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Equity in blood transfusion precision services
Background
Blood collection agencies are integrating precision medicine techniques to improve and individualise blood donor and recipient outcomes. These organisations have a role to play in ensuring equitable application of precision medicine technologies for both donors and transfusion recipients.
Body
Precision medicine techniques, including molecular genetic testing and next generation sequencing, have been integrated in transfusion services to improve blood typing and matching with the aim to reduce a variety of known transfusion complications. Internationally, priorities in transfusion research have aimed to optimise services through the use of precision medicine technologies and consider alternative uses of genomic information to personalise transfusion experiences for both recipients and donors. This has included focusing on the use of genomics when matching blood products for transfusion recipients, to personalise a blood donor’s donation type or frequency, and longitudinal donor research utilising blood donor biobanks.
Conclusion
Equity in precision services and research must be of highest importance for blood collection agencies to maintain public trust, especially when these organisations rely on volunteer donors to provide transfusion services. The investment in implementing equitable precision medicine services, including development of blood donor biobanks, has the potential to optimise and personalise services for both blood donors and transfusion recipients.
Journal Article
A systematic review of machine learning models applied in debt collection operations/Uma revisao sistematica de modelos de machine learning aplicados em operacoes financeiras de cobrancas de dividas
Brazil is facing high default rates, due in part to the pandemic, leading to the search for new debt collection strategies. Machine Learning (ML), successfully used in numerous areas, is an ally to increase the effectiveness of these operations. This article seeks to present a current overview of research on ML applications in debt collection operations, through a Systematic Literature Review. The PICO methodology was used, initially identifying 41 documents, of which 11 underwent systematic review. The results showed four objectives pursued by the studies: default prediction, personalization of collection strategies, optimization of debt recovery actions and credit recovery prediction. And the main algorithms used were Decision Tree, Logistic Regression, Random Forest, Naive Bayes, Artificial Neural Network and Deep Learning. The results revealed that ML is still little explored in this area, offering potential for substantial research advances. Keywords: Debt collection; Machine learning; Financial operations, Systematic literature review. O Brasil enfrenta altas taxas de inadimplencia, devido em parte a pandemia, levando a busca de novas estrategias de cobrancas de dividas. O Machine Learning (ML), empregado com sucesso em inumeras areas, e aliado para elevar a eficacia dessas operacoes. Este artigo busca apresentar um panorama atual das pesquisas sobre aplicacoes de ML nas operacoes de cobranca de dividas, por meio de uma Revisao Sistematica da Literatura. Foi utilizada a metodologia PICO, identificando inicialmente 41 documentos, dos quais 11 passaram por revisao sistematica. Os resultados mostraram quatro objetivos buscados pelos estudos: previsao de inadimplencia, personalizacao das estrategias de cobranca, otimizacao das acoes de recuperacao de dividas e previsao de recuperacao de credito. E os principais algoritmos utilizados foram Decision Tree, Logistic Regression, Random Forest, Naive Bayes, Artificial Neural Network e Deep Learning. Os resultados revelaram que ML e ainda pouco explorado nessa area, oferecendo potencial para avancos substanciais das pesquisas. Palavras-chave: Cobranca de dividas; Aprendizagem de maquina; Operacoes financeiras; Revisao sistematica de literatura.
Journal Article
ASSEMBLY-LINE PLAINTIFFS
Around the country, state courts are being flooded with the claims of massive repeat filers. These large corporate plaintiffs leverage economies of scale to bring tremendous quantities of low-value claims against largely unrepresented individual defendants. Using recently developed litigation-analytics tools, this Article presents the first nationwide study of these “assembly-line plaintiffs,” examining the top civil filers in a range of state courts across the country going back to 2004. It documents the pervasive nature of this litigation, finding that in many court systems just the top ten private filers account for between one fifth and one third of all civil litigation.
This pattern raises serious concerns. Drawing on existing empirical literature and a sample of 1000 recent case dockets, the Article describes how these cases turn state courts into near-automatic claims processors for large corporations, transferring assets from mostly absent defendants without significant scrutiny of the underlying claims. These defendants, moreover, are often particularly vulnerable low-income consumers or members of other marginalized groups. And although many concerns raised by this litigation overlap with those related to unrepresented litigants more broadly, the structural features of assembly-line litigation — its one-sidedness, high volume, and low claim value — present distinctive challenges. The Article concludes by considering a few specific potential reforms designed to meet those challenges: assessing a surcharge on frequent filers as a form of congestion pricing; enabling common defenses to be asserted as affirmative causes of action to facilitate aggregation; and moving courts away from one-case-at-a-time adjudication toward a more investigative, administrative-agency-like model.
Journal Article
Development of a Vision-Based Unmanned Ground Vehicle for Mapping and Tennis Ball Collection: A Fuzzy Logic Approach
The application of robotic systems is widespread in all fields of life and sport. Tennis ball collection robots have recently become popular because of their potential for saving time and energy and increasing the efficiency of training sessions. In this study, an unmanned and autonomous tennis ball collection robot was designed and produced that used LiDAR for 2D mapping of the environment and a single camera for detecting tennis balls. A novel method was used for the path planning and navigation of the robot. A fuzzy controller was designed for controlling the robot during the collection operation. The developed robot was tested, and it successfully detected 91% of the tennis balls and collected 83% of them.
Journal Article
The Policy Alliance Between Hospitals and Debt Collection Agencies: Content Analysis of Public Comments on Regulations on Billing and Collections
Significant debate persists about the obligations of nonprofit hospitals toward low-income patients. Many issues pertaining to this subject were discussed during the rulemaking process following the passage of the Affordable Care Act of 2010, which set forth rules for hospital billing and collection. In public comments, hospitals, debt collectors, and patient advocates debated what constituted “reasonable efforts” to determine whether a patient qualified for hospital financial assistance before resorting to extraordinary collection actions including lawsuits, wage garnishments, and adverse credit reporting. This study analyzes public comments to the proposed Internal Revenue Service rule on section 501(r)(6). After an initial review of the data, 5 commonly mentioned issues were identified. Respondents were organized into commenter types, and the opinion of each respondent to each issue was coded by 2 separate reviewers. Discrepancies between reviewer determinations were resolved by consensus during follow-up discussions. This analysis revealed a set of common concerns: whether reporting delinquent medical debt to credit bureaus and selling debt to third party buyers should be considered extraordinary collection actions; whether hospitals should be able to use presumptive eligibility to rule patients either eligible or ineligible for financial assistance; and whether hospitals should be held legally liable for the actions of third-party debt collectors. Hospitals and debt collection agencies were allied on most issues, particularly in their shared belief that reporting debt to credit bureaus and selling debt to third parties should not be tightly regulated. Patient advocacy organizations and hospitals had divergent opinions on most issues. The alliance of hospitals and debt collectors in advocating for fewer regulations around collections is part of a history of hospital lobbying to maintain tax-exemption with fewer charity care mandates. This alignment helps explain why third-party debt collection agencies, and aggressive collection tactics, have become commonplace in hospital billing.
Journal Article
Regulation of Abusive Debt Collection Practices in the EU Member States: An Empirical Account
The article seeks to establish, in a comprehensive manner, if and how abusive debt collection practices are regulated in the respondent EU Member States. Using empirical data gathered from consumer and supervisory agencies as well as debt collection associations in 26 EU Member States, it provides an insight into (a) the existence of a licencing regime for debt collectors; (b) the potential transboundary dimension of debt collection and its implications for the common market; (c) the types of abusive debt-collection practices encountered in the Member States; (d) the efficacy of self-regulation via Codes of Conduct; and (e) the potential traditional remedies available to consumer-debtors. The article concludes that the existence of different national models creates potential issues and discrepancies in the legal status and defences available to consumer-debtors across the EU, which ultimately affects the proper functioning of the single credit servicing market. The advocated solution is that of a harmonized sector-specific regulation of abusive debt collection practices at EU level.
Journal Article
Determining the Most Predictive Discipline in Olympic Triathlon: A Machine Learning Approach
Background: The aim of the present study was to identify the discipline with the greatest predictive value for overall performance in Olympic-distance triathlon. Methods: Data were extracted from the API (Application Programming Interface) service on the World Triathlon website by signing up for the free service. A custom Python code was written to perform different data collection operations. General statistical analyses and machine learning analyses were performed by creating a Jupyter Notebook file. TensorFlow and PyTorch libraries were used for machine learning analysis. Results: Fifty percent of the employed models identified cycling as the most predictive discipline for race success for both sexes, whereas 33% selected running as the determining discipline. To achieve a podium finish, approximately 78% of the models classified running as the most predictive discipline for males, and approximately 56% of the models did so for females. For finishes between fourth and tenth place, approximately 78% of the models proposed running as the most predictive discipline for both sexes. Swimming was never identified as the most predictive discipline by the majority of models for any group or sex. Conclusion: The most predictive discipline in Olympic triathlon depends on the athlete’s sex and competitive level. Nonetheless, running remains the most consistently predictive discipline, whereas swimming rarely acts as a performance differentiator.
Journal Article
Technical note: A microcontroller-based automatic rain sampler for stable isotope studies
Automatic samplers represent a convenient way to gather rain samples for isotope (δ18O and δ2H) and water quality analyses. Yet, most commercial collectors are expensive and do not reduce post-sampling evaporation and the associated isotope fractionation sufficiently. Thus, we have developed a microcontroller-based automatic rain sampler for timer-actuated collection of integral rain samples. Sampling periods are freely selectable (minutes to weeks), and the device is low-cost, simple, robust, and customizable. Moreover, a combination of design features reliably minimizes evaporation from the collection bottles. Evaporative losses were assessed by placing the pre-filled sampler in a laboratory oven with which a diurnal temperature regime (21–31 ∘C) was simulated for 26 weeks. At the end of the test, all bottles had lost less than 1 % of the original water amount, and all isotope shifts were within the analytical precision. These results show that even multi-week field deployments of the device would result in rather small evaporative mass losses and isotope shifts. Hence, we deem our sampler a useful addition to devices that are currently commercially available and/or described in the scientific literature. To enable reproduction, all relevant details on hard- and software are openly accessible.
Journal Article