Administrative Overhead Research Articles

Using Unsupervised Learning for Data-driven Procurement Demand Aggregation

Procurement is an essential operation of every organization regardless of its size or domain. As such, aggregating the demands could lead to better value-for-money due to: (1) lower bulk prices; (2) larger vendor tendering; (3) lower shipping and handling fees; and (4) reduced legal and administration overheads. This paper describes our experience in developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore with procurement expenditure to the scale of hundreds of millions of dollars annually. We formulate the demand aggregation problem using a bipartite graph model depicting the relationship between procured items and target vendors, and show that identifying maximal edge bicliques within that graph would reveal potential demand aggregation patterns. We propose an unsupervised learning methodology for efficiently mining such bicliques using a novel Monte Carlo subspace clustering approach. Based on this, a proof-of-concept prototype was developed and tested with the end users during 2017, and later trialed and iteratively refined, before being rolled out in 2019. The final performance was 71% of past cases transformed into bulk tenders correctly detected by the engine; for new opportunities pointed out by the engine 81% were deemed useful for potential bulk tender contracts in the future. Additionally, per each valid pattern identified, the engine achieved 100% precision (all aggregated purchase orders were correct), and 79% recall (the engine correctly identified 79% of orders that should have been put into the bulk tenders). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7 million annually.

Introducing SYSPROP—A Software Tool to Identify Small but Economically Viable Timber Parcels in the Pacific Northwest

Abstract The past several decades have seen a steady decline in timber harvest rates from many parts of the United States, particularly the Pacific Northwest. Although various factors fuel this decline, one of the principal drivers is increasing rates of parcelization of the landscape. Increasingly parcelized forested landscapes tend to be more challenging to log—both because urbanization rates are somewhat correlated with parcelization but also due to the additional administrative overhead in securing logging rights in increasingly smaller parcels. The purpose of this note is to introduce SYSPROP, a tool to aid in the automatic identification of economically viable parcels for logging. We conclude with a case study of a small logging company operating in Washington State that used this tool to identify several promising parcels.

The status of central ethical reviewing and challenges regarding its introduction to non-interventional studies in Japan.

ABSTRACTIn 2018, we conducted a study on 121 ethics review committee offices in Japan to examine the state of “central review” in non-interventional studies and discern any challenges regarding its introduction. Of the 452 offices that were invited to participate, 121 responded (26.8% response rate), and 35 (28.9%) had records of furnishing contracting agreements with ethical reviews by other research institutions. The merits of central reviewing include easing the burden on ethics review committees, improving the quality level and consistency of ethical reviews, and enhancing the efficiency in conducting them. The demerits include increased administrative overheads and work for researchers, such as preparing application forms and checking institutional requirements, and a lack of clarity regarding who is responsible for conducting the research, which makes it is less desirable for institutions to have their own ethics review committees. This study revealed that the comprehensive introduction of central review in non-interventional studies continues to encounter many hurdles, and promoting central review requires overcoming these challenges one at a time. The Ethical Guidelines for Medical and Health Research Involving Human Subjects will be revised in 2021 to require central review as a part of ethical reviews for non-interventional studies. In the future, central reviews of non-interventional studies will need to be of high quality and conducted efficiently, and this will require research institutions to utilize relevant central review guidelines and checklists.

User authentication using Blockchain based smart contract in role-based access control

Since the last few decades, information security has become a significant challenge for organizations’ system administrators. However, the Role-Based Access Control (RBAC) model has emerged as a viable solution for organizations to meet the security requirement due to its less administrative overhead. Blockchain technology is distributive and can be used effectively in user authentication and authorization challenges. This paper proposes an RBAC model using a blockchain-based smart contract for managing user-role permissions in the organization. We design a threat and security model to resist attacks such as man-in-the-middle attacks in an organization scenario. The proposed approach uses the Ethereum blockchain platform and its smart contract functionalities to model user-resource communications. The proposed method is tested on Ropsten Ethereum Test Network and evaluated to analyze user authentication, verification, cost, and security.

Vaccine Practice Payment Schedules Create Perverse Incentives for Unnecessary Medical Procedures – at What Cost to Patients?

No published assessment of revenue variation associated with variance in pediatric vaccine uptake exists. Using data from patients in a pediatric practice that provides full-service with informed consent, we provide a detailed analysis of the financial realities of respecting informed consent and allowing parents to exercise their legal right to refuse some or all pediatric vaccines. The data from a 30-day period of billing were tracked and analyzed via superbills, noting vaccines that were ordered and those that were refused. Considering that other practice income covered all operating expenses; these numbers reflect actual profits (from vaccines given) and losses (from vaccines refused). Patients in the practice exhibited increased refusal of some or all vaccines over a period of approximately ten years. These real-world data show losses would exceed one million US dollars for a practice that bills out just over 3 million (gross revenue). With pediatric administrative overhead running 60–80%, it becomes clear that the financial incentives to vaccinate are now a matter of survival for pediatric practices.

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.

Blockchain for Organizing Effective Grass-Roots Actions on a Global Commons: Saving the Planet

An overwhelming majority of experts has been flagging for decades that “Saving the Planet” requires immediate, persistent and drastic action to curb a variety of catastrophic risks over the 21st century. However, despite compelling evidence and a range of suggested solutions, transnational coordination of effective measures to protect our biosphere continues to fall short. To remedy, we propose a novel platform for addressing the central issue of affording trust, transparency and truth while minimizing administrative overheads. This will empower an even loosely organised, global grass-roots community to coordinate a large-scale project on a shared goal (“Commons”) spanning the digital and real world. The Web3 concept is based on the swiftly emerging “Blockchain” and related cryptographic, distributed and permissionless technologies. “Wisdom of the crowds” mechanisms involving competitive parallelisation and prediction markets are enabled by formalised reputation and staking to incentivise high-quality work, fair validation and best management practice. While these mechanisms have been (mostly separately) applied to science, business, governance, web, sensor, information and communication technologies (ICT), our integrative approach around Blockchain-enabled ‘operating principles and protocols’ sets the basis for designing novel forms of potentially crowdfunded Decentralised Autonomous Organisations (DAOs).

Non-profit overhead and efficiency from IRS administrative data

ABSTRACT This paper investigates the efficiency of non-profit overhead spending by estimating its marginal returns on total revenue using IRS data. To explore whether using the overhead ratio as a heuristic for non-profit operational quality is justified, we compare it with administrative efficiency or the efficiency by which overhead is transformed into total revenues. We find that underinvestment on overhead may be explained by reputational concerns. Reputational concerns may be justified given a negative relationship between administrative efficiency and overhead ratio.

Impact of the abolition of copayments on the GP-centred coordination of care in Bavaria, Germany: analysis of routinely collected claims data.

ObjectivesIn 2012, Germany abolished copayment for consultations in ambulatory care. This study investigated the effect of the abolition on general practitioner (GP)-centred coordination of care. We assessed how the proportion of patients with coordinated specialist care changed over time when copayment to all specialist services were removed. Furthermore, we studied how the number of ambulatory emergency cases and apparent ‘doctor shopping’ changed after the abolition.DesignA retrospective routine data analysis of the Bavarian Association of Statutory Health Insurance Physicians, comparing the years 2011 and 2012 (with copayment), with the period from 2013 to 2016 (without copayment). Therefore, time series analyses covering 24 quarters were performed.SettingPrimary care in Bavaria, Germany.ParticipantsAll statutorily insured patients in Bavaria, aged ≥18 years, with at least one ambulatory specialist contact between 2011 and 2016.Primary and secondary outcome measuresPrimary outcome was the percentage of patients with GP-coordinated care (every regular specialist consultation within a quarter was preceded by a GP referral). Secondary outcomes were the number of ambulatory emergency cases and apparent ‘doctor shopping’.ResultsAfter the abolition, the proportion of coordinated patients decreased from 49.6% (2011) to 15.5% (2016). Overall, younger patients and those living in areas with lower levels of deprivation showed the lowest proportions of coordination, which further decreased after abolition. Additionally, there were concomitant increases in the number of ambulatory emergency contacts and to a lesser extent in the number of patients with apparent ‘doctor shopping’.ConclusionsThe abolition of copayment in Germany was associated with a substantial decrease in GP coordination of specialist care. This suggests that the copayment was a partly effective tool to support coordinated care. Future studies are required to investigate how the gatekeeping function of GPs in Germany can best be strengthened while minimising the associated administrative overhead.

Subcontracting New Product Development Projects: The Role of Competition and Commitment

ABSTRACTShould a firm, which seeks to subcontract a new product development project, leverage competition among potential suppliers and ask all of them to engage in research and development in parallel? Or should it first invite offers and commit to the supplier with the best offer, before only this supplier engages in development? Building on analytical literature on both formats, we apply game theory to answer these questions. We identify Bayesian Nash equilibrium strategies and characterize advantages of both formats. We find that having multiple suppliers engage in new product development in parallel is favored only if enough suppliers can be attracted, which is the case when development uncertainty and learning benefits are high. The participation decision also depends on the specific structure of the project's development costs. If administrative overhead and material costs are substantial, while engaging in development and exerting effort is relatively cheap but does not offer many learning opportunities, the number of suppliers who would be willing to engage in parallel development is limited. First inviting offers and selecting the best supplier to exclusively engage in new product development then becomes more attractive for the buyer. We discuss further implications and characterize environments that may foster more innovativeness in this context.

Delivering \u2018tiny targets\u2019 in a remote region of southern Chad: a cost analysis of tsetse control in the Mandoul sleeping sickness focus

BackgroundSince 2012, the World Health Organisation and the countries affected by the Gambian form of human African trypanosomiasis (HAT) have been committed to eliminating the disease, primarily through active case-finding and treatment. To interrupt transmission of Trypanosoma brucei gambiense and move more rapidly towards elimination, it was decided to add vector control using ‘tiny targets’. Chad’s Mandoul HAT focus extends over 840 km2, with a human population of 39,000 as well as 14,000 cattle and 3000 pigs. Some 2700 tiny targets were deployed annually from 2014 onwards.MethodsA protocol was developed for the routine collection of tsetse control costs during all field missions. This was implemented throughout 2015 and 2016, and combined with the recorded costs of the preliminary survey and sensitisation activities. The objective was to calculate the full costs at local prices in Chad. Costs were adjusted to remove research components and to ensure that items outside the project budget lines were included, such as administrative overheads and a share of staff salaries.ResultsTargets were deployed at about 60 per linear km of riverine tsetse habitat. The average annual cost of the operation was USD 56,113, working out at USD 66.8 per km2 protected and USD 1.4 per person protected. Of this, 12.8% was an annual share of the initial tsetse survey, 40.6% for regular tsetse monitoring undertaken three times a year, 36.8% for target deployment and checking and 9.8% for sensitisation of local populations. Targets accounted for 8.3% of the cost, and the cost of delivering a target was USD 19.0 per target deployed.ConclusionsThis study has confirmed that tiny targets provide a consistently low cost option for controlling tsetse in gambiense HAT foci. Although the study area is remote with a tsetse habitat characterised by wide river marshes, the costs were similar to those of tiny target work in Uganda, with some differences, in particular a higher cost per target delivered. As was the case in Uganda, the cost was between a quarter and a third that of historical target operations using full size targets or traps.

Surprise Billing in Surgical Care Episodes - Overview, Ethical Concerns, and Policy Solutions in Light of COVID-19.

Surprise Billing in Surgical Care Episodes - Overview, Ethical Concerns, and Policy Solutions in Light of COVID-19.

A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues

Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.

Can School Enrolment and Performance be Improved by Maximizing Students’ Sense of Choice in Elective Subjects?

This paper explores a system that attempts to maximize high school students’ sense of choice when selecting elective subjects. We propose that individual schools can tailor the combinations of subjects they offer in order to maximize the number of prospective students who can study their preferred subjects, potentially increasing enrol- ment numbers and academic outcomes while also reducing administrative overheads. We analyze the underlying computational problem encountered in this task and describe a suitable AI-based optimization algorithm that we have made available for free download. We also discuss some outcomes of using this method on a small number of case study schools.

Lessons From the Open Library of Humanities

The Open Library of Humanities was launched almost half a decade ago with funding from the Andrew W. Mellon Foundation. In this article, we outline the problems we set out to address and the lessons we learned. Specifically, we note that, as we hypothesized, academic libraries are not necessarily classical economic actors; that implementing consortial funding models requires much marketing labour; that there are substantial governance and administrative overheads in our model; that there are complex tax and VAT considerations for consortial arrangements; and that diverse revenue sources remain critical to our success.

An SLA-Aware Cloud Coalition Formation Approach for Virtualized Networks

One of the main challenges faced by cloud providers is the uncertainty in their workload, resulting from the high variability and the dynamic nature of clients' demands. The inability to meet those demands during peak times can lead to high service rejection rates, experienced delays, and consequently profit and reputation losses. The concept of cloud federation has been proposed as a way to address this challenge, by enabling a group of cloud providers to collaborate by dynamically combining their resources as needed, to satisfy received requests. Existing cloud federation approaches fail to consider clients' SLA requirements during the coalition formation process or provide a self-healing mechanism to deal with unexpected resources' shortage during operation. Furthermore, the state of the art approaches suffer from performance issues, such as high execution times, unstable performance, and lack of convergence to a solution in complex scenarios (e.g., requests with mixed, independent types of VMs). This paper proposes a novel social gaming based approach for coalition formation in the cloud that finds the best coalition of cloud providers to answer requests, while satisfying the clients' SLA requirements. The proposed algorithm, dubbed SLA Aware Cloud Coalition Formation algorithm (S-ACCF), leverages Irving's roommate algorithm to form a stable coalition of cloud providers, with a rapid execution time. The S-ACCF algorithm is designed to maximize the coalition's profit, while minimizing the number of participants in the coalition as well as the penalty incurred by providers who fail to offer all or some of the promised resources using a self-healing process. The S-ACCF algorithm was extensively tested using a variety of scenarios, and its performance was compared to two state of the art approaches: 1) the Optimal Cloud Federation Mechanism (OCFM) that relies on an exhaustive search of all possible solutions to find the best coalition; and 2) the Cloud Federation Formation Mechanism (CFFM) that relies on an iterative split-and-merge approach to find the best coalition. While the optimal approach (OCFM) always finds the best coalition leading to the highest collective profit, it has an exponential time complexity, thus leading to very large execution times. On the other hand, the split-and-merge approach (CFFM), which relies on random selection of sub-groups for coalition formation, suffers from instability (different results in repeated runs), high and variable execution time, and a noticeable requests' rejection rate that changes between runs. The test results show that the S-ACCF algorithm addresses the limitations of the OCFM and the CFFM algorithms, and outperforms the optimal and split-and-merge approaches in terms of execution time, individual provider payoff, and the number of providers per coalition. Furthermore, it yields higher stability and zero rejection rate, when compared to the split-and-merge approach. Indeed, our proposed approach yields an execution time that is 12 to 25 times faster than the optimal and split-and-merge approaches, which is a major advantage for real-time applications. Moreover, when compared to the two other approaches, our S-ACCF algorithm always finds the smallest coalition possible satisfying the client requirements, thus leading to the highest individual payoff for providers and lower administration overhead. Finally, unlike the split-and-merge approach, our algorithm shows a stable performance, and converges towards the optimal solution in simple and complex scenarios, thus making it suitable for production environments.

Achieving security scalability and flexibility using Fog-Based Context-Aware Access Control

In the cyberspace environment, access control is one of the foremost fundamental safeguards used to prevent unauthorized access and to minimize the impact from security breaches. Fog computing preserves many benefits for the integration of both internet of things (IoT) and cloud computing platforms. Security in Fog computing environment remains a significant concern among practitioners from academia and industry. The current existing access control models, like the traditional Context-Aware Access Control (CAAC), are limited to access data from centralized sources, and not robust due to lack of semantics and cloud-based service. This major concern has not been addressed in the literature, also literature still lacks a practical solution to control fog data view from multiple sources.This paper critically reviews and investigates the limitations of current fog-based access control. It considers the trade-off between latency and processing overheads which has not been thoroughly studied before. In this paper, a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework is proposed to enable flexible access control data from multiple sources. To fill the gap in the literature this paper introduces (i) a general data model and its associated mapping model to collate data from multiple sources. (ii) a data view model to provide an integrated result to the users, dealing with the privacy requirements of the associated stakeholders, (iii) a unified set of CAAC policies with an access controller to reduce both administrative and processing overheads, and (iv) a data ontology to represent the common classes in the relevant data sets. The applicability of FB-CAAC proposal is demonstrated via a walkthrough of the entire mechanism along with several case studies and a prototype testing. The results show the efficiency, flexibility, effectiveness, and practicality of FB-CAAC for data access control in fog computing environment.

Practicalities of care closer to home: seven key questions for community psychiatrists

SUMMARYThis article aims to clarify what ‘care closer to home’ means to a community psychiatrist. Care closer to home can be operationalised as primary care liaison and the article reviews experience across England of how a liaison service can work with the recently organised primary care networks. Key competencies needed for liaising with primary care are discussed using seven questions, including bias mitigation, reducing bed-days, consultation skills, knowledge of emerging treatments and reducing administrative overheads while improving access.

Prior authorization and its impact on access to obstetric ultrasound

Prior authorization is a process requiring health care providers to obtain advance approval from a payer before a patient undergoes a procedure for the study to be covered. Prior authorization was introduced to decrease overutilization of ultrasound procedures. However, it has led to unanticipated consequences such as impeding access to obstetric imaging, increased administrative overhead without reimbursement, and contribution to physician frustration and burnout. Payers often use intermediary radiology benefit management companies without providing specialty-specific review in a timely manner as is requisite when practicing high-risk obstetrics. This article proposes a number of potential solutions to this problem: (1) consider alternative means to monitor overutilization; (2) create and evaluate data regarding providers in the highest utilization; (3) continue to support and grow the educational efforts of speciality societies to publish clinical guidelines; and (4) emphasize the importance of practicing evidence-based medicine. Understanding that not all health plans may be willing or able to collaborate with health care providers, we encourage physicians to advocate for policies and legislation to limit the implementation of prior authorization within their own states.

Intelligent Role-Based Access Control Model and Framework Using Semantic Business Roles in Multi-Domain Environments

Today's rapidly developing communication technologies and dynamic collaborative business models made the security of data and resources more crucial than ever especially in multi-domain environments like Cloud and Cyber-Physical Systems (CPS). It enforced the research community to develop enhanced access control techniques and models for resources across multi-domain distributed environments so that the security requirements of all participating organizations can be fulfilled through considering dynamicity of changing environments and versatility of access control policies. The popularity of Role-Based Access Control (RBAC) model is irrefutable because of low administrative overhead and large-scale implementation in business organizations. However, it does not incorporate the dynamically changing policies and lacks semantically meaningful business roles which could have a diverse impact upon access decisions in multi-domain business environments. This paper describes our proposed novel access control framework that uses semantic business roles and intelligent agents through implementation of our Intelligent RBAC (I-RBAC) model. It encompasses occupational entitlements as roles for multiple domains. We use the dataset of original occupational roles provided by Standard Occupational Classification (SOC), USA. The novelty of the paper lies in developing a core I-RBAC ontology using real-world semantic business roles and intelligent agent technologies together for achieving required level of access control in highly dynamic multi-domain environment. The intelligent agents use WordNet and bidirectional LSTM deep neural network for automated population of organizational ontology from unstructured text policies. This dynamically learned organizational ontology is further matched with our core I-RBAC ontology in order to extract unified semantic business roles. The proposed I-RBAC model is mathematically described and the overall I-RBAC framework and its implementation architecture is explained. At the end, the I-RBAC model is validated through the implementation results that show a linear runtime trend of the model in presence of a large number of permission assignments and multiple queries.