Quality Of Decisions Research Articles

The IMproving treatment decisions for Patients with AortiC stenosis Through Shared Decision Making (IMPACT SDM) Study: study protocol for a cluster randomized stepped wedge trial

BackgroundThe American College of Cardiology, American Heart Association, and Centers for Medicare and Medicaid Services recommend shared decision-making (SDM) for patients with severe aortic stenosis choosing between transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR). Although tools such as patient decision aids (DAs) and training in SDM have been shown to improve SDM, implementation of SDM and DAs is limited. The IMproving treatment decisions for Patients with AortiC stenosis Through Shared Decision Making (IMPACT SDM) study aims to (1) determine the effectiveness of the interventions (a DA and clinician SDM training) in achieving SDM (primary outcome) and improving the quality of decisions about aortic valve replacement, (2) determine the reach of the DAs and adoption of training, and (3) explore potential mechanisms of effectiveness and implementation at the patient-, clinician-, and clinic-level.MethodsThe study is a hybrid type II effectiveness-implementation study using a cluster randomized batched stepped wedge trial with 8 sites across the USA. Eligible patients will be surveyed before and after visits with the heart valve team; clinicians will be surveyed after visits. Reach of DAs and adoption of training will be tracked. Clinicians will be interviewed regarding barriers and facilitators to implementation.DiscussionThe IMPACT SDM Study seeks to provide evidence of the ability of the interventions to improve SDM and decision quality, and also to shed light on barriers and facilitators to SDM implementation to promote future implementation efforts.Trial registrationClinicalTrials.gov NCT06171737. Registered on December 15, 2023.

Experience Minimizes the Pull-to-Center Effect in Newsvendor Decisions

Problem definition: The asymmetric pull-to-center effect for newsvendors is a robust finding in operations, and understanding why newsvendors make suboptimal decisions is key for identifying ways to improve decision-making quality in this critical task. Although prior studies have indicated that experience does not substantially mitigate the pull-to-center effect, the experience levels achieved in those previous studies are limited in comparison with the experience level that a near-continuous time environment can provide. Methodology/results: We conduct a set of laboratory experiments using a near-continuous time environment to determine the effect that extensive experience has on newsvendor behavior and the extent to which resultant learning is transferable across conditions. Observed behavior clearly demonstrates that the pull-to-center effect is substantially reduced, if not eliminated, with sufficient experience and that this learning can have positive spillovers to more traditional settings. Further, the experiments suggest that it is the repeated feedback regarding a given inventory decision rather than the ability to explore many strategies that drives improved decision making. Managerial implications: Experience can mitigate the pull-to-center effect, and near-continuous time environments can be an effective training tool for gaining such experience. Funding: This work was supported by the Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky [Grants VEGA 1/0660/23 and VEGA 1/0721/24] and the Slovak Research and Development Agency [Grant APVV-19-0573]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0439 .

Advanced Design Validation Planning Techniques for Commercial Vehicle E-Axles

<div>Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant.</div> <div>Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in the e-axle design validation plan (DVP), particularly in relation to the start of production (SOP) decision-making quality gates.</div> <div>Many DVPs incorporate tests in a specific order to enhance the validation impact of each sample and to guarantee that preconditioning tests are implemented when necessary. An instance of preconditioning is subjecting a system to thermal cycling. Thermal cycle preconditioning ensures that the sample accurately represents a real-world system when undergoing sealing-related tests, such as ingress protection testing according to ISO 20653 [<span>1</span>]. Assessing the necessary thermal cycling to “bed-in” a sample would be highly elaborate using a physical sample, but it is a straightforward aspect to extract from modern finite element analysis (FEA). A case study will be presented to evaluate the minimum required thermal cycling and determine the cost savings for the DVP.</div> <div>Furthermore, the validation supported by simulations will be showcased in the context of accelerated lifetime testing. Often, accelerating such tests necessitates a balance between the distribution of damages and the overall testing duration. By incorporating safety factors from simulations as an initial input, the distribution of damages can be adjusted to allow for some over-testing on more secure components, thereby reducing the overall testing time.</div> <div>Efficiency assessment will additionally be considered for virtual validation due to the complexity of the measurement and data acquisition, contributing to measurement uncertainty. Furthermore, simulation-based validation will be considered for the case of lubrication testing, which can be hampered by a largely qualitative assessment criteria.</div> <div>Overall, these scenarios will be evaluated for their potential to streamline the overall DVP process in terms of testing time, sample quantity, and critical path duration. This underscores the potential benefits of leveraging advanced DVP techniques for cost and time savings.</div>

Investigating the Impact of the Hospital Information System on the Performance of Management Units at Pasteur Hospital in Bam from the Perspective of Managers

Background: The Hospital Information System (HIS) is a comprehensive information management system in hospitals that plays a significant role in the quality of managers' decisions. Therefore, this study aimed to examine the impact of the HIS on the performance of management units at Pasteur Hospital in Bam from the managers' perspective in the year 2023. Method: The present study was an applied, descriptive, and cross-sectional study conducted in 2023. The performance of management units was examined in three clinical, human resources, and financial indicators. The study population consisted of all the managers at Pasteur hospital in Bam at three levels: executive, middle, and top management, who were entered into the study through a census (38 individuals). Information was collected using a standardized questionnaire, and for data analysis, SPSS 27 software and appropriate descriptive and analytical statistical tests (independent t-test and ANOVA) were used. Results: Participating managers were 76.30 % women and 23.70 % men. 73.70 % of them had a bachelor's degree and most of them were in the category of operational and middle managers. From the point of view of the managers of the HIS, the greatest impact on the indicators of the trend of treatment activities (28.60 ± 5.35), cost (50.00 ± 3.60), productivity and training (32.35 ± 12.81) and the least impact on the indicators of information exchange (25.50 ± 4.67), income (32.33 ± 0.57), salary and wages (10.50 ± 5.00). In this study, three components—clinical, financial, and human resources—were examined separately by gender, education level, and management levels. The results of the independent t-test indicated that there was a significant difference only in the clinical component based on gender (p = 0.04) Conclusion: To enhance the effectiveness of the HIS on managers' performance, it is essential to first conduct a comprehensive needs assessment of all management units. This needs assessment should identify the essential tasks and specific needs of each unit, and based on that, the enhancement of the HIS should be aligned with these needs. Moreover, paying special attention to the dimensions and indicators that receive less impact from this system can help improve the overall performance of the system and enhance its effectiveness in all aspects.

The Effect of Digitalization on the Sustainability of Accounting Practices in the Financial Industry

This research aims to examine the effect of digitalization on the sustainability of accounting practices in the financial industry. Using a qualitative approach, this research conducted case studies on several leading companies in the financial sector. We collected data on the implementation of digital technologies in accounting practices through in-depth interviews, participatory observation, and document analysis. The research findings show that digitalization brings significant changes in accounting practices, especially with regard to improving efficiency, transparency, and data accuracy. Research has proven that the integration of technologies like cloud-based accounting information systems, big data, and artificial intelligence enhances the quality of financial decision-making. In addition, digitalization also has a positive impact on environmental sustainability through reduced paper usage and energy efficiency. However, the research also reveals challenges, such as the need to upskill accountants, data security issues, and adapt to regulatory changes. The findings provide important insights for practitioners and policymakers in responding to the dynamics brought about by digitalization in accounting. This research contributes to the literature relating to the interaction between technology and accounting practices by providing an in-depth understanding of how financial firms adapt and utilize technological innovations for their business sustainability. The practical implications of this research include recommendations for the development of policies that support the integration of technology in accounting as well as the development of appropriate human resources.

Challenges Faced by Corporate Management Accounting

With the rapid advancement of globalization and technological innovation, the field of corporate management accounting is facing unprecedented challenges. This paper delves into the primary difficulties encountered by management accounting in the current environment, including the impacts of technological advancements, the complexities of globalization, and increasingly stringent regulatory requirements. Through a detailed literature review and typical case studies, this research comprehensively analyzes the specific effects of these challenges on corporate operations and proposes practical solutions. These strategies include adopting advanced data analysis and automation tools to enhance the accuracy and timeliness of accounting information; strengthening the cultivation of cross-cultural management capabilities to ensure effective communication and decision-making in diverse cultural contexts; and establishing robust regulatory compliance monitoring systems to maintain compliance in a complex regulatory environment. This paper aims to provide theoretical and strategic references for corporate management accounting practices, helping businesses optimize their management accounting operations, improve decision-making quality and operational efficiency, and achieve sustainable development and competitive advantage.

Computers and chess masters: The role of AI intransforming elite human performance.

Advances in Artificial Intelligence (AI) have made significant strides in recent years, often supplementing rather than replacing human performance. The extent of their assistance at the highest levels of human performance remains unclear. We analyse over 11.6 million decisions of elite chess players, a domain commonly used as a testbed for AI and psychology due to its complexity and objective assessment. We investigated the impact of two AI chess revolutions: the first in the late 1990s with the rise of powerful PCs and internet access and the second in the late 2010s with deep learning-powered chess engines. The rate of human improvement mirrored AI advancements, but contrary to expectations, the quality of decisions mostly improved steadily over four decades, irrespective of age, with no distinct periods of rapid improvement. Only the youngest top players saw marked gains in the late 1990s, likely due to better access to knowledge and computers. Surprisingly, the recent wave of neural network-powered engines has not significantly impacted the best players - at least, not yet. Our research highlights AI's potential to enhance human capability in complex tasks, given the right conditions, even among the most elite performers.

Blood pressure measurement technique in clinical practice in the NHS Greater Glasgow and Clyde.

Blood pressure (BP) measurement is a common procedure conducted in various disciplines and is widely available on clinical reports. The diagnosis and management of hypertension require reliable measurement of BP in outpatient clinics. Published studies suggest the standardised method for BP measurement is difficult to apply in routine clinical practice. This study aimed to assess the current practice of BP measurement in outpatient clinics in relevant secondary care clinical specialties across the 15 separate hospital sites of the NHS Greater Glasgow and Clyde region (population 1.2 million) compared to the recommended standardised method. An online questionnaire was developed and disseminated to the supervising clinician of each of 268 regular outpatient clinics. The questionnaire focused on the standardised BP method (patient preparation, environment, and BP measurement technique). The questionnaire was returned for 110 clinics. 73 (66.4%) of the participating clinics measure BP routinely and these formed the basis for further analysis. 3 clinics (4.1%) apply all components of the standardised BP method. 5 (6.9%) clinics deliver advice to patients prior to clinic attendance on how to prepare for BP measurement. 61 (83.6%) of participating clinics have a dedicated quiet environment for BP measurement. 50 (68.5%) clinics always place the cuff on bare upper arm and 63.0% use a cuff size appropriate to upper arm circumference. In a wide range of secondary care out-patient clinic settings, we found that BP measurement rarely adheres to the recommended standards. This has important implications for the quality of treatment decisions that are based on BP measurement.

The Impact of Pressure on Risk Evaluation in Decision-Making: A Case Study of Three Major Accidents

Decision-making is critical in organizational and managerial activities, and stress is an important factor influencing decision-making. This study examines how stress affects risk assessment and decision-making by analyzing three major accident cases (the Fukuchiyama Line derailment, the NASA Challenger STS-51L accident, and Boeing 737 Max 8 design problems). In the Fukuchiyama Line accident, the driver ignored the risk of speeding the train for fear of company penalties. In the Challenger accident, NASA ignored the risk of a leaking O-ring due to political, media, and internal cultural pressures. In the Boeing 737 Max 8 accident, Boeing ignored potential risks in the design of the MCAS system and a single sensor in an effort to catch up with competitor Airbus. This study points out that pressure can lead decision makers to ignore risks with disastrous consequences and suggests that the quality of decision making can be improved by information gathering, seeking expert advice and taking known issues seriously.

Medical emergencies in hospital: The role of treatment escalation plans in out-of-hours decision-making.

Out-of-hours medical emergencies are common in acute hospitals, and are often attended by staff who are unfamiliar with the patient's clinical background. Information in the hospital notes may or may not include guidance about how best to deal with clinical deterioration. The use of Treatment Escalation Plans (TEPs) attempts to address these deficiencies. This was an observational, questionnaire-based evaluation of the experience of trainee hospital doctors who attended out-of-hours medical emergencies. Sixty-five medical emergencies were evaluated by a total of 38 doctors. Thirty patients had a TEP, attended by 20 doctors, and 35 without a TEP were attended by 27 doctors. Information from medical notes to facilitate decision-making was deemed sufficient by the attending doctor in 77% of patients with a TEP, compared to 54% of patients with no TEP. Only 10% of patients with a TEP had their treatment escalated at the time of the call, whereas this occurred in 20% of patients without a TEP (NS). A TEP was deemed 'somewhat' or 'very' helpful by trainees in 21/30 of the cases who had one. In 27/35 cases who did not have a TEP, trainees deemed that it would have been helpful to have had one. Twelve of 38 participants (32%) considered that there was a discrepancy between what was 'expected' and what they considered to be 'right' when managing out-of-hours emergencies. TEPs have an important role in delivering key information that shapes out-of-hours decision-making in deteriorating patients. Both objective outcomes and the subjective quality of medical decision-making are enhanced when a TEP is available.

Dynamic Decisions of Quality and Goodwill in a Two-Echelon Supply Chain with Delay Effect

Improving product quality is an effective way for a company to gain goodwill as well as to increase sales. In this paper, the focus is on a two-echelon supply chain consisting of a manufacturer and a retailer. Considering the delayed effect of quality improvement on product quality, the differential equations on the levels of quality and goodwill as the state variables are established to explore the impact of delay time on members’ strategy inputs, supply chain profits and performance under decentralized and centralized decision models. Additionally, in this paper, how different profit maximization scenarios affect supply chain revenue under decentralized models is examined. The equilibrium solutions are obtained by numerical simulation. The results show that both quality improvement efforts and marketing efforts are decreasing functions of delay time, and the delay effect affects the motivation of supply chain members’ strategic inputs. Importantly, supply chain profit under centralized decision is not always higher than that under decentralized decision when the manufacturer’s concern degree and delay time are taken into account. The Rubinstein bargaining model is then used to optimize the profit distribution between the manufacturer and the retailer under centralized decision. In this paper, it is also identified that the longer the delay time, the lower the levels of quality and goodwill. The dual effect of delay effect and concern degree will exacerbate the negative impact on the levels of quality and goodwill.

System and Organization Controls (SOC) for Cybersecurity as a Nonaudit Service

SUMMARY As cybersecurity risk and disclosure requirements increase, companies are looking to audit practitioners to provide System and Organization Controls (SOC) for Cybersecurity assurance services. Companies can choose to use the same firm as their financial statement auditor (joint provisioning) or a separate assurance provider. This article summarizes a study examining the effects of joint or separate provisioning of this service and cybersecurity incidents on investors’ perceptions of audit quality and investment decisions (Perols and Murthy 2021). The study finds that when the service is jointly provisioned, the negative signal of a subsequent cybersecurity incident can reverse investors’ positive perceptions of auditor competence, increase investors’ sensitivity to potential independence impairments, and result in lower perceptions of audit quality. Overall, the study finds that investors can be less willing to invest in a company when the service is jointly compared to separately provisioned. This article highlights implications for companies, practitioners, and regulators.

DeepMulticut: Deep Learning of Multicut Problem for Neuron Segmentation From Electron Microscopy Volume.

Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NP-hard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver-the greedy additive edge contraction (GAEC)-to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut.

A generalized TODIM approach based on information measurement and consensus building model in probabilistic hesitant fuzzy environment

Information measurement and decision-making methods are two important aspects in the application of probabilistic hesitant fuzzy elements (PHFEs), and these issues are crucial for improving decision quality and enhancing the applicability of PHFSs. This paper focuses on the entropy measure and score function of PHFEs, and then proposes a generalized TOIDM method based on information measurement and consensus building. In this contribution, we develop a novel non-probabilistic entropy measure by two non-fuzzy elements and a novel score function based on sigmoid function. The novel non-probabilistic entropy of PHFEs can comprehensively reflect the dynamic changes of fuzzy uncertainty and hesitant uncertainty, and provide a new axiomatic framework. The novel score function of PHFEs can synthesize multiple information effectively with strong integrity. In the sequel, the two proposed information measurement methods are compared with existing methods to further verify the validity of the developed methods. Eventually a consensus building model is constructed based on the proposed information measurement method, and then a generalized TODIM approach based on information measurement and consensus building is proposed for probabilistic hesitant fuzzy environment. The developed approach is demonstrated with an emergency rescue planning decision-making problem. Sensitivity analysis and comparative analysis are employed to verify the validity and stability of the approach and information measures proposed in this paper. Based on the findings of this study, we conclude that the proposed methods effectively enrich the existing information measurement methods and decision-making methods in the field of probabilistic hesitant fuzzy.

AI on The Bench: The Future of Judicial Systems in The Age of Artificial Intelligence

This in-depth research explores the emerging relationship between artificial intelligence (AI) and legal systems by addressing key questions and understanding the evolution of global justice systems. This study focuses on the role of AI in strengthening the efficiency and objectivity of the judiciary, especially through the application of AI as judges in countries such as China and Estonia. This research aims to systematically analyse these developments, examining how AI is being integrated into justice systems in different parts of the world with challenges related to ethics, accountability, and human rights. The study results show that the integration of AI in the legal system brings increased efficiency and potential for transparency but also raises serious concerns about bias in AI algorithms, limitations in interpreting complex laws, and the impact on human rights principles. The main findings of this research show that the integration of AI in the legal system contains great potential for transformation but also requires a careful approach. While AI can improve the efficiency and quality of decision-making, it is important that AI is developed and implemented within a solid legal and ethical framework that respects human rights and maintains the justice system's integrity. This research emphasizes the need to consider each country's unique legal, cultural, and social context when adopting AI into their legal systems.

Enhancing managerial decisions through strategic intelligence: The contribution of advanced information retrieval systems

This study explores the comparative impact of classic and advanced information retrieval sys- tems (IRS) on strategic intelligence and decision-making quality within organizations. Classic IRS, rooted in keyword-based searches and basic indexing, have supported decision-making by providing fast access to relevant data. However, their limitations in contextual depth, adaptabil- ity, and capacity to handle complex, unstructured information often restrict their effectiveness in today’s dynamic business environments. Advanced IRS, on the other hand, utilize techniques such as natural language processing, machine learning, and semantic analysis to deliver richer, contextually relevant insights. These systems enable a more comprehensive understanding of complex information landscapes, aligning with strategic goals and supporting proactive decision- making. The paper’s analysis highlights that while advanced IRS offer substantial advantages in data precision, strategic alignment, and responsiveness, they also entail higher costs and tech- nical requirements. The findings provide actionable insights for organizations in selecting and implementing the most suitable IRS to enhance decision quality, considering factors like orga- nizational goals, resources, and ethical considerations. This research contributes to the body of knowledge on strategic intelligence by examining the evolution of IRS and their implications for organizational success.

An Intelligent Maneuver Decision-Making Approach for Air Combat Based on Deep Reinforcement Learning and Transformer Networks

The traditional maneuver decision-making approaches are highly dependent on accurate and complete situation information, and their decision-making quality becomes poor when opponent information is occasionally missing in complex electromagnetic environments. In order to solve this problem, an autonomous maneuver decision-making approach is developed based on deep reinforcement learning (DRL) architecture. Meanwhile, a Transformer network is integrated into the actor and critic networks, which can find the potential dependency relationships among the time series trajectory data. By using these relationships, the information loss is partially compensated, which leads to maneuvering decisions being more accurate. The issues of limited experience samples, low sampling efficiency, and poor stability in the agent training state appear when the Transformer network is introduced into DRL. To address these issues, the measures of designing an effective decision-making reward, a prioritized sampling method, and a dynamic learning rate adjustment mechanism are proposed. Numerous simulation results show that the proposed approach outperforms the traditional DRL algorithms, with a higher win rate in the case of opponent information loss.

Spending Review Integration Model in Budget Decision Making at the Ministry of Health of the Republic of Indonesia

The aim of this study is to identify an effective model for integrating spending reviews into the budget decision-making process. Using a qualitative approach, the study collects data through interviews and documentation. The findings suggest that the budget decision-making process would benefit from implementing an integration model known as the Comprehensive Monitoring and Evaluation Model of Budget Management. This model facilitates a more thorough review and oversight of budget allocations, ensuring better-informed decision making. The study recommends further research to assess the impact of this model on the quality of budget decision making, particularly focusing on how it enhances the decision-making process and improves budget management outcomes.

Exploring Respectful Inquiry in Leader-Team Interactions: The Differential Effect of Leader Question Asking and Listening on Team Interaction Dynamics in Decision Making

Despite extensive research streams on leadership and team processes, there is a surprising paucity of studies at their intersection. Both research streams share an increasing attention to the social interactions at the core of these phenomena. Leveraging this behavioral lens, this study draws on respectful inquiry theory to explore how specific leader communication behaviors affect team interaction dynamics during decision-making, as one important team process. We conducted a laboratory study with 22 four-person teams and a confederate leader who engaged in a hidden profile task in a personnel selection scenario. We manipulated the leader’s question asking behavior (open questions vs. statements only) and listening behavior (listening attentively vs. not listening) and randomly assigned teams to one of the four conditions. Team interactions were video-recorded and analyzed at the micro-level of communication. Specifically, we explored how leader communicative behaviors affected (1) the quality of team decision-making, (2) the conversational structure (via speaker turns), and (3) constructive communication patterns. We found that team’s yielded the lowest performance in the “disrespectful inquiry”-condition (i.e., asking questions but not listening). This condition was also characterized by increased levels of interaction amongst team members that could be interpreted as an attempt to compensate for the lack of functional leadership. By adopting a consistent, micro-level behavioral perspective, our findings bridge the literature of leadership and team interactions and suggest an update to extant theorizing on leadership substitutions.

A next generation probabilistic approach to analyze cancer patients data with inference and applications

This study addresses the critical challenges faced in cancer care, particularly in predicting survival times for patients with lung cancer and acute myeloid leukemia. Despite recent advancements in medical science, existing models often fall short in accurately capturing disease progression, leading to less effective clinical decision-making, and compromised patient outcomes. The need for advanced predictive models is urgent to improve survival time forecasts and enhance treatment strategies. In response to this, we introduce a novel probabilistic approach, the New Weibull (NEWE) model, which is part of a newly generated class designed to model cancer patient data more effectively. Our methodology includes using seven well-known estimation methods, each rigorously evaluated for consistency through Monte Carlo simulation studies focused on key metrics such as absolute bias, mean square error, and mean relative error. The datasets analyzed include survival times for twenty acute myeloid leukemia patients, 121 breast cancer patients from 1929 to 1938, 33 patients with acute myelogenous leukemia, data from eighteen individuals who died from causes unrelated to cancer, and survival times of advanced lung cancer patients undergoing standard chemotherapy. The NEWE model outperformed competing models, particularly in Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests, with significantly higher p-values. These findings highlight the NEWE model’s potential to transform predictive oncology by offering more precise survival time predictions, improving the quality of care and decision-making in cancer treatment.