Resource Allocation Decisions Research Articles

The Joint Impact of Enhanced Segment Comparability and Competitive Views on Managers’ Resource Allocation Decisions: Pre- and Post- Updated Segment Reporting Standard (ASU 2023-07)

The Joint Impact of Enhanced Segment Comparability and Competitive Views on Managers’ Resource Allocation Decisions: Pre- and Post- Updated Segment Reporting Standard (ASU 2023-07)

Evaluation of the use of modelling in resource allocation decisions for HIV and TB

IntroductionGlobally, resources for health spending, including HIV and tuberculosis (TB), are constrained, and a substantial gap exists between spending and estimated needs. Optima is an allocative efficiency modelling tool that...

Evaluation of the influence of trastuzumab therapy on serum levels of HER-2 protein and breast cancer cell lines.

Breast cancer is a complex disease characterised by abnormal cell growth in breast tissue. Trastuzumab is a targeted therapy that inhibits the HER-2 receptor and suppresses tumour growth. We aimed to determine if the clinical course of the disease could be predicted by early changes in serum levels of human epidermal growth factor receptor 2 (HER-2/neu) following trastuzumab-based therapy. The study enrolled 120 women, divided into an experimental group (60 breast cancer patients receiving trastuzumab) and a control group (60 healthy women). Serum samples were collected before each weekly trastuzumab treatment. In addition, human breast cancer cell lines MCF-7 and AMJ13 were cultured in vitro and treated with trastuzumab. The study assessed cell viability using a cytotoxicity assay (methyl thiazolyl tetrazolium) and measured HER-2 protein levels. The half maximal inhibitory concentration (IC50) was determined to evaluate the effect of trastuzumab on breast cancer. The results showed that breast cancer patients had significantly lower serum levels of HER-2 compared to the control group. The cytotoxicity assay demonstrated that increasing trastuzumab concentration enhanced growth inhibition and cytotoxicity in the cell lines. There was a significant difference in IC50 between the MCF-7 and AMJ13 cell lines. The study provides valuable insights into the effects of trastuzumab on serum HER-2 levels and breast cancer cell lines. These findings have implications for resource allocation and treatment decisions in breast cancer management. By understanding the impact of trastuzumab on HER-2 levels and tumour cells, healthcare professionals can make more informed decisions regarding therapy options for patients.

Machine Learning Predictive Model for Septic Shock in Acute Pancreatitis with Sepsis.

Acute pancreatitis (AP) progresses to septic shock can be fatal. Early identification of high-risk patients and timely intervention can prevent and interrupt septic shock. By analyzing the clinical characteristics of AP with sepsis, this study uses machine learning (ML) to build a model for early prediction of septic shock within 28 days of admission, which guided emergency physicians in resource allocation and medical decision-making. This retrospective cohort study collected data from the emergency departments (EDs) of three tertiary care hospitals in China. The dataset was randomly divided into a training dataset (70%) and a testing dataset (30%). Ten ML classifiers were utilized to analyze characteristics of AP with sepsis in the training dataset upon admission. Results were evaluated through cross-validation analysis. The optimal model was then tested on the testing dataset without any parameter modifications. The ML model was evaluated using the receiver operating characteristic curve (ROC) and compared to scoring systems through the DeLong test. A total of 604 AP patients with sepsis were included in this study. The auto-encoder (AE) model based on mean normalization, Pearson correlation coefficient (PCC), and recursive feature elimination (RFE) selection, achieved the highest Area Under the Curve (AUC) on the validation dataset (AUC 0.900, accuracy 0.868), with the AUC of 0.879 and accuracy of 0.790 on the testing dataset. Compared to the Sequential Organ Failure Assessment (AUC 0.741), quick Sequential Organ Failure Assessment (AUC 0.727), Acute Physiology and Chronic Health Evaluation II (AUC 0.778), and Bedside Index of Severity in Acute Pancreatitis (AUC 0.691), the AE model showed superior performance. The AE model outperforms traditional scoring systems in predicting septic shock in AP patients with sepsis within 28 days of admission. This assists emergency physicians in identifying high-risk patients early and making timely medical decisions.

FINANCIAL IMPLICATIONS OF MOTIVATION ON EMPLOYEE PERFORMANCE IN THE PUBLIC SERVICE: A CASE STUDY OF NATIONAL HOSPITAL INSURANCE FUND

The Salaries and Remuneration Commission's continuous review of state and public officers' salaries, aimed at enhancing morale and productivity, has not translated into improved public service delivery, as evidenced by public dissatisfaction with diminishing service quality and a declining number of public servants. This study investigates the impact of motivation on employee performance in the public service, using the National Hospital Insurance Fund (NHIF) as a case study. Specific objectives include assessing the effects of compensation, career development opportunities, retirement benefits, and employee recognition on NHIF employee performance. Rooted in theories such as Hierarchy of Needs, Total Reward, Theory of Work Adjustment, and Expectancy Theory of Motivation, this study adopts a descriptive research design. Targeting administrative staff at NHIF Nairobi, the study population comprises 230 staff, and a stratified simple random sampling method is used. Primary data is collected through questionnaires, validated through validity and reliability tests, and analyzed quantitatively using descriptive statistics. The findings reveal that organizations offering competitive salaries attract skilled and high-value employees, contributing significantly to organizational performance. Similarly, organizations investing in training, clear growth paths, mentorship, and meaningful tasks cultivate a highly motivated workforce. The study recommends that NHIF management implement fair, performance-based compensation, regularly reward highperforming employees, provide career development programs, and foster a culture of non-monetary recognition for positive contributions. This research is pertinent to NHIF policymakers, the Central Government of Kenya, other public service sectors, human resource practitioners, and researchers. By exploring the financial implications of motivation on employee performance, the study provides insights for policymaking, resource allocation, and strategic decision-making in public service organizations.

Relationship between Strategic Management Practices and Performance of Agro-Veterinary Based Industries: A case Study of Highchem Agro-Veterinary Division

The general aim of this study was to examine the relationship between strategic management decisions and performance in HighChem Agro-veterinary Division. The objectives of the study were to establish the effect of strategic innovation on performance of the Agro-veterinary industry; to investigate the impact of effective cost leadership on the performance of Agro-veterinary based industry; to examine how product differentiation affects the performance of Agro-veterinary based industry; to establish the effects of resource allocation on of the performance of Agro-veterinary based industry. The study has been anchored on three major theories. These theories are the Porter’s framework of competitive advantage; Resource-based theory and blue ocean theory. The study adopted a descriptive cross-sectional analysis. The study’s target population comprised of HighChem Agro-Veterinary Division employees and their distributors. The sample size used was 98 respondents who were selected conveniently. Online questionnaires were used for data collection which had both open and closed ended question. Linear regression was used to establish the relationship between the study variables. The study concluded that strategic resource allocation decisions and cost reduction strategies positively affects the performance of HighChem Agro-Veterinary Division while, strategic innovations and product differentiation negatively affected the performance of HighChem Agro-veterinary Division. Consequently, the study recommended that HighChem Agro-Veterinary Division should continue with its cost reduction strategies as well as its strategic resource allocation decisions while re-looking at its product differentiation and strategic innovation techniques.

Genetic kidney disease has a higher likelihood and cost of inpatient admissions compared to other aetiologies

PurposeThere is increasing recognition of monogenic aetiologies for kidney disease. We sought to identify whether genetic kidney disease (GKD) has distinct hospitalization patterns compared to other forms of chronic kidney disease (CKD). MethodsHealth service utilization analysis was undertaken in a CKD cohort study across public hospital services in Queensland, Australia. CKD due to clinically coded potential monogenic causes was compared to all other causes in terms of annual frequency, cost, and type of hospital admission. ResultsWe analyzed 7 years of hospital admissions data (809,188 admissions) among 29,046 patients. Compared to non-genetic CKD, GKD was associated with a higher likelihood and cost of admissions. GKD had consistently more admissions (mean excess annual number of admissions increasing from 5.2 in year 1 to 13.4 in year 7) and more costly admissions (mean excess annual cost increasing from $5,265 in year 1 to $12,993 in year 7). This gap in hospitalization likelihood and cost increased over time for both surgical and medical admission episodes, but not for all (immunological, cancer) causes of admissions. ConclusionUnderstanding the nature and extent of differences in healthcare needs between GKD and other CKD will enable better secondary prevention and inform resource allocation decisions to reduce healthcare system pressures attributable to knowable causes.

The Impact of Alternate Methodological and Structural Assumptions on Results of Cost-effectiveness Analysis: Empirical Evidence using Three Indian Economic Evaluations.

Despite advancement in methods and application of economic evaluations (EEs), there are several uncertainties. To assess the impact of alternate methodological and structural assumptions for four key principles of EE, on the results of cost-effectiveness analysis. Three previously published model-based EEs were used: (1) Integrated Management of Neonatal and Childhood Illnesses (IMNCIs) intervention; (2) intervention for multiple myeloma, and (3) safety-engineered syringes (SES) intervention. A series of empirical analyses was undertaken to assess the impact of alternate assumptions for discount-rate, time-horizon, study perspective, and health outcome measure, on incremental cost-effectiveness ratio (ICER), and interpretation of cost-effectiveness. Increasing discount rate resulted in an increase in ICERs, for all three case-studies; however, there was no change in the conclusions. Using shorter time-horizons resulted in a significant increase in ICERs, the multiple myeloma intervention remained cost-ineffective, SES intervention became cost-ineffective, whereas IMNCI intervention remained cost-effective, despite a three-fold increase in ICER. On using disability adjusted life years instead of quality adjusted life years, ICERs increased to 0.04, 2 and 4 times for SES, IMNCI and multiple myeloma interventions, respectively. On analyzing results from a societal perspective, a decline in ICERs was observed. The decline was significant for IMNCI where the intervention turned dominant/cost-saving. In the other two case-studies decline in ICERs was modest, 32% for multiple myeloma, and 4% for SES. We observed a significant impact of using alternate assumptions on ICERs which can potentially impact resource-allocation decisions. Our findings provide strong argument in favor of standardization of processes and development of country-specific guidelines for conduct of EE.

Does Ownership Structure and Financial Health Affect Firm’s Earnings Quality? Evidence from Emerging Economy

Earnings quality is a demanding attribute of firm and is valued by investors in resource allocation decisions. High earnings quality firms create value for stakeholders and poor earnings quality causes value deterioration. This study takes into account earnings quality attributes of Pakistani corporate sector and investigates impact of ownership structure and financial health on firms’ earnings quality. Earnings quality has been measured by four attributes of accrual quality, earnings persistence, earnings predictability, earnings smoothness. A sample of 325 non-financial PSX listed Firms have been selected and classified into four unique categories on the basis of financial health (healthy and distressed) and ownership (family and non-family). The classification aims to determine level of earnings quality for each category. Results find significant effect of book financial position and ownership structure on earnings quality of firms. Findings reveal Earnings quality for non distressed family (NDF) firms was highest followed by non distressed non family firms (NDNF), while earnings quality for distressed family (DF) firms was poor and poorer for distressed non family (DNF) firms when firms were classified into these four categories. These results provide important implications for investors, analysts, regulators and standard setters.

Factors Influencing the Institutionalization of Health Technology Assessment: A Scoping Literature Review

ABSTRACT There is global interest in institutionalizing Health Technology Assessment (HTA) to inform resource allocation decisions. However, institutionalization of HTA remains limited particularly in low- and lower-middle-income countries. We conducted this scoping review to synthesize evidence on factors that influence the institutionalization of HTA at the macro (national)-level across countries globally. We searched for relevant literature in six databases namely PubMed, Embase, CINAHL, Scopus, EconLit, and Google Scholar. We conducted the last search on December 31, 2021. We identified 77 articles that described factors that influence institutionalization of HTA across 135 high-, middle-, and low-income countries. We analyzed these articles thematically. We identified five sets of factors that influence the institutionalization of HTA across countries of different income levels. These factors include: (1) organizational resources such as organizational structures, and skilled human, financial, and information resources; (2) legal frameworks, policies, and guidelines for HTA; (3) learning and advocacy for HTA; (4) stakeholder-related factors such as stakeholders’ interests, awareness, and understanding; and (5) collaborative support for HTA through international networks and non-governmental and multi-lateral organizations. Countries seeking to institutionalize HTA should map the availability of the factors identified in this review. Developing these factors wherever necessary can influence a country’s capacity to institutionalize the conduct and use of HTA.

The interdependent influence of lobbying and intellectual capital on new drug development

Pharmaceutical firms are top lobbying spenders in the United States (US). While the potential benefits of lobbying are recognized in the literature, the criticism against unethical aspects of lobbying warrants pharmaceutical firms to strategize their lobbying activities. Our study addresses the question “How does a pharmaceutical firm's resource allocation decision toward lobbying activities (and other non-market activities) influence that firm's new drug launches and do these non-market activities interact with market activities?” Specifically, we investigate the interaction between lobbying and the intellectual capital (human, structural, and social capital) of firms. We use a formal model to develop our hypotheses, which we test on a sample of the largest US-listed pharmaceutical firms between 1999 and 2021. Our analyses suggest that human and structural capital weaken the effect of lobbying on the firm's rate of new drug launches, whereas a firm's social capital strengthens the relationship between lobbying and the rate of new drug launches.

NURSING HOMES’ FIVE-STAR QUALITY RATING SYSTEM: FORECASTING THE FUTURE STABILITY AND VOLATILITY

Abstract The utilization of publicly reported quality measures such as nursing homes’ five-star quality rating system to hold providers accountable for their quality of care has increased over the years. Therefore, nursing homes have invested significant resources into maintaining or improving the ratings awarded to them by CMS. This study utilizes statistical models to analyze the past rating histories to predict future evolution of ratings of nursing homes. Data on all U.S. nursing homes for the years 2017 - 2021 was derived from CMS Nursing Home Compare. Primary variable represents the overall five-star quality rating of nursing homes. A Markov chain model and a Mover Stayer model are used to predict changes in ratings of nursing homes. The one-year rating transition probabilities are estimated using maximum likelihood estimation. The key findings indicate the likelihoods of future upgrades and downgrades in nursing homes’ ratings. Nursing home administrators are under constant pressure to strategically allocate resources for maintaining or improving the five-star ratings awarded to them by CMS. The findings of this study could help nursing home administrators in forecasting future change in their rating and making better resource allocation decisions such as those related to preventing a rating downgrade. The study findings could also be used by policymakers to focus on nursing homes facing downgrades and assisting them in improving their quality of service based on the potential change in their rating. The same results could also be utilized to assess the stability of ratings given to nursing homes.

Joint optimization of task scheduling and computing resource allocation for VR video services in 5G‐advanced networks

AbstractWith the development of 5G networks and related hardware equipment, the demand for various VR video services including panoramic video live broadcast and immersive game is increasing day by day. Due to the explosive growth of data volume and the trend toward lightweight VR equipment, traditional local computing architecture faces problems of insufficient computing power and high cost. As an emerging computing paradigm, the remote computing architecture of cloud‐edge collaboration frees users from cumbersome local restrictions. However, edge computing resources are often limited, and the remote cloud has inconsistent network latency. Therefore, how to adaptively schedule and allocate computing resources is the key to determining VR service quality. Based on an in‐depth analysis of the characteristics of VR video services and network environments, this article first builds a cloud‐edge‐network coorperation computing and transmission architecture for VR video services. This architecture can adaptively offload concurrent computing tasks and allocate computing power to them in VR scenes based on network conditions provided by the 5G‐Advanced networks via network information exposure interface. This paper proposes a general optimization problem to maximize user experience. And a single‐slot delay optimization model was constructed. Combining convex optimization theory and deep learning algorithms, this paper designs a task scheduling and computing power allocation strategy based on deep reinforcement learning. The policy gradually learns optimal task scheduling and resource allocation decisions to maximize delay‐related rewards in the long run. This article further introduces the graph neural network under the attention mechanism to solve the problem of changes in the number of concurrent tasks. Finally, through Cloudsim simulation, this article verifies the superiority of task scheduling and computing power allocation strategies based on deep reinforcement learning compared to other common strategies in terms of resource utilization, client latency, scalability and decision time.

The budgets development of international projects in the context of improving the efficiency of foreign economic activities

Introduction. Effective management of international projects in modern conditions of globalization and ever-increasing competition is becoming an important success factor for many enterprises, requiring, at the same time, large intellectual, resource, and financial costs. One of the key elements of successful project implementation is a properly formed budget, which determines financial resources and costs necessary to achieve the set goals. However, despite the fact that budgeting, the result of which is the formation of the budget, is not a new practice of domestic organizations, difficulties and problems still arise in its implementation. The purpose of the paper is to identify the problems of the budget development of international projects by domestic enterprises in the context of increasing the efficiency of foreign economic activity and to develop recommendations for their solution. Results. The budget is the result of budgeting, which is an integrated management technology that is directly implemented at the first stage of the management process and forms the basis for its implementation. In the process of developing the budgets of international projects, a detailed analysis of the effectiveness of the use of financial resources, formed sources after the completion of the project and the ineffective use of information in the field of risks is often not carried out, which often leads to an increase in the expenditure part over the revenues in the budgets of international projects. In order to ensure optimal use of resources, improve the budgeting process and make better management decisions, the management staff of domestic enterprises is recommended to implement post-project analysis as the last stage of developing international project budgets. It is a post-completion evaluation of the project, analyzing actual and projected budgets for time, cost, and quality. And also to include the stage of risk management in the process of developing the budgets of international projects. This phase will involve justifying resource allocation decisions and carrying out an assessment of the cost of the measures to be taken to minimize, accept, eliminate or transfer risks. Conclusion. In conditions of uncertainty, recommendations for domestic enterprises to carry out post-project analysis to obtain information about existing advantages and disadvantages in the formed preliminary budgets and the introduction of a risk management stage in the development of the budget at all stages of the implementation of an international project will allow domestic companies to be more efficient in the implementation of foreign economic activities and to use your finances rationally.

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks.

Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented "end-edge-cloud" multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model

To further enhance fairness in the allocation of en-route space–time resources in the collaborative trajectory selection program, a study on the plan preferences between air traffic control (ATC) and airlines in the selection process of the final plan is conducted based on the initial resource allocation plans, considering the roles of airlines in resources allocation decisions. By using Stackelberg game theory, a game model is established for the roles played by ATC and airlines in the process of selecting plans. Then, combining the overall consideration of ATC for all affected flights, the preferences of airlines for initial allocation plans are obtained, and the option range of selectable plans is narrowed down to determine the optimal allocation plan. The results of the example analysis show that the proposed model and method can effectively select the optimal allocation plan from the six initial allocation plans, select the trajectories and entry slots in the congestion areas for airlines that better meet the operation demand, and provide the decision basis with more preferences for ATC to select the final allocation plan. When ATC prefers the lowest overall delay cost, the delay cost of the selected optimal allocation plan is 267.7 min, which is 23.84% lower than the traditional RBS algorithm; when considering the preferences of the main base airline in East China, the delay cost of the selected optimal allocation plan is 287.7 min, which is 18.15% lower than the traditional RBS algorithm.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.

There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions. This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021. Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021. The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

Scoping Review of Health Economics Research on Refugee Health in Sub-Saharan Africa

ObjectivesMost refugees and internally displaced people (IDP) stay in low- and middle-income settings. A substantial proportion are hosted by countries in sub-Saharan African (SSA), which puts significant pressure on limited government healthcare budgets. As health economics may guide more optimal healthcare decision making, we scope the health economics literature on forcibly displaced populations in SSA to identify the nature and range of health economics evidence. MethodsWe conducted a scoping review of peer-reviewed and gray literature in English published from 2000 to 2021. Our search terms comprised a combination of keywords related to refugees, SSA, and health economics. We followed a stepwise methodology consisting of the identification and selection of studies, extraction and charting of data. ResultsWe identified 29 health economics studies on refugees and IDPs in SSA covering different providers, interventions, and delivery platforms. Twenty-one articles studied the determinants of health, followed by 5 on the supply of healthcare and 2 concerned with economic evaluation and the demand for healthcare, respectively. We found an equal division of articles focusing on refugees and IDPs, as well as by settlement type. Mental health was the most frequently studied health area and Uganda was the most studied destination country. ConclusionsThe health economics literature on refugees in SSA remains limited. Our scoping review encourages future research to study a larger variety of healthcare systems and health economic topics such as economic evaluations, health financing and whole health systems to support resource allocation decisions and sustainable long-term solutions.

Logical Inconsistencies in the Health Years in Total and Equal Value of Life-Years Gained

ObjectivesThis study aimed to assess whether recently proposed alternatives to the quality-adjusted life-year (QALY), intended to address concerns about discrimination, are suitable for informing resource allocation decisions. MethodsWe consider 2 alternatives to the QALY: the health years in total (HYT), recently proposed by Basu et al, and the equal value of life-years gained (evLYG), currently used by the Institute for Clinical and Economic Review. For completeness we also consider unweighted life-years (LYs). Using a hypothetical example comparing 3 mutually exclusive treatment options, we consider how calculations are performed under each approach and whether the resulting rankings are logically consistent. We also explore some further challenges that arise from the unique properties of the HYT approach. ResultsThe HYT and evLYG approaches can result in logical inconsistencies that do not arise under the QALY or LY approaches. HYT can violate the independence of irrelevant alternatives axiom, whereas the evLYG can produce an unstable ranking of treatment options. HYT have additional issues, including an implausible assumption that the utilities associated with health-related quality of life and LYs are “separable,” and a consideration of “counterfactual” health-related quality of life for patients who are dead. ConclusionsThe HYT and evLYG approaches can result in logically inconsistent decisions. We recommend that decision makers avoid these approaches and that the logical consistency of any approaches proposed in future be thoroughly explored before considering their use in practice.

Defining ‘therapeutic value’ of medicines: a scoping review

ObjectivesIn recent years, discussions on the importance and scope of therapeutic value of new medicines have intensified, stimulated by the increase of prices and number of medicines entering the market....