Tool For Allocation Research Articles

Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions

The integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient’s care pathway. We identify several challenges within this ecosystem, including interoperability issues, information silos, hard-to-map patient care journeys, increased workload on health care professionals, coordination and communication gaps, and compliance with privacy regulations. These challenges are often associated with inefficiencies and diminished care quality. We also examine how emerging artificial intelligence (AI) tools have the potential to enhance the management of patient information flow. Specifically, AI can boost interoperability across diverse health systems; optimize and monitor patient care pathways; improve information retrieval and care transitions; humanize health care by integrating patients’ desired outcomes and patient-reported outcome measures; and optimize clinical workflows, resource allocation, and digital tool usability and user experiences. By strategically leveraging AI, health care systems can establish a more robust and responsive digital information ecosystem, improving care coordination and patient outcomes. This perspective underscores the importance of continued research and investment in AI technologies in patient care pathways. We advocate for a thoughtful integration of AI into health care practices to fully realize its potential in revolutionizing care coordination.

Subsidies in India: Bridging the Data Gaps

This paper looks at reported subsidy spending in India, in light of ongoing central initiatives to build transparency and accessibility of information related to financial operations and decisions of public expenditure. Subsidies have become a prominent policy tool for public resource allocation in India. However, without a clear definition and reporting of ‘subsidy’, the term tends to be loosely used to encompass many schemes and programs of the Union and state governments, including the recent rise in ‘freebies’, which need to be clearly differentiated. Moreover, many forms of financing through special securities and extra-budgetary resources have been used to finance subsidy spending, making it difficult to comprehensively define and measure subsidy expenditure in the budget and other annual accounts. These issues relate to the existence of data gaps in India’s fiscal reporting and accounts, which are a critical area of concern due to the large financial implications of subsidy expenditures. This paper identifies how subsidy spending has been accounted in India and explains the resultant data gaps that render such fiscal data inconsistent and incomparable across levels of government. The paper seeks to understand whether the present reporting of subsidy spending in the finance accounts and budget documents provide clear and comprehensive information about budget allocations, revenue sources, expenditures, and other related financial matters. It then proposes steps in the way forward to improve their transparency.

A machine learning-based approach for maximizing system profit in a power system by imbalance price curtailment

The integration of wind farms into the power grid is difficult due to unpredictable wind speed fluctuations. This variation has an impact on power generation profitability, demanding effective forecasting to lessen pricing risks. A novel optimization approach is proposed in this paper to expand social welfare and profitability while increasing revenue for power generators. This method is crucial for avoiding financial risks related to variable wind patterns. Narrowing the gap between anticipated and actual wind speeds (WSAN, WSAC) can lessen the negative impact of imbalanced prices on profitability. This reduction is necessary to enhance the economic performance of the power system. The paper endorses the use of machine learning (ML) techniques, specifically Long Short-Term Memory (LSTM) and Random Forest (RF) methods, to precisely anticipate wind speed. These models serve as analytical tools for enlightening decision-making and resource allocation in wind energy generation. According to the study, pricing imbalances have a major impression on profit calculations in deregulated systems. The empirical data show that effective forecasting can expand financial outcomes for energy companies, reducing risks and maximizing revenue. Finally, the empirical results highlight the significance of accurate wind speed forecasts and the use of advanced optimization approaches in growing the profitability and efficiency of renewable energy-dependent power systems. These findings offer a strong foundation for further research and use of machine learning techniques in the energy sector. The optimization model was accomplished with modified IEEE 14 bus test systems in this work.

Molecular matching tools for allocation and immunosuppression optimization. Ready for primetime?

Purpose of review Molecular matching continues to be an important topic in organ transplantation. Over the years, several studies – larger and smaller – supported correlations of molecular incompatibility loads and clinical outcomes. However, their practical utility for clinical decision making remains controversial and there is no consensus on the context in which they should be used. Recent findings The recent literature on molecular matching can be divided into four main areas of research: several groups present improvements of the algorithmic pipelines (1), increasing the robustness of previous findings. Further clinical evidence is reported (2) in various cohorts and other organ transplant domains, such as liver and lung transplantation. Consideration is given to the application of molecular matching in the allocation of deceased organs (3), suggesting options to improve allocation equity and utility. Furthermore, evidence is provided for personalized immunosuppression based on immunological risk (4), including infection and post graft failure management. Summary There is ample evidence that current molecular matching algorithms add value to immunologic risk stratification for organ transplant recipients. First studies on how to translate these insights into patient management with respect to organ allocation and personalized medicine are underway and require further support.

398Regulation of Takeover Bids in a Global Context

Abstract This paper describes how the regulation of corporate takeovers has developed over time in an increasingly global context, using Sweden – which is now one of Europe’s largest stock markets and one of the most active markets for corporate control – as an example. The paper discusses aspects such as the use of a Takeover Panel as a regulatory tool. It touches upon the economic aspects of corporate takeovers and their role as a corporate governance tool and a tool for the efficient allocation of resources. The policy choice of whether to allocate blocking powers to the board of the target company or to adopt a board neutrality approach is discussed. Protectionist trends as opposed to openness and free trade are also touched upon.

Move out of the land: certification and migration in China

Abstract Motivated by the emphasis on securing property rights as an important tool for the efficient allocation of resources, including land and labour, we use three rounds of China Rural Household Panel Survey data to assess the impact of a land certification programme (LCP) on the labour and land allocation in rural China. We find that the LCP results in increased rural–urban migration, more active land rental markets, as well as higher household income and consumption in rural China. These effects vary with age and are larger for younger individuals and households with heads aged 50 years or younger. Improving property rights could be a central task for the Chinese government to tackle issues related to labour and land misallocation.

Digital Leadership Impacts on a Village-owned Enterprise Performance: A Moderation Effect of Artificial Intelligence

This study investigates the impact of digital leadership on the performance of village-owned enterprises, or VOEs emphasizing the moderating effect of artificial intelligence, or AI. As digital transformation reshapes the business landscape, effective digital leadership emerges as a crucial factor for enhancing organizational performance, particularly in rural settings. This study employs quantitative surveys and interviews from VOEs across various villages with 192 research sample size. The findings reveal that digital leadership significantly correlates with improved performance metrics, such as profitability, operational efficiency, and community values. Moreover, the integration of AI technologies further amplifies these effects, providing tools for better decision-making, resource allocation, and customer interaction. The moderation analysis indicates that the presence of AI not only enhances the effectiveness of digital leadership but also facilitates innovative practices within VOEs. This research also contributes to the understanding of how digital leadership, coupled with AI, can drive sustainable growth in village enterprises, offering practical implications for policymakers and community leaders aiming to leverage technology for rural development. Future studies are suggested to explore the long-term effects of these dynamics in diverse contexts.

Achieving land degradation neutrality: land-use planning and ecosystem approach

IntroductionThe research purpose is to scientifically substantiate an integrated approach to solving the problem of land degradation, based on the idea of land degradation neutrality (LDN), taking into account ecosystem services when planning land use to maximize the conservation of natural capital. The methodological basis of the research is the provisions and principles of the concepts of sustainable development, achieving LDN, and ecosystem services, as well as the research results revealing various aspects of land use, particularly their degradation.MethodsThe following research methods are used in the paper: dialectical – to determine the cause-and-effect conditions of land degradation; analysis – to highlight the current state of land use in Ukraine and the factors that have led to land degradation; synthesis – for global trends towards achieving LDN; deduction – to explore the possibility of introducing global experience in achieving LDN in Ukraine; structural-functional analysis – to substantiate the feasibility of introducing an ecosystem approach to land-use planning to achieve LDN.ResultsAs a result of the research, the current land degradation state in Ukraine has been analyzed, and ways of achieving LDN in land-use planning through the prism of an ecosystem approach have been substantiated. Based on statistical data, the current and potential levels of arability of the territory of Ukraine have been calculated by natural-climatic zones, and the areas of eroded arable lands in Ukraine have been determined by the erodibility factor (low-eroded, mediumeroded,and highly-eroded).DiscussionFor the first time, a structural-logical scheme has been developed for organizational-economic support for the effective use of degraded and low-productive agricultural lands in the context of implementing the idea of LDN, which is a tool for rational allocation and use of degraded lands. This scheme can serve as a basis for the development of land-use planning strategies for territorial communities, for institutions, and organizations competent in the field of land management.

Assetisation as a means to solve public problems: the research excellence framework and competitive future-making

ABSTRACT In this paper, we engage with the Research Excellence Framework (REF) – the UK government’s national policy tool for competitive allocation of scarce research funding. Success on the terms of the REF provides guaranteed income for UK Universities for a 6- or 7-year period – and as a result, we suggest that the REF operates as an asset-like structure. We utilise 3 examples to build a comparative analysis of ways in which the REF is becoming organised in asset-like ways, the mechanisms involved in monetising future REF income in the present and the consequences that follow from this activity. In making projections that bring future income into the present, we suggest much is at stake. We will use the paper to argue that as a result of these practices assetization is now becoming a key means of steering University governance.

Combining Site-Specific Nutrient Allocation Tools Can Decrease Input Demands in No-Till Wheat Farming

ABSTRACT Wheat cultivated under conservation agriculture faces challenges in nutrient management due to altered soil dynamics and heavy residue loads. This often necessitates excessive nutrient applications to address the nutritional demands of the crop. Conducted at the Norman E. Borlaug Crop Research Centre during the winter seasons of 2020–21 and 2021–22, this study explored the effects of different nitrogen (N) application strategies on the ‘HD 2967’ wheat cultivar planted amidst rice residues using a super-seeder. The Nutrient Expert® (NE) combined with LCC-based N top dressing (NE+LCC) demonstrated the most optimal nutrient balance, leading to a 5.9% increase in grain yield and a 42.8% increase in dry matter accumulation compared to traditional methods. The composite SSNM treatments like NE+SPAD and NE+LCC recorded 48.9% and 41.9% higher SPAD values, respectively, as compared to RDF. The NE treatments resulted in significant savings, with a reduction of 60% in phosphorus usage and 5% in potassium. Economically, the NE treatment proved superior, showcasing a 12.7% higher net return compared to the RDF approach. The study concludes that site-specific nutrient management, facilitated by advanced tools like LCC and SPAD, when integrated with software-driven strategies, boosts wheat yield and fosters sustainable farming practices. This approach minimizes environmental impacts by optimizing nutrient applications and aligns with the sustainability development goal of responsible consumption and production.

Robust portfolio optimization model for electronic coupon allocation

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

Economic valuation of open research data: A conceptual framework and methodological approach

Abstract The economic significance of open research data is widely acknowledged, yet its quantification remains challenging. This paper presents an effective valuation instrument to help stakeholders understand and evaluate the economic benefits of open research data. By conducting a scoping review and prioritizing user engagement, this study introduces a comprehensive conceptual framework for the economic valuation of open research data. The valuation is based on economic value and willingness to pay, employing the Contingent Valuation Method (CVM). It incorporates per-use models (view, download, and request) and periodic subscription models (monthly and yearly). An empirical survey was conducted at the National Basic Science Data Center (NBSDC) in China to verify this framework. Both pricing models, comprising five distinct tactics, was supported by surveyed users. Measuring economic value by views and by year was preferred, while willingness to pay by downloads and by year was considered more reasonable. Overall, the most applicable valuation approach is on a yearly basis. Through this case study at NBSDC, specific pricing tactics were identified, and the total economic value and users’ willingness to pay were assessed. This study is arguably the first to establish a conceptual framework with pricing tactics from a user perspective. This methodological approach for economic valuation of open research data provides evidence and tools for future research, policy formulation, and resource allocation in the context of open science and innovation.

Corporate income tax reform and intra-industry resource reallocation

ABSTRACT Taxation is a crucial tool for government economic regulation and resource allocation. Our study, using data from the Chinese Industrial Enterprise Database from 1998 to 2010, highlights the significant role of corporate income tax reform in reducing resource misallocation in the industry. Employing the robust Difference-in-Differences method, we empirically confirm this effect. Our mechanism analysis reveals that the reform promotes increased competition among industry firms, resulting in enhanced resource allocation efficiency. These findings emphasize the vital role of tax policy in shaping resource allocation dynamics in Chinese market, underscoring its broader economic significance.

MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model

The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) refines emission inventories by providing detailed spatial (horizontal and vertical), temporal, and species allocations, enhancing the accuracy of CMAQ performance. Its efficient algorithm and modular design offer flexibility for managing both gridded and tabulated inventories, widely used in various sectors. In addition, the shapefiles with specific shapes supported by MEIAT-CMAQ can address the allocation challenges in transportation emissions. The evaluation of MEIAT-CMAQ, using model-ready inventories before (BASE scenario), and after allocation without (EXPR scenario) or with (EXPR-V scenario) vertical allocation, demonstrates significant improvements in the mean bias (MB) of gaseous pollutants (O₃, NO₂, CO). In both the EXPR and EXPR-V scenarios, the MB for O3 exhibits notable enhancements, with respective improvements of 5.7% and 26.9%. For NO₂, corresponding MB improvements are even more pronounced, reaching 27.6% and 61.7% in the EXPR and EXPR-V scenarios, respectively. Likewise, enhancements are observed in the MBs of CO, demonstrating increases of 8.4% and 45.2% in the EXPR and EXPR-V scenarios, respectively. Moreover, with regard to spatial accuracy, the incorporation of the MEIAT-CMAQ model yields significant improvements. Specifically, in the EXPR scenarios, spatial accuracy for O3 and NO2 demonstrates respective enhancements of 13.5% and 9.5%. Furthermore, the inclusion of vertical allocation leads to additional enhancements in CO, NO₂, and PM2.5, resulting in improvements of 17.6%, 16.6%, and 23.2%, respectively. MEIAT-CMAQ provides an efficient method for transforming coarse-resolution emission inventories into high-resolution files directly useable in the model, offering enhanced flexibility for users to select any period for generating model-ready emission files. This capability provides substantial technical support for automating processes within business departments and significantly improves the performance of high-resolution modeling and forecasting.

Package CovRegpy: Regularized covariance regression and forecasting in Python

Abstract This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.11. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression, along with a powerful portfolio allocation framework for dynamic multi-period asset investment management. The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: CovRegpy.py, CovRegpy_DCC.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These six scripts contain implementations of software features including multivariate covariance time series models based on the regularized covariance regression (RCR) framework, dynamic conditional correlation (DCC) framework, risk premia parity (RPP) weighting functions, singular spectrum analysis (SSA), singular spectrum decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting, and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. We examine, herein, two real-world case studies for actuarial practitioners. The first of these is a modification (demonstrating the regularization of covariance regression) of the original example from Hoff & Niu ((2012). Statistica Sinica, 22(2), 729–753) which modeled the covariance and correlative relationship that exists between forced expiratory volume (FEV) and age and FEV and height. We examine this within the context of making probabilistic predictions about mortality rates in patients with chronic obstructive pulmonary disease. The second case study is a more complete example using this package wherein we present a funded and unfunded UK pension example. The decomposition algorithm isolates high-, mid-, and low-frequency structures from FTSE 100 constituents over 20 years. These are used to forecast the forthcoming quarter’s covariance structure to weight the portfolio based on the RPP strategy. These fully funded pensions are compared against the performance of a fully unfunded pension using the FTSE 100 index performance as a proxy.

Development and Validation of the Bone Tumor Surgery Complexity Score

Bone tumors often manifest with non-specific symptoms such as pain and swelling, often posing diagnostic challenges. Optimal treatment requires centralized care in specialized centers, emphasizing the need for complete tumor removal and interdisciplinary collaboration. We developed the Bone Tumor Surgery Complexity Score (BT-SCS) based on a retrospective study of 501 patients. The BT-SCS, structured around patient demographics, tumor biology, and surgical parameters, categorizes surgical cases into four groups to comprehensively assess complexity. Application of the BT-SCS resulted in scores ranging from 3 to 33, with an average score of 14 ± 7.2. Patients with malignant tumors had higher scores (19.6 ± 5.2) compared to those with benign (10.0 ± 3.8) or intermediate malignant tumors (14.6 ± 7.1). Patients with pelvic tumors registered the highest scores (16.0), followed by extremities/trunk (14.3) and spinal tumors (13.6). The BT-SCS was validated against the Case Mix Index (CMI), using an independent cohort of bone and soft tissue cases. This validation process, utilizing Loess smoothing, illustrated the BT-SCS’s granular differentiation of surgical complexity, particularly in the lower-to-mid-range of case severities. The BT-SCS represents a significant shift from volume-based to complexity-based assessments in surgical care, aligning with evolving healthcare paradigms. It serves as a tool for strategic patient allocation to treatment centers, aiming to improve outcomes and benchmarking in sarcoma care. The score’s development and application in clinical practice align with the focus on patient-centered and value-based healthcare. Future enhancements, including machine learning integration and outcome data, will refine its categorization process, enhancing clinical utility.

Building a robust pharmacy research program: Reflections from a large healthcare system not affiliated with a college of pharmacy

AbstractProfessional standards for academic health care system pharmacy departments and clinical pharmacists advocate for research and scholarly activities. Developing and sustaining a research program in a clinical pharmacy department is challenging. Over the span of a decade, a robust pharmacy research program was developed in an academic medical center not affiliated with a college of pharmacy. The aim of this manuscript is to provide a framework suitable for scaling research in pharmacy departments within an academic medical center. Objectives to accomplish the aim includes describing the structure and evolution of the clinical pharmacy department research program, listing measurable outputs and achievements of the pharmacists and the program and describing the barriers encountered and solutions that were enacted to overcome them. Elements critical to building and sustaining a research program in the pharmacy department included long‐term commitment from within the department, a departmental research infrastructure, support from institutional leadership, research funding from the institution, and a growth mindset in accordance with company initiatives to support evolving research needs. A research curriculum was developed for pharmacy residents and pharmacists, created electronic tools for research proposal scientific reviews and allocation of funds, and established departmental research career development awards that allowed for protected time to pursue research and professional development. Over the first 10 years of the program, publication rates grew more than three‐fold, faculty appointments and promotions increased approximately five‐fold, and pharmacist investigators obtained extramural federal funding. Further offsetting clinical workload relative to dedicated research time remains a challenge. The experience and success at this academic medical center suggest it is feasible to develop a robust, sustainable clinical pharmacy department research program.

Dynamic capital allocation with reallocation cost

Traditional static capital allocation incurs significant reallocation costs over periods due to drastic fluctuations. We propose a new framework for dynamic capital allocation with time-varying capital constraints that incorporates reallocation costs. We obtain an analytical solution in a recursive form and demonstrate its admirable performance through simulations. The solution compromises between static allocation and expected future risk structure and strikes a balance between addressing current risk profiles and adjustment costs. It successfully provides an effective allocation tool while preserving stability.

Comparative analysis of two disaster risk assessment tools for primary health care centers: The case of Iran

AimAssessing disaster risk in primary health care (PHC) centers is essential for ensuring continuity of operations during disasters. There is a lack of internationally recognized methods for conducting this assessment in PHC centers. In Iran, the SARA tool, a modified version of the Hospital Safety Index, has been utilized, but its effectiveness is being questioned. This study aims to evaluate the SARA by comparing it with the Kaiser Permanente Hazard Vulnerability Analysis (KP HVA) tool to identify differences and potential issues. Methodology: The cross-sectional study was conducted in two stages. First, the expert panel was used to compare the two tools' instructions and inputs and extract similarities and differences. In the second phase, the outputs of the two tools were compared by implementing them in 156 PHC centers supervised by Semnan University of Medical Sciences. Data was collected using SARA and KP HVA tools and analyzed using Excel software. ResultsThe findings illustrated the disparities in the results obtained from the two tools. Although significant similarities exist in the instructions and inputs, the SARA results indicated the safety and preparedness of PHC centers without considering the hazards. On the other hand, the KP HVA results highlighted hazard risk priorities based on comparable dimensions. ConclusionThe leading cause of the differences is the multiple-hazard and all-hazard approaches of KP HVA and SARA tools. It is recommended that the SARA tool be upgraded based on the advantages of the KP HVA tool for better resource allocation.

Exploring Multi-Armed Bandit algorithms: Performance analysis in dynamic environments

The Multi-armed Bandit algorithm, a proficient solver of the exploration-and-exploitation trade-off predicament, furnishes businesses with a robust tool for resource allocation that predominantly aligns with customer preferences. However, varying Multi-armed Bandit algorithm types exhibit dissimilar performance characteristics based on contextual variations. Hence, a series of experiments is imperative, involving alterations to input values across distinct algorithms. Within this study, three specific algorithms were applied, Explore-then-commit (ETC), Upper Confident Bound (UCB) and its asymptotically optimal variant, and Thompson Sampling (TS), to the extensively utilized MovieLens dataset. This application aimed to gauge their effectiveness comprehensively. The algorithms were translated into executable code, and their performance was visually depicted through multiple figures. Through cumulative regret tracking within defined conditions, algorithmic performance was scrutinized, laying the groundwork for subsequent parameter-based comparisons. A dedicated experimentation framework was devised to evaluate the robustness of each algorithm, involving deliberate parameter adjustments and tailored experiments to elucidate distinct performance nuances. The ensuing graphical depictions distinctly illustrated Thompson Sampling's persistent minimal regrets across most scenarios. UCB algorithms displayed steadfast stability. ETC manifested excellent performance with a low number of runs but escalate significantly along the number of runs growing. It also warranting constraints on exploratory phases to mitigate regrets. This investigation underscores the efficacy of Multi-armed Bandit algorithms while elucidating their nuanced behaviors within diverse contextual contingencies.