Patients, particularly those at the end of their lives, frequently receive goal-discordant care, and their surrogate decision-makers suffer long-term psychological injury. Contributors to these issues may include infrequent communication between clinicians and surrogates, failure to discuss prognosis, values, and treatment options that include comfort-focused care, and surrogates facing high-stakes decision-making while underprepared and overwhelmed psychologically and emotionally. This is a multicenter, patient-randomized efficacy trial of a multi-component intervention, versus usual care, for 370 incapacitated, critically ill adults at high risk of death or severe disability, and their surrogate decision-makers, from 7 hospitals across the United States. The intervention combines surrogate utilization of a digital Family Support Tool (FST) in real-time during their loved one's hospitalization with proactively scheduled family meetings, for which both surrogates and clinicians receive additional preparation, at set intervals during the ICU hospitalization. Those in the control arm will receive usual ICU care. Our primary outcome is patient-centeredness of care, measured using the modified Patient Perceived Patient-Centeredness of Care (PPPC) scale. Secondary outcomes include surrogates' psychological symptom burden, communication and decision quality, and patients' health resource utilization and clinical outcomes. This trial will provide robust evidence about the impact of combining the FST with increased and intentional communication, on patient, family, and health system outcomes for those hospitalized in the ICU.

Health Services Research: Practice Patterns, Quality of Life and Shared Decision Making II (IP80)

Health Services Research: Practice Patterns, Quality of Life and Shared Decision Making I (IP67)

With the rapid development of artificial intelligence, autonomous driving has advanced significantly. Vehicle trajectory prediction, a key component of autonomous driving, directly affects the quality of planning and decision-making in dynamic traffic scenarios and is crucial for intelligent transportation systems. This paper proposes a collaborative prediction framework integrating physical and data-driven models. The physical model, enhanced by driving style parameters, better characterizes vehicle kinematics and driving styles for improved trajectory prediction. The data-driven model employs a spatiotemporal feature decoupling module with a dual-stream attention mechanism to extract and fuse temporal and spatial features from historical trajectories, enabling accurate future trajectory prediction. Through a bidirectional constraint framework, the two models achieve collaborative parameter optimization: the physical model provides kinematic feasibility constraints to ensure predictions align with real-world motion laws, while the data-driven model compensates for unmodeled dynamic factors, addressing the limitations of physical models in complex scenarios. Experimental results on the NGSIM and HighD data sets validate the superiority of the proposed method, providing a novel solution for trajectory prediction in autonomous driving.

Approved Mental Health Professionals (AMHPs) hold essential decision-making authority on whether individuals will be subject to compulsory detention under the Mental Health Act 1983 (as amended 2007) in England and Wales. Despite exercising profound coercive powers affecting individual liberty, the regulatory architecture surrounding AMHP practice is fragmented and diffuse, with oversight distributed across the Care Quality Commission, Social Work England, and multiple professional body regulators, and with no single body holding end-to-end accountability for decision quality. The dominant regulatory approach in contemporary UK health and social care is Right-touch Regulation (RTR), developed by the Professional Standards Authority and articulated through successive iterations since 2009. RTR presents itself as a model of proportionate, targeted, and risk-based intervention: a 'third way' between heavy-handed oversight and regulatory absence. The central claim of this article is that Right-touch Regulation, as currently utilised by the PSA, is structurally unsuited to AMHP oversight. The model presupposes conditions that are not present in AMHP governance. Situating RTR within broader regulatory theories (responsive regulation, smart regulation, and harm-based regulation), the article reviews what RTR claims as lineage but omits in practice. The conclusion argues that, until the preconditions for proportionate regulation are established (visibility, ownership, feedback), the language of Right-touch continues to legitimate a system that does not effectively regulate at all.

The exponential growth of organizational data across industries has created an urgent need for effective tools that convert raw data into actionable intelligence. Decision Support Systems (DSS), traditionally reliant on static reporting and spreadsheet-based analysis, have undergone a fundamental transformation with the integration of interactive visualization platforms. This research paper investigates the deployment of Microsoft Power BI as a visualization-based Decision Support System (V-DSS) across organizations in the Marathwada region of Maharashtra, India, with a focus on how real-time dashboards, interactive data models, and AI-enhanced visual analytics enhance managerial decision-making quality, speed, and confidence. Employing a mixed-methods research design combining a structured survey of 135 managers, analysts, and business intelligence professionals with qualitative case study analysis of four organizations in Chhatrapati Sambhajinagar, the study documents significant improvements in decision-making cycle time (average 39% reduction), report generation time (average 67% reduction), and managerial confidence in data-driven decisions (from 51% to 84% among high Power BI adopters). Key Power BI capabilities driving these outcomes include the DAX (Data Analysis Expressions) formula engine, Power Query data transformation, natural language Q&A queries, and custom visual integration. The study introduces the Visualization-Decision Effectiveness Framework (VDEF) as a structured implementation model, while identifying critical barriers including data governance gaps, Power BI licensing costs, and organizational resistance to self-service analytics cultural change

Emerging adult students face complex adaptation challenges that demand effective decision-making abilities. This study aims to examine the mediating role of depression in the relationship between resilience and decision-making styles. Using a quantitative correlational design, data were collected from 70 college students through purposive sampling. The instruments employed included the Connor-Davidson Resilience Scale (CD-RISC), the Beck Depression Inventory-II (BDI-II), and the General Decision-Making Style (GDMS) inventory. Mediation analysis using the PROCESS Macro Model 4 revealed that resilience was negatively correlated with depression (r = -0.612) and positively correlated with decision-making style (r = 0.559). The mediation test confirmed that depression serves as a partial mediator (indirect effect 0.419;95% CI [0.267, 0.570]). These findings underscore that resilience enhances decision-making quality both directly and indirectly by reducing depressive symptoms

Purpose This study aims to examine the relationships among AI capability, decision-making quality, and innovation performance, and to investigate the moderating effect of algorithmic transparency on the relationship between AI capability and decision-making quality. Design/methodology/approach A questionnaire survey was conducted using the Credamo data platform. To reduce common method bias, a time-lagged survey design was adopted. Data on AI capability, algorithmic transparency, decision-making quality, and innovation performance were collected from 435 participants. Established scales from authoritative foreign journals were used for measurement, and appropriate translation and verification procedures were carried out. Findings (1) AI capability is positively associated with innovation performance. Decision-making quality mediates the relationship between AI capability and innovation performance. (2) Algorithmic transparency positively moderates the relationship between AI capability and decision-making quality. Originality/value This study enriches AI capability research by incorporating engineering perspectives. It extends organizational learning theory by examining how AI capability shapes decision-making processes within engineer–AI collaboration contexts, identifying decision-making quality as a mediator and algorithmic transparency as a moderator. The findings offer practical insights for construction firms to enhance innovation performance through effective AI integration while helping engineers better leverage AI tools in design and project management workflows.

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises.

The ABCD of AI-Enabled Clinical Reasoning: A Guided Framework to Transform Learner Interactions and Decision Quality.

The relationship between financial literacy, self-efficacy, and performance in a financial simulation game.

This study aims to empirically examine the effect of earnings informativeness, income smoothing, and capital structure on firm value, with managerial ability as a moderating variable. The research is grounded in signaling theory, which emphasizes that the quality of financial information and financial decisions serve as important signals in shaping investor trust and perceptions. The research design applies a causal approach with a quantitative method. The study focuses on industrial sector companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. From a total of 67 firms, 45 were selected as the sample using purposive sampling based on consistent listing criteria and financial reporting in Indonesian rupiah, resulting in 135 panel data observations. The analysis employed Chow, Hausman, and Lagrange Multiplier tests to determine the most appropriate model, with estimation conducted using the Random Effects Model (REM) adjusted by robust standard errors. The moderating effect was tested using Moderated Regression Analysis (MRA). The findings reveal that earnings informativeness has no significant impact on firm value, while income smoothing and capital structure show a significant positive effect. Managerial ability was found to weaken the relationship between income smoothing and firm value but did not moderate the relationship of earnings informativeness or capital structure with firm value. Overall, these results highlight the importance of effective capital structure management and transparent earnings reporting as positive signals for investors, while managerial ability plays a more selective role in influencing market responses to corporate information.

The article looks at how to visualize complex information in project management, focusing on how people perceive and process information. It examines how individuals see, interpret, and remember visual data when faced with dense information and limited time for making decisions. The study reviews how different visual formats affect data understanding, lower cognitive load, and improve the quality and speed of managerial decisions. It highlights visual design as a way to structure complex information. Clarity, logical hierarchy, and consistency in presenting data are crucial. The article investigates how color, contrast, typography, and spatial organization influence attention, perception, and meaning. It also points out that poor or cluttered visualizations can lead to misunderstandings and decrease decision-making accuracy. Additionally, the paper discusses how visualization is used in modern project management tools. It looks at dashboards, infographics, Gantt charts, and interactive interfaces, showing how these tools aid analytical thinking and communication within project teams. Visualization is seen not just as a data presentation method, but as a way to influence cognitive processes and encourage collaboration, especially in complex and changing project settings. The study also focuses on user-oriented design, including how adaptable and interactive visual solutions can be. Effective visualization needs to consider user needs and cognitive traits. The article emphasizes integrating visualization into decision support systems and explores how new technologies, like artificial intelligence, can help improve data interpretation and management. The findings suggest that visualization effectiveness relies on matching the data presentation style with the information type and users' cognitive abilities. Well-designed visual tools enhance understanding, reduce errors, and lead to more informed and timely decision-making, ultimately improving overall project performance. In this context, particular importance is attached to selecting appropriate visualization techniques depending on the stage of the project and the type of tasks being addressed. The study also underlines that continuous improvement of visual communication practices is necessary due to the growing volume and complexity of data in project environments. Overall, visualization is positioned as a critical component of effective project management in the digital era.

Objectives: To develop and internally validate an automated system for classifying chest radiograph (CXR) reports for community-acquired pneumonia in children. Methods: We performed a retrospective single-center study using 1000 pediatric emergency department encounters (2016 to 2022) with CXR. Reports were adjudicated by two physicians as positive, negative, or indeterminate for pneumonia. We evaluated five open-source LLMs (Gemma2 9B, Gemma2 27B, Falcon3 7B, DeepSeek R1 Distill Llama 8B, and Llama3.1 8B) on a 70/30 train-test split for an outcome of pneumonia. We reported performance metrics for both three-class and binary classification (pneumonia + indeterminate vs. no pneumonia). Results: The median patient age was 4.2 years (IQR 1.7 to 10.5), and 54.4% were admitted from the ED. After clinician adjudication, 27.8% of reports were labeled pneumonia, 13.7% indeterminate, and 58.5% no pneumonia. Gemma2 9B achieved the best performance overall, with a pneumonia F1 score of 0.82 and no-pneumonia F1 score of 0.97 in three-class classification. Binary classification further improved performance (F1=0.97 for Gemma2 9B and 0.93 for 27B). Discrepancies between model and human labels often involved ambiguous language, highlighting interpretive subjectivity rather than model error. All LLMs substantially outperformed traditional NLP classifiers such as XGBoost, random forest, and logistic regression. Conclusions: Open-source LLMs accurately classified pediatric CXR reports for pneumonia. These findings support the feasibility of integrating LLMs into decision support and quality improvement pipelines to enhance radiographic interpretation and improve pediatric emergency care.

Background: Decision fatigue refers to the progressive deterioration in the quality of decisions after a prolonged period of decision‑making. Surgeons, who face high‑stakes, repetitive intraoperative choices, are particularly vulnerable. This study assessed the prevalence and perceived impact of decision fatigue among surgeons at the National Orthopaedic Hospital Dala (NOHD), Kano.  Methods: A descriptive cross‑sectional study was conducted among all 20 surgeons (4 spine surgeons, 16 orthopaedic surgeons) at NOHD between January and March 2026. A structured, self‑administered questionnaire assessed awareness of decision fatigue, intraoperative decision load, perceived decline in decision quality during prolonged lists, influence of time‑of‑day on judgment, and self‑attributed clinical errors or near‑misses. Responses were recorded on a 5‑point Likert scale (1=strongly disagree to 5=strongly agree). Data were analysed using descriptive statistics.  Results: All 20 surgeons (100% response) participated. Mean age was 44.8±8.2 years; all were male. Awareness of decision fatigue was high (90% agreed/strongly agreed). Intraoperative decision load was rated as very high by 80% of respondents. Decline in decision quality during the latter half of prolonged surgical lists was reported by 75%. Time‑of‑day influence on clinical judgment was acknowledged by 70%. Moreover, 60% attributed at least one clinical error or near‑miss to decision fatigue in the preceding 12 months. The mean overall perception score across domains was 4.1±0.7 (scale 1–5). Spine surgeons reported slightly higher scores than orthopaedic surgeons (4.3 vs 4.0, p>0.05).  Conclusion: Decision fatigue is highly prevalent among surgeons at NOHD Kano and is perceived to adversely affect clinical judgment and patient safety. Despite universal recognition of the problem, no institutional mitigation strategies exist. Structured breaks, workload distribution, and cognitive offloading strategies are urgently needed.

Responsible decision-making in pharmaceutical manufacturing increasingly occurs within complex, distributed, and rapidly evolving environments. While the European Qualified Person (QP) holds defined regulatory accountability, modern decision contexts extend beyond compliance verification and require integration of scientific understanding, lifecycle knowledge, and organisational governance. Building on recent discussions surrounding technological evolution and regulatory expectations, this review explores how professional judgement operates in environments characterised by uncertainty, accelerating timelines, and expanding organisational interfaces.Specification compliance alone often provides insufficient confidence for decision-making. Confidence increasingly emerges through process understanding, integration of multidisciplinary expertise, and development of coherent scientific narratives that connect data, process behaviour, and patient expectations. Accelerated environments also reveal how governance structures and organisational systems influence decision quality, highlighting the importance of clear accountability, trusted expertise, and independent judgement.This paper introduces the concept of patient-relevant decision quality and discusses how experienced professionals contribute to continuity of understanding across lifecycle stages. Seen through this lens, the QP represents one perspective within a broader system of responsible decision-making, where organisational maturity, principled leadership, and stewardship of judgement support scientifically grounded and ethically sound outcomes.

ABSTRACT Purpose Grounded in the dynamic capabilities view and organisational learning theory, this study examines the relative association of big data analytics-enabled dynamic capabilities (BDAEDC) on the quality of the decisions made in organisations through the mediating roles of exploitative and explorative learning and through the moderating role of top-management support for learning (TMSL). Design/Methodology/Approach Using cross-sectional survey data collected from 330 software firms in Pakistan, the study tests a moderated mediation model via partial least squares structural equation modelling (PLS-SEM) in WarpPLS 8.0. Findings It has been found that BDAEDC is a significant source of improved exploitative and exploratory learning, which are responsible for improved decision-making quality. Explorative learning ends up being a more influential mechanism. Bootstrapped mediation analysis confirms both learning orientations as key pathways. Top-management support of learning has a negative moderating effect on both the associations. Originality/Value By combining theory on dynamic capabilities with theory on organizational learning, digital capabilities for analytics integrate technological and organizational perspectives and create a new understanding of the mechanisms and boundary conditions of the effect of analytics-enabled capabilities on decision-making quality.

This study examines how Artificial Intelligence (AI) reshapes entrepreneurial opportunity recognition by transforming the cognitive processes by which entrepreneurs evaluate and interpret business opportunities. Rather than treating AI as purely informational aid, this study investigates how human heuristics and algorithmic biases jointly condition AI’s influence on entrepreneurial judgment and cognitive processing. This study adopted an explanatory sequential mixed-methods design. Survey data from 150 startup founders were analyzed using partial least squares structural equation modeling (PLS-SEM) to test the direct, mediating, and moderating relationships among AI use, cognitive decision-making quality, heuristics, algorithmic bias, and opportunity recognition quality. Semi-structured interviews with 22 entrepreneurs were then analyzed using the Gioia methodology to uncover the cognitive mechanisms underlying these relationships. The results show that AI use enhances opportunity recognition, both directly and indirectly, by improving the quality of cognitive decision-making. However, this effect is contingent on cognitive forces at both human and algorithmic levels. Human heuristics weaken the cognitive benefits of AI, while algorithmic biases, such as automation bias and anchoring, introduce additional distortions in the evaluation. Qualitative evidence reveals that opportunity recognition increasingly emerges from hybrid cognitive systems in which intuition, analytical reasoning, and algorithmic cues interact, sometimes reinforce, and sometimes undermine judgment. This study advances entrepreneurial cognition theory by conceptualizing algorithmic bias as a distinct cognitive mechanism and demonstrating how AI creates hybrid cognitive systems that reconfigure opportunity recognition. The findings move beyond the binary views of intuition vs. analytics and offer a multilevel explanation of when and how AI improves or distorts entrepreneurial judgment.

This article examines the theoretical and practical foundations of implementing the planning function in internal organizational management, taking into account contemporary challenges and the appropriateness of applying a process-based approach. It is argued that the effectiveness of the planning function is determined primarily by the ability of enterprises and organizations to quickly adjust their activities in response to the demands of various stakeholder groups and changes in the exogenous and endogenous environment in which they operate. This defines the practical approach to studying the implementation of the planning function not only from the perspective of the functional mechanism of an organization’s activities, but also through the specific steps and iterations of its implementation across the various stages of economic activity. The role of the process-based approach in internal organizational management is explored, and its impact on improving organizational performance through the integration of business processes into management activities is identified. The article describes how the planning function, within the context of the process-based approach, is implemented through various forms of business models and their manifestations in the market environment. Particular attention is paid to the transformation of approaches to implementing the planning function in wartime conditions and the need for organizations to adapt quickly to changes. It has been argued that the answers to the following questions – which cover the analysis of the consumer segment, value proposition, distribution channels, key resources, and business partners – can serve as the empirical and informational basis for selecting a particular form of implementing an organization’s planning function. The main contours of the planning function’s implementation are defined through the lens of key management actions and processes. The primary difficulties in implementing the planning function, which reduce the quality of management decisions and the overall effectiveness of the organization’s operations, are grouped.

Increasing digitalisation has expanded the availability of data in operational procurement processes. Although data-driven systems are expected to improve decision-making, decision quality does not necessarily increase under conditions of high information density. Instead, decision-makers are confronted with multiple simultaneously plausible options, resulting in decision overload. This study analyses decision overload in data-driven ordering processes from an organisational perspective. Based on a structured literature review and theory-guided case analysis, the study identifies recurring mechanisms that impair decision-making capability. The findings show that decision overload does not primarily result from data availability, but from the absence of institutionalised selection mechanisms. Three core mechanisms are identified: lack of information selection, diffusion of decision responsibility, and absence of goal prioritisation. Building on these findings, a decision architecture framework is developed that translates these mechanisms into organisational design principles. The framework includes information filtering, clear assignment of responsibilities, temporal structuring, limitation of automated decision-making logic, and institutionalised goal prioritisation. The study shifts the focus of data-driven decision-making research from data and analytics to organisational decision structures, demonstrating that decision quality depends on the institutional design of selection mechanisms.