Resource scarcity compromises explore-exploit decision-making

2022 ◽  
Vol 98 ◽  
pp. 104254
Author(s):  
Shou-An A. Chang ◽  
Julian Jara-Ettinger ◽  
Arielle Baskin-Sommers
Keyword(s):  
Decision Making ◽  
Resource Scarcity

Different approach to medical decision-making in difficult circumstances: Kittay’s Ethics of Care

2021 ◽  
pp. medethics-2021-107521
Author(s):  
Liam Butchart ◽  
Kristin Krumenacker ◽  
Aymen Baig
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Ethics Of Care ◽  
Resource Scarcity ◽  
Medical Decision ◽  
Alternative Method

The onset of the COVID-19 pandemic has necessitated advances in bioethical approaches to medical decision-making. This paper develops an alternative method for rationing care during periods of resource scarcity. Typical approaches to triaging rely on utilitarian calculations; however, this approach introduces a problematic antihumanist sentiment, inviting the proposition of alternative schemata. As such, we suggest a feminist approach to medical decision-making, founded in and expanding upon the framework of Eva Kittay’s Ethics of Care. We suggest that this new structure addresses the issue of medical decision-making during times of resource scarcity just as well as pure utilitarian approaches while better attending to their significant theoretical concerns, forming a coherent alternative to the current bioethical consensus.

scholarly journals Priority Setting in The Brazilian Emergency Medical Service: A Multi-Criteria Decision Analysis (MCDA)

2020 ◽  
Author(s):  
Talita Dias Chagas Frazão ◽  
Ana FA Santos ◽  
Deyse GG Camilo ◽  
João FC Junior ◽  
Ricardo Pires Souza
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Decision Model ◽  
Medical Service ◽  
Evaluation Criteria ◽  
Resource Scarcity ◽  
Multiple Criteria ◽  
Multicriteria Decision ◽  
Emergency Situations ◽  
Proposed Model

Abstract Background: Multicriteria Decision Analysis is a tool capable of supporting decisions with multiple criteria. Notwithstanding its con rmed value in the health area; so far, no studies have been found to help prioritize victims in the Emergency Medical Service, EMS. Since decision making within EMS involves multiple criteria, it is essential to nd techniques and tools that encompass such elements, as to reduce errors. As to address this gap, the current research developed a multicriteria decision model to help prioritizing victims in the Brazilian EMS, which are still managed as a manual task. Methods: To reach such endeavour, it was formed an expert panel and a discussion group, tasked to de ne the limits of the problem, and to identify the evaluation criteria for choosing a victim, amongst four alternatives derived from clinical and traumatic diseases scenarios of absolute priority in emergency situations occurrences. For prioritization, an additive mathematical method was utilized, aggregating criteria in a exible and interactive version - FiTradeoff. Results: The present work contributed to victims' prioritization by using the multicriteria decision support methodology which led to the identi cation of twenty- ve evaluation criteria to guide the decision. It was noted that the protocols to guide regulating physicians do not consider all the criteria for prioritizing victims in an environment of resource scarcity. In the prioritization simulation composed of four demanding victims and only one available ambulance, the proposed model supported the decision by suggesting the prioritization of Victim 2. Conclusions: From the identi ed improvement points, the developed decision model was able to improve the regulatory action of medical professionals. The elicitation procedure enabled the identi cation of criteria that, albeit well known, were not formalized by the current guidance protocols, which could contribute to contradictions and conicts across the decision chain. Last, but not least, the proposed model could support decision making under the guarantee of a rational and transparent decision-making process that could be applied in other EMS.

scholarly journals Cloud computing survey on services, enhancements and challenges in the era of machine learning and data science

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
Wajid Hassan ◽  
Te-Shun Chou ◽  
Omar Tamer ◽  
John Pickard ◽  
Patrick Appiah-Kubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cloud Computing ◽  
Data Science ◽  
Service Level Agreement ◽  
Resource Scarcity ◽  
Cloud Services ◽  
Future Research ◽  
Intelligent Decision Making ◽  
Intelligent Decision

<p>Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.</p>

scholarly journals Ethical issues related to the COVID-19 pandemic in patients with cancer: experience and organisations in a French comprehensive cancer centre

2020 ◽  
pp. bmjspcare-2020-002504
Author(s):  
François Blot ◽  
Sarah N Dumont ◽  
Laurence Vigouret-Viant ◽  
Nelly Verotte ◽  
Julien Rossignol ◽  
...  
Keyword(s):  
Decision Making ◽  
Critical Care ◽  
Ethical Issues ◽  
Ethical Dilemmas ◽  
Real Life ◽  
The Elderly ◽  
Ethics Committee ◽  
Resource Scarcity ◽  
Patients With Cancer ◽  
Deontological Ethics

BackgroundThe COVID-19 pandemic has aggressively reached the most vulnerable, not only the elderly but also patients with chronic conditions such as cancer. In this study, we present the outlines of ethical thinking and the measures implemented to try to respect our basic values of care, in the specific environment of an oncology hospital.MethodsOur ethics committee created an ethical watch system based on 24/7 shifts to assist practitioners in their daily decisions. We discuss the challenges faced by patients with cancer during the pandemic, such as access to critical care and ethical dilemmas in the context of resource scarcity, as well as the issue of isolation of patients. We also debate the restrictions in access to oncology care in a health context strongly ‘prioritised’ against COVID-19.ResultsIn all areas of an ethical dilemma, either for sorting out access to critical care or for the dramatic consequences of prolonged isolation of patients, our common thread was our attempt to protect, whenever possible, the principles of deontological ethics by strictly resisting utilitarian pressure. Respecting democratic health decision-making processes is a cornerstone of ethically relevant decisions, including in the context of a sanitary crisis.ConclusionThe role of an ethics committee related to real-life situations includes not only a reflexive perspective in respect of fundamental principles, but also the help to enlighten and resolve ethical dilemmas in complex clinical situations. This ethical watch team assists physicians in decision-making, promoting the supportive and palliative dimension of care with a holistic approach.

scholarly journals Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios

2020 ◽  
Author(s):  
Marcio Dorn ◽  
Eduardo Avila ◽  
Clarice Sampaio Alho ◽  
Alessandro Kahmann
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Resource Scarcity ◽  
Fine Tuning ◽  
Strategic Decision ◽  
Critical Element ◽  
Prior Probabilities ◽  
Qrt Pcr ◽  
Health Care Structure ◽  
Care Structure

Background: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: A Naïve-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests. Hemogram result data was used to predict qRT-PCR results in situations where the latter was not performed, or results are not yet available. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity. Data assessment can be performed in an individual or simultaneous basis, according to desired outcome. Based on hemogram data and background scarcity context, resource distribution is significantly optimized when model-based patient selection is observed, compared to random choice. The model can help manage testing deficiency and other critical circumstances. Conclusions: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

scholarly journals Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9482
Author(s):  
Eduardo Avila ◽  
Alessandro Kahmann ◽  
Clarice Alho ◽  
Marcio Dorn
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sensitivity And Specificity ◽  
Resource Scarcity ◽  
Fine Tuning ◽  
Strategic Decision ◽  
Critical Element ◽  
Workforce Management ◽  
Qrt Pcr ◽  
Health Care Structure

Background COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

‘I’m being forced to make decisions I have never had to make before’: Oncologists and the conundrums created by COVID-19.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 12001-12001
Author(s):  
Chithra R. Perumalswami ◽  
Emily Chen ◽  
Carly Martin ◽  
Susan Dorr Goold ◽  
Raymond G. De Vries ◽  
...  
Keyword(s):  
Decision Making ◽  
Ethical Dilemmas ◽  
Resource Scarcity ◽  
Optimal Decision ◽  
Individual Interviews ◽  
Integrating Technology ◽  
Qualitative Data Analysis Software ◽  
Technology And Communication ◽  
Optimal Decision Making ◽  
Seriously Ill

12001 Background: The COVID-19 pandemic has created conundrums for physicians. This study examines the experiences of oncologists who engage in complex decision-making regarding the use of chemotherapy in seriously ill persons in the context of the COVID-19 pandemic. Methods: Between January 2020 and August 2020, the authors conducted semi-structured, in-depth individual interviews with 22 purposefully sampled oncologists from practices enrolled in the Michigan Oncology Quality Consortium. Transcripts were double-coded and reconciled by consensus using qualitative data analysis software for thematic analysis. Results: Among the thematic clusters we identified, one was related to conundrums created by the COVID-19 pandemic. In this presentation, we report the results pertaining to three themes within this cluster: (1) the ethical dilemmas faced by oncologists due to the COVID-19 pandemic, (2) the need for both patients and oncologists to manage uncertainty and emotions, and (3) the importance and complexity of integrating technology and communication for seriously ill persons. Oncologists grappled with several conundrums including resource scarcity, resource allocation, delays in care, a duty to promote equity and non-abandonment, high levels of uncertainty and fear, and the importance of advanced care directives and end-of-life care planning. Non-abandonment featured as a coping mechanism for increased stress, and integration of communication with telemedicine was frequent and necessary. Conclusions: This study offers an in-depth exploration of the conundrums faced by oncologists due to the COVID-19 pandemic and how they navigated them. Optimal decision-making for seriously ill persons with cancer during the COVID-19 pandemic must include open acknowledgement of the ethical dilemmas faced, the heightened emotions experienced by both patients and their oncologists, and the urgent need for integrating technology with compassionate communication in determining patient preferences.

Sustainability criteria and cost–benefit analysis: an analytical framework for environmental–economic decision making at the project level

2000 ◽  
Vol 5 (3) ◽  
pp. 259-288 ◽  
Author(s):  
DIRGHA N. TIWARI
Keyword(s):  
Decision Making ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Resource Scarcity ◽  
Ecological Sustainability ◽  
Environmental Costs ◽  
Benefit Analysis ◽  
Economic Decision ◽  
Sustainability Criteria ◽  
Economic Decision Making

This article develops a framework for environmental–economic decision-making in a single project case that includes the ecological sustainability criteria, environmental costs, natural resource scarcity prices and local peoples’ preferences and presents a case study of the lowland irrigated agriculture system. The geographic information system (GIS) technique has been used for evaluating ecological criteria and integrating information for use in the cost–benefit analysis at different levels of computation process. The environmental costs and economic value of water associated with the lowland irrigated agriculture are estimated using both the direct and indirect economic valuation approaches. Various sets of alternatives were designed for promoting sustainable use of resources, and the net present value is estimated in each of these cases by incorporating environmental costs and economic values of water obtained from different methods. The cost–benefit analysis (CBA) carried out in these different cases indicated that diversification of crops, rather than the conventional monocropping system, would promote sustainable resource use and generate higher benefits to the farmers and society, if external costs, such as environmental costs and scarcity value of irrigation water, and ecological sustainability criteria are also considered in the economic decision-making process. The results of the case study also indicated that sustainability criteria could well be incorporated into the CBA in a single project case by addressing local people's concerns, resource scarcity values and ecological sustainability criteria with the use of spatial analysis techniques such as GIS.

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