Linking Performance Measures to Resource Allocation: exploring unmapped terrain

Purpose Using the example of community access programs (CAPs), the purpose of this paper is to describe resource allocation and policy decisions related to providing health services for the uninsured in the USA and the organizational values affecting these decisions. Design/methodology/approach The study used comparative case study methodology at two geographically diverse sites. Researchers collected data from program documents, meeting observations, and interviews with program stakeholders. Findings Five resource allocation or policy decisions relevant to providing healthcare services were described at each site across three categories: designing the health plan, reacting to funding changes, and revising policies. Organizational values of access to care and stewardship most frequently affected resource allocation and policy decisions, while economic and political pressures affect the relative prioritization of values. Research limitations/implications Small sample size, the potential for social desirability or recall bias, and the exclusion of provider, member or community perspectives beyond those represented among participating board members. Practical implications Program directors or researchers can use this study to assess the extent to which resource allocation and policy decisions align with organizational values and mission statements. Social implications The description of how healthcare decisions are actually made can be matched with literature that describes how healthcare resource decisions ought to be made, in order to provide a normative grounding for future decisions. Originality/value This study addresses a gap in literature regarding how CAPs actually make resource allocation decisions that affect access to healthcare services.

Social inequalities are worsened by the decision about how resources are to be allocated. Considering that children and adolescents are likely to become future leaders and decision-makers, understanding which principles they follow regarding resource allocation in situations of inequality is crucial. This paper aims at presenting a protocol for systematic review of the relationship between perception of resource inequality and allocation decisions made by children and adolescents. Method: A systematic search will be conducted in the PsycINFO, PsycArticles, Web of Science (Core Collections), Scopus, ERIC and LILACS databases. The paper will also include empirical studies from the authors’ personal files, studies obtained through contact with the main authors of the articles included, and through backward and forward snowballing. The studies’ quality will be assessed using the Quality Assessment with Diverse Studies (QuADS). Finally, a narrative synthesis will be conducted to review and synthesize data. This protocol was outlined following the PRISMA-P guidelines. The protocol is registered at the Open Science Framework (OSF) under the code DOI: https://doi.org/10.17605/OSF.IO/D96MY. The systematic review will follow the Joanna Briggs Institute guidelines, and will be reported according to the PRISMA 2020 reporting guidelines. Discussion: The results of this review will be relevant to policy makers, decision-makers, educators, and researchers.

The dominant health economic units upon which new treatment funding decisions are made are the incremental cost per life year gained (LYG) or the cost per quality-adjusted life year (QALY) gained. Neither of these units modifies the amount of health gained, by the amount of health patients would have had if they had not been given the treatment under consideration, which may unfairly undervalue the treatments for poor prognosis conditions. How certain patients make decisions about their own treatment has previously been explored, but not how they, or doctors, would allocate hypothetical resource within a healthcare system given information on disease-treatment scenarios' prognoses with and without treatment. Information on prognosis without treatment is used within the resource allocation strategies of many doctors and most patients. Individuals use this information in a variety of different ways and a single dominant strategy for quantitative modification of health units is not apparent. Information on prognosis without treatment, or prognosis with standard treatment, is available from the control arm of randomized controlled clinical trials and should be used qualitatively to facilitate decision-making around the second inflexion point on cost per QALY/LYG acceptability curves. Health economic assessments increasingly contribute to funding decisions on new treatments. Treatments for many poor prognosis conditions perform badly in such assessments because of high costs and modest effects on survival. We aimed to determine whether underlying shortness of prognosis should also be considered as a modifier in such assessments. Two hundred and eighty-three doctors and 201 oncology patients were asked to allocate treatment resource between hypothetical patients with unspecified life-shortening diseases. The prognoses with and without treatment were varied such that consistent use of one of four potential allocation strategies could be deduced: life years gained (LYGs) - which did not incorporate prognosis without treatment information; percentage increase in life years (PILY); life expectancy with treatment (LEWT) or immediate risk of death (IRD). Random choices were rare; 47% and 64% of doctors and patients, respectively, used prognosis without treatment in their strategies; while 50% and 32%, respectively, used pure LYG-based strategies. Ranking orders were LYG > PILY > IRD > LEWT (doctors) and LEWT > LYG > IRD > PILY (patients). When LYG information alone could not be used, 76% of doctors prioritized shorter prognoses, compared with 45% of patients. Information on prognosis without treatment is used within the resource allocation strategies of many doctors and most patients, and should be considered as a qualitative modifier during the health economic assessments of new treatments for life-shortening diseases. A single dominant strategy incorporating this information for any quantitative modification of health units is not apparent.

We develop an optimal control model for optimally allocating effort between detection and correction processes, during testing phase of a software development life cycle (SDLC). The objective is to propose a resource allocation plan to minimise the total testing cost with the reliability criteria using flexible software reliability growth model considering testing effort under dynamic environment. Furthermore, we have incorporated the learning curve effect in correction cost function due to its importance in cost estimation, and resource allocation decisions. A technique based on optimal control theory is proposed for the software managers to optimise the allocation of the limited resources, while genetic algorithm is used to find the approximate value of detection and correction effort. An illustrative example is presented. The optimal release policy under reliability criteria is also discussed.

Increasing costs for patient care may necessitate financial cuts in the health-care budget. Our aim is to examine whether the public prefers cuts for psychiatric rather than medical conditions and how resource allocation preferences are related to illness beliefs and attitudes. A telephone survey involving German adult population was conducted in 2004 (n = 1012). Participants were presented with a list of nine medical and mental diseases including alcoholism, depression, schizophrenia, Alzheimer's disease, cancer, diabetes, rheumatism and AIDS, and were asked to name three conditions where they would prefer to see health-care resources cut. For all conditions we asked about personal attitudes and illness beliefs. People were more willing to have financial resources cut for psychiatric than for medical conditions, with resources for alcoholism having the least public backing. Alzheimer's disease was rated more favourably compared to other mental disorders. Generally, the perception of the severity of a disease was associated with resource allocation decisions, favouring those conditions that were considered to be severe. Mental diseases evoked a far greater desire for social distance than most medical diseases which had considerable influence on resource allocation preferences. The perception of personal responsibility had, in contrast, only limited effect on resource allocation decisions. It varied considerably in the case of psychiatric conditions but was not fundamentally different among medical and mental diseases. Personal susceptibility, treatment effectiveness and the perceived life-threat posed by a disease also had limited effects. According to public resource allocation preferences psychiatric patients are at risk of being structurally discriminated within the health-care system.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

With the rise of an innovation agenda in science policy, previous studies have identified a shift in how the state delegates responsibility to funding agencies in order to change the behaviour of the scientific community. This paper contributes to this literature through a micro-level study of how one of Canada’s largest research funding agencies, the Natural Sciences and Engineering Research Council (NSERC), has changed resource allocation for research over 25 years. Our study foregrounds research funding agencies as key sites for examining the reconfiguration of the relationship between the state and science, as expressed in programmatic and resource allocation decisions. Through analysis of an original dataset compiled from NSERC’s funding and documentary data, we demonstrate the relationship between the introduction of innovation objectives in funding instruments, the adoption of new delegation modes to guide resource allocation, and changes in funding among research fields over time. Our study empirically demonstrates the cumulative effect of programmatic and funding decisions in a major agency, going beyond previous accounts of more general trends at the national level.

Stream processing applications running on Heterogeneous Multi-Processor Systems on Chips (HMPSoCs) require efficient resource allocation and management, both at compile-time and at runtime. To cope with modern adaptive applications whose behavior can not be exhaustively predicted at compile-time, runtime managers must be able to take resource allocation decisions on-the-fly, with a minimum overhead on application performance. Resource allocation algorithms often rely on an internal modeling of an application. Directed Acyclic Graph (DAGs) are the most commonly used models for capturing control and data dependencies between tasks. DAGs are notably often used as an intermediate representation for deploying applications modeled with a dataflow Model of Computation (MoC) on HMPSoCs. Building such intermediate representation at runtime for massively parallel applications is costly both in terms of computation and memory overhead. In this paper, an intermediate representation of DAGs for resource allocation is presented. This new representation shows improved performance for run-time analysis of dataflow graphs with less overhead in both computation time and memory footprint. The performances of the proposed representation are evaluated on a set of computer vision and machine learning applications.

Poster #235 SMOKING AND OTHER CARDIOVASCULAR DISEASE RISK BEHAVIOURS AMONG PEOPLE WITH SEVERE MENTAL DISORDERS

Financial literacy and retirees' resource allocation decisions in New Zealand

COUNTERPOINT: Is Considering Social Determinants of Health Ethically Permissible for Fair Allocation of Critical Care Resources During the COVID-19 Pandemic? No

Federated learning (FL) has been recognized as a promising paradigm to support distributed AI model training among wireless devices (WDs) under the coordination of an edge server (ES) without sharing local datasets. To alleviate computation burden of resource-limited WDs, model partitioning that leverages computing capability at the ES is further integrated into FL, yielding the split (S) FL framework. In this paper, we study online bandit model partitioning and resource allocation for SFL over dynamic wireless networks, aiming to minimize overall energy-latency cost (ELC). Unlike prior works focusing on offline static or online gradient-based model splitting and resource allocation, we consider a practical setting where the analytical expression of ELC function is unavailable, and instead only the function values at queried points are revealed. To tackle such a challenging mixed-integer non-linear programming problem in the online bandit context, novel Bayesian optimization (BO)-based approaches are put forth by relying on a Gaussian process (GP)-based surrogate model to actively select the model splitting points and resource allocation decisions per round via the low-complexity acquisition. Besides incorporating training model-specific structural information in the kernel design of the GP surrogate, an ensemble of GP models with data-adaptive weights is further leveraged to capture system dynamics. To cope with the challenging combinatorial nature and strong coupling over mixed action space during acquisition, an efficient alternating optimization approach is proposed building upon a novel contextual local search method. Numerical tests demonstrate that the proposed BO-based approaches outperform the contemporary baselines under various practical SFL settings.

This paper proposes an integrated approach to jointly tackle districting and resource allocation decisions, two interrelated problems that, in most cases, are handled separately. The proposed approach is applied to the context of Emergency Medical Services (EMS), where a large territory needs to be covered by a limited number of resources, i.e., the ambulances. The territory is usually split into districts; each district receives a share of ambulances, which are managed quasi-independently. This paper focuses on the importance of districting decisions, which will impact daily operations and, therefore, the performance of the system. To address the districting and resource location and allocation problems jointly, it proposes an iterative algorithm that exploits the interaction between the strategic (i.e., the districting) and the operational (i.e., the location and allocation of resources) decisions to build compact and balanced districts and, at the same time, find the location and allocation of resources that maximize the performance in terms of system’s response time. Starting from an initial set of districts, the iterative algorithm solves the associated resource location and allocation problem for each of them. Then, according to the performance reached by the location–allocation solutions, the districts are modified. Applied to realistic instances inspired by the city of Montreal, Canada, the algorithm produced results that improved simultaneously the system’s expected response time and the metrics assessing the quality of the districts.

Stochastic customer order scheduling on heterogeneous parallel machines with resource allocation consideration

In recent years, investment in workplace mental health and wellbeing has grown. However, there is little understanding of how resource allocation decisions are made in such settings. To ensure evaluative research can support resource allocation, a process-based understanding of decision-making would be helpful. In phase one a systematic review of the literature on the implementation of workplace mental health and wellbeing schemes was conducted to draw insights on workplace resource allocation processes. In phase two an in-depth case study of a large company was conducted with interviews with those involved in resource allocation and wellbeing. Interviews were coded and analysed using descriptive and explanatory accounts. The findings from the review and case study were combined and developed into a causal process theory. This study shows that the stages in mental health and wellbeing investment revolve around ensuring organisational buy-in, workforce investment, and continual evaluation. Further work is needed to explore the transferability of the resulting process theory across different types of workplaces. However, it is clear that the features of real world decision-making in workplaces present challenges and opportunities for the research community.