Purpose Governments increasingly deploy artificial intelligence (AI)-based chatbots to improve accessibility, efficiency and consistency in citizen services. However, empirical evidence on how such systems contribute to public value and service process transformation remains limited. This study aims to examine how a government-operated chatbot shapes citizen interactions and public value through emotional, thematic and behavioral dynamics. Design/methodology/approach The study analyzes 1,846 real-world inquiries submitted to a Ministry of Labor chatbot. Sentiment analysis, computational content classification and longitudinal trend modeling were used to assess emotional tone, topic concentration, usage patterns and repeat-contact behavior. Findings Citizen inquiries were predominantly neutral or negative in tone, while chatbot responses remained largely neutral, indicating emotional stabilization rather than escalation. Employment security topics generated higher emotional intensity, although sentiment differences across topics were not statistically significant. Usage fluctuated over time, and repeated inquiries increased moderately, suggesting both informational friction and growing institutional reliance. Practical implications Effective public-sector chatbot deployment requires process-oriented governance, topic-sensitive response design, explainability mechanisms and continuous performance monitoring to align operational efficiency with citizen experience. Originality/value The study advances understanding of AI-enabled service transformation by operationalizing public value through interaction-level indicators and demonstrating how chatbot-mediated processes influence citizen experience in digital government.

Bear encounters are increasing worldwide, complicating wildlife management efforts. This research presents an efficient and reliable bear detection system integrating Internet of Things (IoT) devices with GPS tracking. Designed for individuals and institutions monitoring wildlife, the system—Beartrackingapp—combines custom hardware and software to improve real-time tracking and response. We outline its architecture and discuss how it enhances safety in human-bear interactions.

Digital sovereignty, the assertion of national authority over digital infrastructure, data, platforms, and technologies, has become a critical concern amidst global surveillance and platform dependency, particularly for the Global South, including India. This study investigates the multifaceted nature of India’s pursuit of digital sovereignty, moving beyond technical or legal definitions to examine infrastructure control, technological autonomy, and governance legitimacy. Drawing on the Resource-Based View, Dependency Theory, and Institutional Theory, the study posits five hypotheses about the impact of local digital infrastructure investment, data governance frameworks, digital literacy and skills, open-source technology adoption, and regional cooperation on digital sovereignty. Employing a quantitative survey methodology (i.e., based on 317 responses) and Partial Least Squares Structural Equation Modelling (PLS-SEM) technique, the research provides empirical support for the theoretical framework, highlighting the crucial roles of institutional coherence, infrastructural development, and endogenous capabilities in fostering national digital autonomy. These findings contribute to the understanding of digital sovereignty as a strategic imperative and developmental goal for emerging economies, offering insights for policymakers in the Global South.

Abstract Background Hip fractures are among the most common consequences of osteoporosis, yet adequate diagnosis in medical records remains suboptimal, and translation of documented osteoporosis into pharmacologic therapy is often incomplete. This study evaluated whether integration of a dedicated nurse practitioner into the orthopedic inpatient workflow was associated with improved osteoporosis documentation following hip fracture. A secondary, exploratory aim was to examine associations between documentation and downstream clinical outcomes. Methods Data were extracted from the MDClone big data platform (ADAMS), and included patients aged ≥ 60 hospitalized with ICD-10 coded hip fractures between 2007-2024. Results The cohort comprised 6,933 patients, of whom 4,405 (63.5%) were women, with a mean age of 81.3 years. A total of 4,150 patients (59.8%) were discharged with a diagnosis of osteoporosis or osteoporotic fracture. A sustained increase in documentation rates was observed after 2015, coinciding with the integration of a nurse practitioner into inpatient care. Recorded use of anti-osteoporosis pharmacologic therapy was low and similar between groups. No consistent differences were observed in secondary clinical outcomes. Conclusions The rate of osteoporosis documentation following hip fractures was substantially higher than reported in the literature from centers without fracture liaison services. Integration of a dedicated nurse practitioner into the orthopedic inpatient workflow was associated with a significant and sustained improvement in osteoporosis documentation. These findings highlight the potential of targeted inpatient interventions to improve post-fracture osteoporosis recognition and documentation. Trial registration Not applicable. This is a retrospective observational study.

PurposeThis research investigates ChatGPT-4o reliability in analyzing medical data for diabetic patients at risk of limb loss. It evaluates whether a generative AI tool can serve as a viable alternative to traditional statistical methods for predictive medical analysis. The research question is: How does ChatGPT-4o perform in answering predictive questions about patient outcomes compared with a professional statistician using conventional tools?MethodsData were drawn from Sheba Medical Center's diabetic foot clinic, focusing on mortality and amputation risk. ChatGPT-4o's predictive responses were compared with those produced by a professional statistician. The study emphasized the importance of prompt design and required substantial human involvement in data cleaning to ensure accuracy.ResultsChatGPT-4o produced accuracy comparable to traditional statistical methods when prompts were well-designed. Findings highlight the central role of prompt engineering in obtaining reliable outputs. Human intervention in preparing the dataset remained necessary, underscoring current limitations in fully automating the process.ConclusionThe study demonstrates the potential of generative AI-specifically ChatGPT-4o-as a tool enabling clinicians to analyse medical data without advanced technical training. With proper instruction and careful prompt engineering, generative AI can help democratize access to predictive medical analysis as a user-friendly alternative to conventional methods.

While digital transformation has offered opportunities to improve public service delivery, including healthcare, it also inadvertently facilitated the spread of misinformation. This study explores the factors contributing to and exacerbating health misinformation during the COVID-19 pandemic in Ethiopia and Liberia, two developing countries with diverse socio-cultural backgrounds. This study investigates the complex interplay of technological, socio-cultural, and political factors that influenced the creation, dissemination, and amplification of misinformation in these contexts. Qualitative data collected through 35 interviews with key stakeholders, including health communicators, policy consultants, medical professionals, journalists, and community leaders, were thematically analysed. The findings reveal 12 key factors, grouped into three categories, that contributed to the creation and spread of misinformation: the abundance of open data (i.e., open public health information), democratised content creation, weakened gatekeepers, echo chambers, anonymity on digital platforms, trust in government institutions, cultural beliefs, community dynamics, language barriers, political polarisation, limited government capacity, and historical factors. Drawing on these findings and country-specific nuances, the study proposes tailored strategies and interventions to combat health misinformation and mitigate its negative consequences in Ethiopia and Liberia.

Research suggests that the anticipated benefits of digital transformation are realised when organisations demonstrate dynamic capabilities-the ability to integrate, adapt, and reconfigure both internal and external resources. These capabilities enable organisations to meet the demands of today's dynamic technological, political, and business environment. One such capability is the extent to which employees can anticipate and effectively respond to significant internal and external changes. This phenomenon, referred to as workforce agility, has been recognised as a key determinant of how digital transformation strategies are planned and appropriately executed. However, a closer examination of the existing literature reveals that the relationship between workforce agility and digital transformation remains largely unexplored, particularly within the public sector context. Thus, this study set out to contribute to the limited literature on public sector digital transformation by focusing on organisational agility and two related constructs-organisational structure and transformational leadership. Employing PLS-SEM analysis on data from 392 public sector respondents, the findings offer empirical support for the significant direct influence of workforce agility and organisational structure in driving successful digital transformation. The results confirm that well-defined yet flexible organisational structures support the adoption of digital technologies and foster workforce agility, which in turn facilitates digital transformation. Furthermore, the study reveals that transformational leadership significantly strengthens the positive impact of workforce agility on digital transformation, highlighting its role in cultivating an environment where employees actively contribute to digital initiatives. Interestingly, however, the moderating effect of transformational leadership on the direct relationship between organisational structure and digital transformation was not statistically significant, suggesting that a robust organisational structure may provide a sufficient foundation for digital transformation, potentially minimising the additional direct influence of leadership on this specific link. The study's contributions to both research and practice are presented, offering nuanced insights into the interplay of structure, agility, and leadership in public sector digital transformation.

The current article describes a process to mitigate challenges that arise when medical practitioners and data specialists operate with differing terminologies and face technological and organizational barriers in accessing and utilizing medical big data. We present a structured methodology for improving access to clinical data and apply this approach using a case study focused on optimizing antibiotic management for patients with gram-negative bloodstream infections. Using the ArchiMate® organizational architecture language, we developed a project framework that aligns strategic, business, application, and technological layers of hospital operations. Each component was used to articulate project goals, guide the implementation process, and track intervention outcomes. After implementing a real-time monitoring tool and engaging clinicians directly in the data workflow, 65% of the identified patients received targeted interventions, and the median duration of antibiotic therapy was reduced from 6 to 5 days. Our approach enabled faster decision-making, and drove meaningful organizational change - demonstrating how structured data access can lead to improved healthcare delivery and patient outcomes.

Healthcare systems rely on vast data repositories serving a variety of purposes which include business functions and patient medical records. With recent advances in big data analytics, the latent value for all this data has become more apparent yet operational use has lagged behind. Healthcare operations suffer from multiple and complex issues making harnessing the potential of big data more important than ever. This article describes use of the ArchiMate® modeling language to adopt an organizational architecture language for the purpose of building a model for personalization of healthcare-related big data. This model describes how data can be made accessible to healthcare professionals in a way that allows clinicians to generate clinical, operational, and managerial value from the data with minimal involvement of additional data professionals. The article concludes with a case study proof-of-concept demonstrating the value of the approach and suggestions for implementation.

Inspired by the success of organisations in the private sector, public organisations turned to customer relationship management (CRM) tools to learn about various citizen groups and their particular needs. Recently, AI-driven CRM (also referred to as AI-CRM) has increasingly gained recognition as a revolutionary solution for improving the relationship between organisations and their customers-by automating routines, improving segmentation and prioritisation, providing virtual assistance, and guiding employees. However, various challenges were found to hinder the adoption of the technology and the realisation of the anticipated benefits of AI-CRM in the public sector. This study explores AI-CRM adoption and integration with existing systems at the Ministry of Labour of Israel. Based on the review of the extant literature and in-depth interviews with selected experts in various units, we present the expected results and contributions of the ongoing study.

The COVID-19 crisis has affected human mental health and health behavior. This was not the first pandemic in the world and will not be the last. Pandemics affect health behavior regarding prevention, avoidance, and management of illness. This study applies literature from Information Systems (IS) success, pandemic behavior, unified theory of acceptance and use of technology (UTAUT), and mobile technology identity (MTI) to investigate the effects of information technology (IT) attributes on health behavior during a pandemic. It is based on data from 232 patients of Arabic ethnic origin who visited a primary care clinic in Shefar'am. Results, based on structural equation modeling, indicate that perceived usefulness of mobile technology increases satisfaction with health reporting. Moreover, anxiety increases disclosure effort in using mobile technology, both directly and through a path mediated by MTI. Finally, disclosure effort reduces change in health behavior and satisfaction with self-reporting. Previous studies on health behavior during a pandemic and the role of IT in health behavior do not consider the role of IT in people's heath behavior, such as how IT can enable them to more frequently check their health during the pandemic. This study contributes to the literature by addressing this gap and providing insights into the role of IT in health behavior change during a pandemic. It also offers insights for clinics regarding the usefulness of mobile health (m-health) applications for patients so that they can to help patients change their health behavior towards using these applications.

The rapidly changing technological, economic, and political landscapes have become a defining characteristic of this era. Organisations across sectors and industries are embarking on digital transformation to navigate through this dynamic environment. However, the success of digital transformation depends on dynamic capability—the ability to integrate, adapt and reconfigure internal and external capabilities to meet the demands of the dynamic environment. One of these capabilities is workforce agility—the ability of the workforce to foresee and respond to significant internal and external changes. Workforce agility is one of the imperative capabilities that can determine digital transformation success. However, the relationship between the two constructs is rarely studied, particularly in the public sector. Recognising the differences between organisations in the private and public sectors, this study investigates the significance of workforce agility for digital transformation in public organisations. The study also aims to test the moderating effect of transformational leadership on the influence of (1) workforce agility on digital transformation and (2) organisational structure on digital transformation. The theoretical model developed based on the extant literature will be tested using quantitative data collected in three municipalities in Sweden. Partial Lease Square Structural Equation Modelling (PLS-SEM) will be used for data analysis. Expected results and contributions are briefly discussed.

As the world’s population continues to live longer, the consideration/inclusion of older adults in mainstream technology development is paramount. Organizations that develop software and hardware for mainstream technology will miss a significant market segment if they fail to consider the aging population. The following questions are addressed in this paper as shared by three academic experts in this research domain. What are the prominent definitions of social inclusion? 2) How do older adults (65 years or older) fit that definition? What are some key interpretations that placed older adults as a socially excluded group in mainstream technology? How have IT vendors included aging adults? And, what are the ways IT vendors have not included aging adults? How do you encourage vendors/software developers to consider cognitive age versus chronological age in new app development? Points of convergence and divergence among the researchers are reported and suggestions for future research addressing the inclusion of older adults in mainstream technology development are provided.

Healthcare systems produce big data with latent capabilities for healthcare providers. Big data is a strategic resource and requires the appropriate infrastructure for data entry, systematic analysis, and visualizations for decision makers. Attempts to build a big data infrastructure raise various challenges related to availability, accessibility, reliability, and quality while considering information privacy and security. These challenges are significant and can disrupt the ability to realize data’s hidden potential. A variety of technological tools are available to medical staff members for big data use in healthcare. However, access to the data alone does not guarantee the appropriate use of these tools and still requires understanding the needs of end users to ensure success. In recent decades, several models have been developed to evaluate the implementation of new information technology and the adoption of technology by users. The current paper focuses on this value and related challenges: how to turn organizational data into meaningful knowledge by introducing a new implementation model in big data in healthcare. The model is an integrated one, presenting practical aspects, timeline aspects related to the life of the project, personalization in access to data, reference to information providers, and technological solutions. The project uses an organizational architecture tool to describe the implementation model and generate outcomes. The model will be based on 15 clinical and managerial use cases. Outcomes will be described by strategic objectives in the model and will be presented in the ArchiMate® language.

Women diagnosed with HPV face a hard-to-understand disease that may impact their psychological and physical health and may pose challenges communicating with healthcare providers in sensitive settings. We posit patient empowerment through targeted educational materials can improve sensitive communication and lead to better health outcomes. This study measured the impact of a patient-empowerment process used in a gynecology clinic for HPV patients to improve sensitive communication during medical-related meetings and on subsequent patient empowerment outcomes. The empowerment process was based on expert-vetted informative material made accessible in the physician’s waiting room on tablet devices. Communication between physicians and patients was measured during medical visits via a direct observation, encoding process. Empowerment items were tested following medical visits. The results were compared to a control group that received non-medical, lifestyle material. 237 female, gynecology patients from a large, private clinic participated. Using expert-vetted, relevant material to enhance patient education in a clinical setting results in higher levels of patient empowerment. Physician interaction impacts patient empowerment as do various communication behaviors and this can lead to positive health outcomes. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://theberylinstitute.org/experience-framework/). Access other PXJ articles related to this lens. Access other resources related to this lens.