healthcare experiences.

Originality/value: The originality of this paper is about the relevance of considering chatbots’ and final users’ characteristics to strategically plan healthcare services process redesign. Indeed, it examines chatbots' perceived warmth and competence in reshaping service delivery processes. Additionally, it highlights the moderating role of emotional receptivity seeking, emphasizing the importance of emotional communication in digital healthcare environments. © 2025, Emerald Publishing Limited.

Purpose: Eye-tracking technology offers significant potential in healthcare by enabling hands-free interactions that reduce physical contact and infection transmission risks. However, its adoption remains suboptimal due to barriers including unfavorable cost-benefit perceptions, digital literacy gaps and workflow disruption concerns. This study explores value co-creation interactions between technology providers and healthcare professionals that facilitate eye-tracking adoption in digitalized healthcare settings.

Design/methodology/approach: The study employs a qualitative approach drawing on exploratory and semi-structured interviews with eye-tracking technology providers and physicians who use eye-tracking in clinical practice and research. Data analysis follows a thematic analysis approach.

Findings: The findings reveal two distinct but interconnected dimensions of value co-creation interactions that enable eye-tracking adoption. Value co-production-enabling interactions encompass aligning technology capabilities with user needs, co-developing manuals and guidelines and collaboratively interpreting regulatory frameworks. Value-in-use-enabling interactions focus on aligning technology performance with end-user experience, co-developing implementation strategies and collaborative data interpretation and analysis.

Originality/value: This study contributes to healthcare digitalization literature by examining how collaborative interactions between technology providers and healthcare professionals enable complex technology adoption. Unlike previous research focusing primarily on patient-provider relationships, this study demonstrates how technology providers actively participate in healthcare value chains beyond traditional product delivery. The findings extend value co-creation theory by identifying specific interaction mechanisms within value co-production and value-in-use dimensions, contributing to understanding how collaborative approaches may support hands-free healthcare technology integration. © Luis Irgang, Wiktor Zborowski, Ernesta Stakionyte, Henrik Barth and Magnus Holmen.

Recent advancements in connectivity and automation driven by artificial intelligence (AI) is leading to trans-formative changes in the healthcare sector. This study investigates physicians' experience of AI-based technologies in healthcare. To achieve this objective, we gathered responses through open-ended essays from 326 physicians working in Swedish healthcare. These respondents have experience in using AI technologies for distinct tasks, which include prediction, diagnosis, medical image analysis, text generation, analysis, chatbots, wearable devices, telemedicine and robot assistance. The data was analyzed by thematic coding. The findings show that the physicians' perception towards use of AI in healthcare is influenced by drivers and barriers that are present at macro, organizational, system and personal level. The identified drivers include work task changes, functional aspects, organizational aspects, system characteristics and personal motivators. The barriers include legal and ethical dilemma, organizational readiness, system limitations and personal demotivators. This study leverages paradox theory as a framework to deepen the understanding of the complexities and interconnections between perceived barriers and potential solutions related to AI in healthcare as a contribution to the literature. © 2025 The Authors. Published by Elsevier Ltd.

Background: The purpose of this paper is to explore on the main barriers related to the Internet of Things devices adoption in healthcare, with specific concern to developing countries.

Methods: A qualitative research approach with the systematic combination method has been adopted, considering an Indian hospital as a research setting. Semi-structured interviews were carried out as part of the data collection process

Results: The study helped us identify some of the main factors affecting IoT adoption in the hospital, categorised under three different sections: 1) technological barriers, 2) organisational barriers, and 3) environmental barriers. Findings suggest that IoT adoption barriers in hospitals are related to adoption challenges mainly focused on technological acceptance, complexity, organisational behaviour, lack of expertise and infrastructure, lack of stringent regulations and standards, and finally, security and privacy concerns.

Conclusion: Thus, this research makes a unique contribution for researchers and hospital managers in developing countries to understand the common barriers behind the IoT adoption process and to understand the steps to be taken to overcome these barriers. © 2025, Journal of Health Informatics in Developing Countries.

A growing and aging population provides challenges for the healthcare sector, generating higher healthcare costs, and ineffective work process that results in long patient queues and problems with recruiting and retaining healthcare professionals. Artificial intelligence (AI) is considered as one means to provide efficient processes for healthcare professionals, e.g. in diagnostics and treatment recommendations. However, research has shown that there are many obstacles to successfully introducing and using AI applications in healthcare, especially by focusing on the organizational level. However, individual healthcare professionals have an important role to play in the transition towards information driven healthcare. 

Therefore, we address the healthcare professionals' perception of the usefulness and value of AI applications, as well as challenges and considerations of this new technology. 

The study is based on an exploratory approach with more than 350 healthcare professionals in Sweden, carried out beginning of 2024. The questionnaire includes perceptions of the use of AI and identifies potential challenges that need to be addressed. The respondents include doctors (92%) and nurses (8%). The sample consists of answers from 221 (62%) male and 136 (38%) female respondents. Most of the respondents work in public hospitals (54%) and health centers (20% public and 14% private). Several AI applications are used by healthcare professionals, spanning from administrative work reduction to new insights in the analysis of complex cases.

Thematic analysis is conducted to create a model of perception of usefulness, values and problems (barriers). The analysis includes a stepwise analysis to identify patterns and themes.

The  results from the project provide insights into how the introduction of AI applications in healthcare changes the work of healthcare professionals and the perceived challenges that need to be addressed to improve their work by using AI. To some extent, implementation and use is based on healthcare professionals’ interest in using new advanced technology but for others the decision to adopt AI is primarily based on formal decisions within the organization. Respondents that have been using AI for at least six months, indicate AI supports decision making, with the main benefit consisting of a more effective and faster work process, while other respondents do not perceive any changes. A surprising result is that healthcare professionals have identified the possibility to test and evaluate new ideas and more complex cases. One interpretation is that AI has made the workload easier, which may allow for more innovative work. Another interpretation is that their experience-based knowledge is augmented by AI, and this makes it possible for them to handle more complex cases.   However, others experience a learning paradox – challenging to find time and learn how to use the technology, while at the same time adopting by testing AI applications.

Conclusions drawn from the ongoing study provide insights on the transformation phase towards implementing and using AI applications in healthcare.

Background: Healthcare is undergoing a digital transformation as advanced analytics, artificial intelligence (AI), and connected devices revolutionize clinical decision-making, care delivery, and patient outcomes. Digital technologies, particularly AI, have created unprecedented opportunities for value creation through improved diagnostics, automated workflows, and data-driven care. Despite this potential, the adoption of digital technologies in healthcare remains slower than in other industries. Multiple factors contribute to this gap, with value creation emerging as central to adoption decisions. While individual factors - such as healthcare professionals' responses to new technologies - significantly influence adoption, medical technology (MedTech) firms play an essential role in facilitating value creation through digitalization. Previous research has largely overlooked this complexity, particularly the interplay between healthcare professionals' adoption decisions and MedTech firms' capabilities. 

Purpose: The purpose of this doctoral thesis is to explore how healthcare professionals adopt digital technologies and how MedTech firms acquire capabilities to facilitate digitalization and value creation in healthcare. 

Method: To fulfill the overall purpose of this thesis, five independent papers were developed. The papers comprise one qualitative descriptive study, one sys- tematic literature review, one exploratory qualitative study, one embedded case study, and one survey. The data collection is based on exploratory and semi-structured interviews, workshops, observation, and questionnaires. The data analysis follows a thematic analysis technique and linear regression analysis. 

Findings: The findings of this thesis indicate that digitalization and value creation in healthcare emerge through dynamic interactions between healthcare professionals and MedTech firms. Healthcare professionals, as technology adopters, evaluate multiple value dimensions (cost-sacrifice, instrumental, hedonic, and symbolic) that shape their attitudes toward digital technologies. They engage in facilitation activities to manage resistance to change and develop paradoxical mindsets to navigate tensions arising from technological transitions. MedTech firms acquire the necessary capabilities through formal, vii semi-formal, and informal mechanisms to facilitate digitalization. These capabilities - health-related, data-driven, and social capabilities - are primarily accessed through external actors. Value co-creation occurs through structured encounters between healthcare professionals and MedTech firms, where they share resources and knowledge, align expectations, and collaborate to enhance technology adoption and value realization. This interplay creates a foundation for successful healthcare digitalization while ensuring technologies meet clinical needs and create sustainable value. 

Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. © 2023, The Author(s).