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Telemedical physician triage (TPT) is an example of a hierarchical knowledge-based service system (HKBSS) in which a second level of decision agent (telemedical physician) renders a decision on cases referred to him or her by the primary level agents (triage nurses). Managing the speed-versus-quality trade-off in such systems presents a unique challenge because of the interplay between agent knowledge and flow of work between the two levels. We develop a novel model of agent knowledge, based on the beta distribution, and deploy it in a partially observable Markov decision process model to describe the optimal policy for deciding which cases (patients) to refer to the second level for further evaluation. We show that this policy has a monotone control-limit structure that reduces the fraction of decisions made at the upper level as workload increases. Because the optimal policy is complex, we use structural insights from it to design two practical heuristics. These heuristics enable an HKBSS to adapt efficiently to workload shifts by adjusting the criteria for referring decisions to the upper level based on partial real-time queue length information. Finally, we conduct analytic and numerical analyses to derive insights into the management of a TPT system. We find that (1) the telemedical physician should evaluate more patients as congestion in the emergency room waiting area increases; (2) training that improves accuracy of the physician and/or nurses can be effective even if it only does so for a single patient type, but training that improves consistency must do so for all patient types to be effective; and (3) patient classification in triage should consider environmental and operational conditions in addition to the patient’s medical condition. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2905 . This paper was accepted by Vishal Gaur, operations management.

Background In order to support decisions regarding governance, organization and control models of the healthcare system, the Swedish government, as well as regional-level agencies, regularly commissions expert reports that are supposed to form the basis for decisions on new steering forms in healthcare. Aim The aim of this study was a) to perform a descriptive mapping of commissioned reports on Swedish healthcare governance and b) to pursue an in-depth content analysis of a strategic sample of such reports. Method Initially, 106 reports from both national and regional levels were gathered and analysed. A matrix was constructed, consisting of questions on who had commissioned the report, who had produced it, what problems the report set out to solve and what solutions were suggested. Further, questions were posed on whether the report was research-based and whether ethical assumptions and arguments were presented. Thereafter, a strategic sample of 36 reports was selected for an in-depth analysis, using inductive content analysis. Results The descriptive mapping showed that the aim of the analysed reports differed in form and content, and that they varied from giving an overview and investigating effects and consequences of new control models to more concrete goals, such as suggesting improvement measures. Academic experts involved in creating the reports often represented economics or business studies. The content analysis revealed examples of standardization in care, characterized by requirements to follow national guidelines, but also examples of requests for increased respect for professionals’ competence and experience. Further, the analysis showed how the definition of equity in care had changed, from a focus on equity in access to care in the reports produced in the 1990s to an emphasis of arguments for geographical sameness and equity in quality of care in the later reports. Discussion Two dominant trends were identified in the material, namely increased standardization and arguments for trust in the system. The great number of reports implies that the system risks requesting more information than it can handle and result in documents where the same message is recurrently repeated or create conflicts of interest and value tensions between different suggestions. Conclusion Commissioned reports can have substantial consequences for new reforms of management practices in healthcare. It is therefore important to investigate them critically. The results of our investigation may contribute to a more comprehensive and adequate model for acquiring and using expert reports regarding healthcare governance, both in Sweden and in similar healthcare systems.

A conceptual model of BIM-based design and refurbishment, based on pre-built indicators and allowing the assessment of the building energy demand and eco-building parameters, is presented. The new approach presented in this model creates a knowledge-based decision-making environment for refurbishment strategies and quality control, in this way creating the preconditions to bridge the gap between expected and actual energy performance. The model with integration of new BIM-based optimization subsystems enables energy management and optimization processes. For a comprehensive evaluation of refurbishment measures, it is suggested to include energy efficiency, eco-efficiency, and economic parameters.

This paper provides an integrated and comprehensive conceptual framework for knowledge based decision making support systems. Previous research has focused primarily on general decision support systems. The paper extends the previous work by presenting a framework to support specific decisions using knowledge marts that contain decision pertinent knowledge. A proposed methodology to test the effectiveness of this new model is proposed. The model presented provides much more specific knowledge support than previous systems.

The paper aims to present the methodological framework used to design a conceptual model for knowledge management processes integration into strategic decision-making at research and development organisations. The framework structure includes four sequential stages based on both quantitative and qualitative data, which define the main factors and relations of integration. First, a sequential methodology between a systematic literature review and qualitative content analysis is used. Second, a matrix relation scheme of knowledge management processes into decision-making phases is applied. Third, a standardised representation of integrated processes is developed using business process model and notation and business process integration model methods. Finally, the integrated knowledge-based decision-making process aligned with corporate strategy definition for R&D organisations is presented.

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. RR2-10.37766/inplasy2022.9.0061.

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

Purpose The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research. Design/methodology/approach The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge. Findings The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge. Practical implications Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions. Originality/value The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

Automatic cephalometric landmark detection has emerged as a pivotal area of research that combines medical imaging, computer vision, and orthodontics. The identification of cephalometric landmarks is of utmost importance in the field of orthodontics, as it contributes significantly to the process of diagnosing and planning treatments, as well as conducting research on craniofacial aspects. This practice holds the potential to improve clinical decision-making and ultimately increase the outcomes for patients. This work explores a wide range of strategies, encompassing both traditional edge-based methods and advanced deep learning approaches. The study leveraged various academic publication databases like IEEEXplore, ScienceDirect, arXiv, Springer and PubMed to thoroughly search for articles related to automatic cephalometric landmark detection. Additionally, other pertinent publications were acquired from credible sources like Google Scholar and Wiley databases. Screening the articles relied on three selection criteria: (a) publication titles, abstracts, literature reviews, (b) cephalometric radiograph datasets suitable for 2D landmarking, and (c) studies conducted over different time periods were employed to gain a comprehensive understanding of the evolution of methodologies used in landmark prediction to identify the most relevant papers for this review. The initial electronic database search identified 268 papers on landmark detection. A total of 118 publications were selected and incorporated in the present study after a meticulous screening process. Performance analysis was conducted on studies that reported Successful Detection Rates (SDRs) within different clinically accepted precision ranges, Mean Radial Error (MRE) with Standard Deviation (SD) between manually annotated and automated landmarks as outcomes. Bar graphs and custom combination plots were utilized to analyse the correlations among different methodologies employed and their evaluation metrics outcomes. The performance comparison results indicate that Deep Learning techniques showed superior accuracy in automating 2D cephalometric landmarks compared to other conventional and Machine Learning approaches. Recently, more advanced Deep Learning algorithms have been developed to improve the accuracy of automatic landmark prediction.

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working has highlighted the shortcomings in current procedures. These gaps will be filled by artificial intelligence (AI), which will also alter how KM is transformed and knowledge is handled. This article analyzes studies from 2012 to 2022 that examined AI and KM, with a particular emphasis on how AI may support businesses in their attempts to successfully manage knowledge and information. This critical review examines the current approaches in light of the literature that is currently accessible on AI and KM, focusing on articles that address practical applications and the research background. Furthermore, this review provides insight into potential future study directions and improvements by presenting a critical evaluation.