Decision Scenarios Research Articles

Multi-modal incomplete label information three-way bidirectional decision-making: Applications of disease assessment

Disease assessment involves two stages of decision-making process. The first process is to infer the development trend of the disease through the existing judgment conditions, which is to make a positive estimation (prediction) decision. The second process is to update the judgment conditions for different disease states and optimize the clinical assessment evidence by fusing disease progression data, that is, to make a reverse evaluation (optimization) decision by reviewing historical diagnostic and treatment data to summarize the best judgment conditions for the disease characteristics. Therefore, we construct a three-way bidirectional decision method for multi-modal incomplete labeling information, considering the mixed data features with no labels and labels, and the bidirectional decision features of positive prediction and reverse optimization. Specifically, the method first introduces semi-supervised learning (SSL) into the rough sets, constructs a rough sets model based on cosine similarity relation, generates pseudo-labels for incomplete information, and explains the positive prediction decision process principle. Second, the gradient space is constructed on the predictive information system by the three-way decision-making principle, the loss function formed by the combination of KL divergence and cross entropy loss function is used to find the direction of the fastest decline in attribute information value, and the optimal decision rule and the automatic updating strategy of the threshold value are obtained. At the same time, the principle of the reverse optimization decision process is explained. Finally, multimodal information from pathological tissue images is used to evaluate clinical progress problem. A pathological tissue images dataset includes 2000 colorectal cancer and 300 kidney disease cases used for simulation experiments. The proposed three-way bidirectional decision-making model can be widely applied to actual decision-making scenarios for evaluating clinical progress of different diseases. Compared with previous studies, this method can realize objective calculation and automatic update of threshold and loss values and effectively solve complex prediction and optimization decision-making problems.

A framework for early-stage sustainability assessment of innovation projects enabled by weighted sum multi-criteria decision analysis in the presence of uncertainty.

A two-level hierarchical framework for early-stage sustainability assessment (FESSA) amongst a set of alternatives applicable from the earliest stages of process or product development is introduced, and its use in combination with an improved method weighted-sum method multi-criteria decision analysis (WSM-MCDA) in the presence of uncertainty is presented through application to a case study based upon a real-world decision scenario from speciality polymer manufacture. The approach taken addresses the challenge faced by those responsible for innovation management in the manufacturing process industries to make simultaneously timely and rational decisions early in the innovation cycle when knowledge gaps and uncertainty about the options tend to be at their highest. The Computed Uncertainty Range Evaluations (CURE) WSM-MCDA method provides better discrimination than the existing Multiple Attribute Range Evaluations (MARE) method without the computational burden of generating heuristic outcome distributions via Monte-Carlo simulation.

Reception to and Efficacy of a Serious Video Game for Correctional Intervention: Project Choices

Project Choices ( PC), a newly developed videogame for correctional intervention, consists of realistic decision-making scenarios and cognitive behavioral skills feedback. A pilot study investigated PC engagement and immersion by employing a cross-over design with a sample of 24 men on probation remanded to residential treatment. The study also examined effects of gameplay on criminogenic thinking, self-perceived criminogenic risk, and social problem-solving. As hypothesized, relative to a leisure video game ( Tetris), participants generally appeared comparably engaged by and immersed in PC. Most hypothesized effects of PC on treatment-relevant outcomes were not statistically significant; however, PC contributed to moderate to large treatment effects across most outcomes of interest. Although results are promising, PC requires further validation to determine whether it could serve as a useful adjunctive tool for practitioners hoping to further reach and engage corrections clients in criminogenic risk-reduction services. Future research directions for technology like PC are numerous and encouraged.

An Improved Weighted Evidence Combination Based on Tangent Similarity and Its Application in Decision-Making

Dempster-Shafer theory (DST) has been widely recognized across multiple disciplines for its superior handling of uncertainty compared to traditional probability theory. Nonetheless, the application of Dempster's rule in the presence of conflicting evidence can lead to outcomes that are sometimes non-intuitive. To mitigate this issue, this paper proposes a new tangent similarity measure within DST to assess the conflict between evidences. The proposed measure adheres to several key properties, enhancing its ability to accurately reflect the similarity between evidences. Additionally, an improved weighted evidence combination framework utilizing the tangent similarity measure is developed. The effectiveness of the proposed method is demonstrated through a decision-making scenario in plant disease detection.

Does Real Option Value Influence Oncologists’ Treatment Recommendations? A Survey of US Oncologists

ObjectivesSurvival benefit from anticancer treatments, even if modest, improves a patient’s chances of accessing future innovations, thereby creating real option value. There is no empirical evidence on the impact of potential future innovations on oncologists’ treatment recommendations. MethodsWe conducted a national online survey of practicing medical and hematological oncologists. We presented a hypothetical metastatic cancer patient with median survival of 6 months under 4 decision-making scenarios with varying expected efficacy and time to arrival of future innovations. We assessed the likelihood of discussing future innovations with their patients and the likelihood that future innovations would influence their current treatment recommendation, as well as factors associated with these 2 outcomes using multivariate logistic regressions. ResultsA total of 201 oncologists completed the survey. When future innovations were expected to improve survival by 6 months and be available in 6 months, 76% of oncologists were likely or very likely to discuss the innovations with their patients, and 68% reported they would influence their current treatment recommendations. A 1-month increase in the expected survival improvement of future innovation was associated with a 1.17 greater odds (95% CI 1.1-1.25) of reporting likely or very likely to discuss future innovations with their patients, whereas a 1-month increase in the expected time to arrival was associated with a 0.91 lower odds (95% CI 0.88-0.94). ConclusionsGiven that potential future innovations seem to influence oncologists’ treatments recommendations, evidence to inform clinical guidelines and value assessments should consider data on real option value impacts to support informed treatment decision making.

Evaluation decision on green degree combination of typical welding processes for large-diameter thick plates

Abstract Aiming at the environmental impact of large-diameter thick plate manufacturing scenarios in the welding process, a comprehensive evaluation method combining AHP and TOPSIS is proposed to analyze and make decisions on the green welding process suitable for large-diameter thick plates. The optimal green welding process evaluation and decision-making index system for large-diameter thick plates is constructed. The system can be flexibly applied to different large-diameter plate welding scenarios by focusing on different optimization objectives, providing welders with a more suitable green welding process. The constructed model is applied to the seashore factory welding scenarios and general welding scenarios for comparative decision-making, and the effectiveness and feasibility of the proposed comprehensive decision-making evaluation method are verified by the actual data calculation results.

Orientation and mental representations in the decision-making process: a comparison between junior and senior professional

In the workplace, professionals have an orientation to individual or group decision-making based on ideal or real representation of the group situation. However, no previous studies investigated these dimensions in professionals of different ages. A total of 216 professionals were enrolled in this study based on their work experience and seniority. Specifically, senior professionals were from different Italian consulting companies, while juniors had not yet approached the world of work, but were completing their professional education. Participants were asked to read a realistic decision-making scenario and rate their individual or group decision-making orientation, thinking of an ideal and real group situation. A stronger group decision-making orientation was found in both groups of professionals, independent of age. However, professionals appear less group-oriented when they mentally self-represent their real compared to their ideal group. Additionally, in senior professionals, a positive correlation between individual and group orientation was found in the ideal group situation. Indeed, in junior professionals, a positive correlation between individual and group orientation was found in the real situation. Therefore, it might be that becoming older and increasing professionalism level correlate with awareness of the difference between real and ideal group representations.

Toward a person-environment fit framework for artificial intelligence implementation in the public sector

Using an embedded mixed method design, we compared a nationally representative sample of US adults and a sample of US-based emergency managers (EM) on their attitudes toward artificial intelligence (AI) and their intentions to rely on AI in a set of decision-making scenarios relevant to emergency management. Emergency managers reported significantly less positive attitudes toward AI and were less likely to rely on AI for decisions compared to the nationally representative sample. Our analysis of EMs' open-ended responses explaining their choices to use or not use AI-based solutions reflected specific concerns about implementation rather than wariness toward AI generally. These concerns included the complexity of the potential outcomes in the scenarios, the value they placed on human input and their own extensive experience, procedural concerns, collaborative decision-making, team-building, training, and the ethical implications of decisions, rather than a rejection of AI more generally. Managers' insights integrated with our quantitative findings led to a person-environment fit framework for AI implementation in the public sector. Our findings and framework have implications for how AI systems should be introduced and integrated in emergency managerial contexts and in public sector organizations more generally. Public managers' perceptions and intentions to use AI and organizational oversight processes are at least as important as technology design considerations when public sector organizations are considering the deployment of AI.

Aggregation operators based on Einstein averaging under q-spherical fuzzy rough sets and their applications in navigation systems for automatic cars

This study introduces innovative operational laws, Einstein operations, and novel aggregation algorithms tailored for handling q-spherical fuzzy rough data. The research article presents three newly designed arithmetic averaging operators: q-spherical fuzzy rough Einstein weighted averaging, q-spherical fuzzy rough Einstein ordered weighted averaging, and q-spherical fuzzy rough Einstein hybrid weighted averaging. These operators are meticulously crafted to enhance precision and accuracy in arithmetic averaging. By thoroughly examining their characteristics and interrelations with existing aggregate operators, the article uncovers their distinct advantages and innovative contributions to the field. Furthermore, the study illustrates the actual implementation of these newly constructed operators in a variety of attribute decision-making scenarios employing q-SFR data, yielding useful insights. Our suite of decision-making tools, including these operators, is specifically designed to address complex and uncertain data. To validate our approach, this study offers a numerical example showcasing the real-world applicability of the proposed operators. The results not only corroborate the efficacy of the proposed method but also underscore its potential significance in practical decision-making processes dealing with intricate and ambiguous data. Additionally, comparative and sensitivity analyses are presented to assess the effectiveness and robustness of our proposed work relative to other approaches. The acquired knowledge enriches the current understanding and opens new avenues for future research.

Exploring collaborative decision-making: A quasi-experimental study of human and Generative AI interaction

This paper explores the effects of integrating Generative Artificial Intelligence (GAI) into decision-making processes within organizations, employing a quasi-experimental pretest-posttest design. The study examines the synergistic interaction between Human Intelligence (HI) and GAI across four group decision-making scenarios within three global organizations renowned for their cutting-edge operational techniques. The research progresses through several phases: identifying research problems, collecting baseline data on decision-making, implementing AI interventions, and evaluating the outcomes post-intervention to identify shifts in performance. The results demonstrate that GAI effectively reduces human cognitive burdens and mitigates heuristic biases by offering data-driven support and predictive analytics, grounded in System 2 reasoning. This is particularly valuable in complex situations characterized by unfamiliarity and information overload, where intuitive, System 1 thinking is less effective. However, the study also uncovers challenges related to GAI integration, such as potential over-reliance on technology, intrinsic biases particularly ‘out-of-the-box’ thinking without contextual creativity. To address these issues, this paper proposes an innovative strategic framework for HI-GAI collaboration that emphasizes transparency, accountability, and inclusiveness.

Football teaching and training based on video surveillance using deep learning.

The objective performance evaluation of an athlete is essential to allow detailed research into elite sports. The automatic identification and classification of football teaching and training exercises overcome the shortcomings of manual analytical approaches. Video monitoring is vital in detecting human conduct acts and preventing or reducing inappropriate actions in time. The video's digital material is classified by relevance depending on those individual actions. The research goal is to systematically use the data from an inertial measurement unit (IMU) and data from computer vision analysis for the deep Learning of football teaching motion recognition (DL-FTMR). There has been a search for many libraries. The studies included have examined and analyzed training through profound model construction learning methods. Investigations show the ability to distinguish the efficiency of qualified and less qualified officers for sport-specific video-based decision-making assessments. Video-based research is an effective way of assessing decision-making due to the potential to present changing in-game decision-making scenarios more environmentally friendly than static picture printing. The data showed that the filtering accuracy of responses is improved without losing response time. This observation indicates that practicing with a video monitoring system offers a play view close to that seen in a game scenario. It can be an essential way to improve the perception of selection precision. This study discusses publicly accessible training datasets for Human Activity Recognition (HAR) and presents a dataset that combines various components. The study also used the UT-Interaction dataset to identify complex events. Thus, the experimental results of DL-FTMR give a performance ratio of 94.5%, behavior processing ratio of 92.4%, athletes energy level ratio of 92.5%, interaction ratio of 91.8%, prediction ratio of 92.5%, sensitivity ratio of 93.7%, and the precision ratio of 94.86% compared to the optimized convolutional neural network (OCNN), Gaussian Mixture Model (GMM), you only look once (YOLO), Human Activity Recognition- state-of-the-art methodologies (HAR-SAM). This finding proves that exercising a video monitoring system that provides a play view similar to that seen in a game scenario can be a valuable technique to increase selection accuracy perception.

Approach to the Patient Considering Long-term Antithyroid Drug Therapy for Graves' Disease.

Antithyroid drugs (ATD) are the treatment of choice for the majority of patients with Graves' hyperthyroidism worldwide. However, relapse of hyperthyroidism after withdrawal of arbitrarily chosen conventional 12 to 18 months of therapy is very common. In the last 2 decades, many studies have shown that treatment with long-term ATD (LT-ATD) is effective and safe in the maintenance of euthyroidism. In addition, it has been reported that serum TSH receptor antibody may not decrease permanently before 5 to 6 years of ATD treatment, and clinical trials have shown that ≥5 years of ATD treatment is accompanied by remission in the majority of patients with Graves' hyperthyroidism. The objective of this article is to discuss the optimal time to withdraw of conventional ATD therapy, to illustrate the decision-making of the management of recurrent hyperthyroidism, to review the proper management of LT-ATD, and to generate suggestions for lifelong ATD treatment by discussing 4 scenarios of decision-making in patients with Graves' disease.

Identifying priority ecosystem services in tidal wetland restoration.

Classification systems can be an important tool for identifying and quantifying the importance of relationships, assessing spatial patterns in a standardized way, and forecasting alternative decision scenarios to characterize the potential benefits (e.g., ecosystem services) from ecosystem restoration that improve human health and well-being. We present a top-down approach that systematically leverages ecosystem services classification systems to identify potential services relevant for ecosystem restoration decisions. We demonstrate this approach using the U.S. Environmental Protection Agency's National Ecosystem Service Classification System Plus (NESCS Plus) to identify those ecosystem services that are relevant to restoration of tidal wetlands. We selected tidal wetland management documents from federal agencies, state agencies, wetland conservation organizations, and land stewards across three regions of the continental United States (northern Gulf of Mexico, Mid-Atlantic, and Pacific Northwest) to examine regional and organizational differences in identified potential benefits of tidal wetland restoration activities and the potential user groups who may benefit. We used an automated document analysis to quantify the frequencies at which different wetland types were mentioned in the management documents along with their associated beneficiary groups and the ecological end products (EEPs) those beneficiaries care about, as defined by NESCS Plus. Results showed that a top combination across all three regions, all four organizations, and all four tidal wetland types was the EEP naturalness paired with the beneficiary people who care (existence). Overall, the Mid-Atlantic region and the land steward organizations mentioned ecosystem services more than the others, and EEPs were mentioned in combination with tidal wetlands as a high-level, more general category than the other more specific tidal wetland types. Certain regional and organizations differences were statistically significant. Those results may be useful in identifying ecosystem services-related goals for tidal wetland restoration. This approach for identifying and comparing ecosystem service priorities is broadly transferrable to other ecosystems or decision-making contexts.

Smart TOPSIS: A Neural Network-Driven TOPSIS with Neutrosophic Triplets for Green Supplier Selection in Sustainable Manufacturing

Decision-making in complex environments requires advanced methodologies to manage uncertainty and indeterminacy effectively. This research introduces a novel decision-making framework that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with Neutrosophic Triplets (NTs) to address the limitations of decision-making methods in handling indeterminate information. By incorporating a frequency analysis-based ranking strategy and leveraging neural network-driven machine learning, the proposed method significantly enhances the accuracy and computational efficiency of the decision-making process. The necessity of this research stems from the growing complexity of multi-attribute decision-making (MADM) scenarios where traditional methods fall short in accurately ranking alternatives under uncertainty. The novelty lies in integrating NTs with a machine-learning approach, providing a more flexible and robust framework for MADM. The proposed method’s contributions are demonstrated through its application in green supplier selection, a critical area in sustainable supply chain management. The results reveal that the smart TOPSIS method improves decision accuracy and reduces computational complexity, making it a viable tool for broader applications. Although the proposed methodology is primarily applied to green supplier selection management, it can also be extended to real-world scenarios in various research fields.

Projecting exceedance of juvenile salmonid thermal maxima in streams under climate change: A crosswalk from lab experiments to riparian restoration

Abstract Concern over rising water temperatures for freshwater ectotherms has led to application of experimentally derived thermal thresholds to stream temperature models for estimation of streams at high risk of exceeding thermal thresholds under current and future climate conditions. We optimised an approach that links field‐relevant thermal maxima experiments to corresponding stream temperature models and identifies opportunities to reduce stream temperatures through riparian tree growth. We conducted a thermal maxima experiment on cold‐water adapted juvenile Chinook (Oncorhynchus tshawytscha) and coho salmon (Oncorhynchus kisutch) that reflected natural temperatures by using incremental temperature ramping with diel fluctuations (IT‐Dmax) and refit a regionally specific stream temperature model for British Columbia, Canada to directly relate to lab‐derived thresholds. Salmon‐bearing streams across British Columbia were categorised by threshold exceedance risk (i.e., low, moderate, high, severe) based on risk tolerance scenarios for management decision making (i.e., considering a range of prediction intervals [PIs] and stream thermal sensitivities to air temperature). We linked these results directly to riparian management and restoration actions by estimating the potential for riparian tree growth to reduce threshold exceedance risk. Lab‐derived IT‐Dmax endpoints were consistently 24°C (based on the median value between the 7‐day average of the daily maximum and the mean weekly average temperature) across acclimation treatments for both species. Under current conditions, most stream reaches (99.6% using an intermediate risk tolerance scenario) were below the thermal threshold; streams with moderate to high risk of threshold exceedance were projected to increase from 0.4% to 1.5% (total linear stream length of 6,929 km) by end of century. The risk‐averse (high sensitivity, 75% PI) and risk‐tolerant (low sensitivity, 95% PI) scenarios differed by 1,107 km of streams predicted to have a moderate to severe risk of exceeding thresholds by end of century. Maximal riparian tree growth was predicted to shift 670 km of streams from moderate to low exceedance risk under end‐of‐century conditions and intermediate risk tolerance, showing the potential for mitigation from thermal impacts. Our integrative approach tackled several key considerations in identifying streams at high temperature risk for freshwater ectotherms that to date have not been addressed. Furthermore, we showcased the delineation of spatially comprehensive estimates that identify where management mitigation and a specific restoration activity may be most needed to reduce impacts of rising temperatures on rearing salmonids across an extensive region.

Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process

A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the unit disc. This extension allows for a more nuanced representation of uncertainty. In this research, we develop Complex Polytopic Fuzzy Sets (CPoFS) and establish basic operational laws of CPoFS. Leveraging these laws, we introduce new operators under a confidence level, including the confidence complex Polytopic fuzzy Einstein weighted geometric aggregation (CCPoFEWGA) operator, the confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (CCPoFEOWGA) operator, the confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (CCPoFEHGA) operator, the induced confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (I-CCPoFEOWGA) operator and the induced confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (I-CCPoFEHGA) operator, enhancing decision-making precision in uncertain environments. We also investigate key properties of these operators, including monotonicity, boundedness, and idempotency. With these operators, we create an algorithm designed to solve multiattribute decision-making problems in a Polytopic fuzzy environment. To demonstrate the effectiveness of our proposed method, we apply it to a numerical example and compare its flexibility with existing methods. This comparison will underscore the advantages and enhancements of our approach, showing its efficiency in managing complex decision-making scenarios. Through this, we aim to demonstrate how our method provides superior performance and adaptability across different situations.

Many-Objective Whale Optimization Algorithm for Engineering Design and Large-Scale Many-Objective Optimization Problems

In this paper, a novel Many-Objective Whale Optimization Algorithm (MaOWOA) is proposed to overcome the challenges of large-scale many-objective optimization problems (LSMOPs) encountered in diverse fields such as engineering. Existing algorithms suffer from curse of dimensionality i.e., they are unable to balance convergence with diversity in extensive decision-making scenarios. MaOWOA introduces strategies to accelerate convergence, balance convergence and diversity in solutions and enhance diversity in high-dimensional spaces. The prime contributions of this paper are—development of MaOWOA, incorporation an Information Feedback Mechanism (IFM) for rapid convergence, a Reference Point-based Selection (RPS) to balance convergence and diversity and a Niche Preservation Strategy (NPS) to improve diversity and prevent overcrowding. A comprehensive evaluation demonstrates MaOWOA superior performance over existing algorithms (MaOPSO, MOEA/DD, MaOABC, NSGA-III) across LSMOP1-LSMOP9 benchmarks and RWMaOP1-RWMaOP5 problems. Results validated using Wilcoxon rank sum tests, highlight MaOWOA excellence in key metrics such as generational distance, spread, spacing, runtime, inverse generational distance and hypervolume, outperforming in 71.8% of tested scenarios. Thus, MaOWOA represents a significant advancement in many-objective optimization, offering new avenues for addressing LSMOPs and RWMaOPs’ inherent challenges. This paper details MaOWOA development, theoretical basis and effectiveness, marking a promising direction for future research in optimization strategies amidst growing problem complexity.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.

Impact of Prehospital Ultrasound Training on Simulated Paramedic Clinical Decision-Making.

When used appropriately, focused limited-scope ultrasound exams could potentially provide paramedics with accurate and actionable diagnostic information to guide prehospital decision-making. In this study we aimed to investigate the impact of a 13-hour prehospital ultrasound training course on the simulated clinical decision-making of paramedics as well as their ultrasound skills, knowledge, and self-confidence. We evaluated the ultrasound competence of 31 participants using post-course written and practical assessments. Written clinical decision scenarios were administered pre- and post-training. Post-training scenarios included an uninterpreted ultrasound clip to aid decision-making. Scenarios included extended focused assessment with sonography in trauma, pulmonary exam, and focused echocardiography combined with carotid pulse check exams. Correct answers to scenarios were defined as those selected by a veteran emergency physician. Participants also indicated their confidence in each of their decisions using a Likert scale. Training yielded a statistically significant increase in both mean scenario score (35.5% absolute increase) and mean participant self-confidence (15.8% relative increase), across all exam/decision types assessed (P ≤ 0.001). The focused pulmonary exam yielded the largest increase in both mean score improvement (59.7% absolute increase) and paramedic confidence in their decisions (28.6% increase). Trained paramedics can perform focused ultrasound exams and accurately interpret and apply actionable exam findings in the context of written scenarios. Analysis through our model characterized the theoretical clinical yield of each prehospital ultrasound exam and demonstrated how each exam may provide improved decision accuracy in several specific simulated clinical contexts. These results provide support for growing evidence that focused limited-scope ultrasound may be an effective prehospital diagnostic tool in the hands of trained paramedics.

Трансформация подходов к управлению в условиях цифровизации

This article delves into the evolution of conventional management approaches in response to the escalating influence of digital technologies on the operations of modern organizations. Amidst fierce competition, businesses cannot afford to disregard the new paradigm: the implementation of data-driven management methods, the utilization of Decision Support Systems (DSS), forecasting, and scenario analysis exemplified by digital twins, among others. Fundamental management science does not have time to adapt to the rapid development of practical management and its new tools. The aim of this article is to substantiate the necessity of reevaluating the amassed knowledge base and experience in management to foster a new scientific underpinning for businesses in the realm of digital transformation. The importance of the managerial aspect of digitalization is the central subject of the study. This endeavor will appeal to company executives, managers, and individuals engaged in management and digital transformation issues.