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The potential benefits of collaboration technologies are typically realized only in groups led by collaboration experts. This raises the facilitator-in-the-box challenge: Can collaboration expertise be packaged with collaboration technology in a form that nonexperts can reuse with no training on either tools or techniques? We address that challenge with process support applications (PSAs). We describe a collaboration support system (CSS) that combines a computer-assisted collaboration engineering platform for creating PSAs with a process support system runtime platform for executing PSAs. We show that the CSS meets its design goals: (1) to reduce development cycles for collaboration systems, (2) to allow nonprogrammers to design and develop PSAs, and (3) to package enough expertise in the tools that nonexperts could execute a well-designed collaborative work process without training.

Palliative care is based on the principle of multi-professional collaboration, which integrates diverse competencies and perspectives to provide holistic care and support for patients and their relatives. In palliative care teams, there is an intensive exchange of information and knowledge; however, current documentation and hospital information systems often fall short of meeting the specific demands for effective collaboration and dynamic communication in this field. This action design research study is based on the three-and-a-half-year interdisciplinary research project PALLADiUM and aims to demonstrate the added value of knowledge-driven digitalization. Our study provides novel recommendations for digitally supported multi-professional collaboration tailored to the specific requirements of palliative care and similar fields. Based on the analytical distinction between 'information' and 'knowledge,' we present design recommendations for co-creative, knowledge-driven development processes and multi-professional collaboration support systems. We further illustrate how these recommendations have been implemented into a functional technical demonstrator and outline how our results could impact future digitalization initiatives in healthcare.

Computer-supported collaborative learning (CSCL) is widely adopted in industry learning, but it still faces challenges, including low participation, distraction, and learning inefficiency. In our study, we follow the design science research method to develop artifacts (a process and discussion platform) to address these CSCL challenges. Collaboration engineering was used as our design theory. A Discussion Platform was designed as a tool to help non-expert practitioner instruct collaborative learning process. We carried out evaluations on the two designed artifacts through 81 managers working in various industries through a mixed-method approach, including survey and qualitative interviews. We find that our designed artifacts receive high satisfaction in industry CSCL and reduce problems of low participation, distraction, and learning inefficiency. We identified several factors that contribute to the problem solving of low participation, distraction and inefficiency in industry CSCL, including usability, expression affordance, process guidance, goal clarity, flexibility affordance, thinkLet instruction, and flow experiences.

The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, whereas the undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSSs) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information. However, comprehensive adoption is partially disrupted by specific technological and personal characteristics. With the rise of artificial intelligence (AI), CDSSs have become an adaptive technology with human-like capabilities and are able to learn and change their characteristics over time. However, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSSs. Our study aims to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of an AI-enabled CDSS. We conducted a literature review including 3 different meta-databases, screening over 1000 articles and including 101 articles for full-text assessment. Of the 101 articles, 7 (6.9%) met our inclusion criteria and were analyzed for our synthesis. We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSSs in accordance with our research objective, namely, training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSSs, some characteristics and factors retain their importance, whereas others gain or lose relevance owing to the uniqueness of human-AI interactions. However, only a few (1/7, 14%) studies have mentioned the theoretical foundations and patient outcomes related to AI-enabled CDSSs. Our study provides a comprehensive overview of the relevant characteristics and factors that influence the interaction and collaboration between medical professionals and AI-enabled CDSSs. Rather limited theoretical foundations currently hinder the possibility of creating adequate concepts and models to explain and predict the interrelations between these characteristics and factors. For an appropriate evaluation of the human-AI collaboration, patient outcomes and the role of patients in the decision-making process should be considered.

Interest in critical care-related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally. The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords. A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed. The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies. This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.

Background Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. Main body Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent “black-box” nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. Conclusions AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

Shared understanding has been claimed to be crucial for effective collaboration of researchers and practitioners. Heterogeneity in work groups further strengthens the challenge of integrating understanding among diverse group members. Nevertheless, shared understanding and especially its formation are largely unexplored. After conceptualizing shared understanding, we apply collaboration engineering to derive a validated collaboration process module (compound thinkLet \"MindMerger\") to systematically support heterogeneous work groups in building shared understanding. We conduct a large-scale action research study at a German car manufacturing company. The evaluation indicates that with the use of MindMerger, team learning behaviors occur, and shared understanding of the tasks in complex work processes increases among experienced diverse tool and dye makers. Thus, the validated compound thinkLet MindMerger provides designers of collaborative work practices with a reusable module of activities to solve clarification issues in group work early on. Furthermore, findings from the field study contribute to the conceptualization of the largely unexplored phenomenon of shared understanding and its formation.

Information and communication technologies (ICTs) are becoming an impetus for quality health care delivery by nurses. The use of ICTs by nurses can impact their practice, modifying the ways in which they plan, provide, document, and review clinical care. An overview of systematic reviews was conducted to develop a broad picture of the dimensions and indicators of nursing care that have the potential to be influenced by the use of ICTs. Quantitative, mixed-method, and qualitative reviews that aimed to evaluate the influence of four eHealth domains (eg, management, computerized decision support systems [CDSSs], communication, and information systems) on nursing care were included. We used the nursing care performance framework (NCPF) as an extraction grid and analytical tool. This model illustrates how the interplay between nursing resources and the nursing services can produce changes in patient conditions. The primary outcomes included nurses' practice environment, nursing processes, professional satisfaction, and nursing-sensitive outcomes. The secondary outcomes included satisfaction or dissatisfaction with ICTs according to nurses' and patients' perspectives. Reviews published in English, French, or Spanish from January 1, 1995 to January 15, 2015, were considered. A total of 5515 titles or abstracts were assessed for eligibility and full-text papers of 72 articles were retrieved for detailed evaluation. It was found that 22 reviews published between 2002 and 2015 met the eligibility criteria. Many nursing care themes (ie, indicators) were influenced by the use of ICTs, including time management; time spent on patient care; documentation time; information quality and access; quality of documentation; knowledge updating and utilization; nurse autonomy; intra and interprofessional collaboration; nurses' competencies and skills; nurse-patient relationship; assessment, care planning, and evaluation; teaching of patients and families; communication and care coordination; perspectives of the quality of care provided; nurses and patients satisfaction or dissatisfaction with ICTs; patient comfort and quality of life related to care; empowerment; and functional status. The findings led to the identification of 19 indicators related to nursing care that are impacted by the use of ICTs. To the best of our knowledge, this was the first attempt to apply NCPF in the ICTs' context. This broad representation could be kept in mind when it will be the time to plan and to implement emerging ICTs in health care settings. PROSPERO International Prospective Register of Systematic Reviews: CRD42014014762; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42014014762 (Archived by WebCite at http://www.webcitation.org/6pIhMLBZh).

PurposeThe authors investigate how logistics digitalization affects two types of third-party logistics (3PL) performance: financial performance and service performance. In particular, the authors explore the mediating role of customer collaboration between logistics digitalization and firm performance based on organizational information processing theory and examine the moderating role of government support.Design/methodology/approachThe authors use an SPSS macro program (PROCESS regression analysis) to analyze survey data from 235 3PL firms in China. The mediation model, moderation model and moderated mediation model are tested.FindingsThe empirical results show that in the new age of digitalization transformation, logistics digitalization positively affects 3PL's financial performance and service performance by strengthening customer collaboration. Additionally, government support amplifies the positive effect of customer collaboration on service performance but not financial performance. The moderated mediation test further indicates that government support strengthens the positive indirect effect of digitalization on service performance through customer collaboration.Originality/valueThis study offers empirical insights into the growing body of 3PL literature, and the findings contribute to the theoretical and practical understanding of the emerging research topic of digital transformation (DT) and sustainability issues in 3PL firms.

Artificial intelligence (AI)-enabled decision support systems are critical tools in medical practice; however, their reliability is not absolute, necessitating human oversight for final decision-making. Human reliance on such systems can vary, influenced by factors such as individual psychological factors and physician experience. This study aimed to explore the psychological factors influencing subjective trust and reliance on medical AI's advice, specifically examining relative AI reliance and relative self-reliance to assess the appropriateness of reliance. A survey was conducted with 223 dermatologists, which included lesion image classification tasks and validated questionnaires assessing subjective trust, propensity to trust technology, affinity for technology interaction, control beliefs, need for cognition, as well as queries on medical experience and decision confidence. A 2-tailed t test revealed that participants' accuracy improved significantly with AI support (t =-3.3; P<.001; Cohen d=4.5), but only by an average of 1% (1/100). Reliance on AI was stronger for correct advice than for incorrect advice (t =4.2; P<.001; Cohen d=0.1). Notably, participants demonstrated a mean relative AI reliance of 10.04% (139/1384) and a relative self-reliance of 85.6% (487/569), indicating a high level of self-reliance but a low level of AI reliance. Propensity to trust technology influenced AI reliance, mediated by trust (indirect effect=0.024, 95% CI 0.008-0.042; P<.001), and medical experience negatively predicted AI reliance (indirect effect=-0.001, 95% CI -0.002 to -0.001; P<.001). The findings highlight the need to design AI support systems in a way that assists less experienced users with a high propensity to trust technology to identify potential AI errors, while encouraging experienced physicians to actively engage with system recommendations and potentially reassess initial decisions.