Lessons learned from the design of the decision support system used in the Hurricane Katrina evacuation decision

2007 ◽  
Author(s):  
Alex Kirlik
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Hurricane Katrina ◽  
Support System ◽  
Lessons Learned

Lessons Learned from the Design of the Decision Support System Used in the Hurricane Katrina Evacuation Decision

2007 ◽  
Vol 51 (4) ◽  
pp. 253-257 ◽  
Author(s):  
Alex Kirlik
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Hurricane Katrina ◽  
Support System ◽  
Lessons Learned ◽  
Cognitive Engineering ◽  
High Stakes ◽  
Engineering Research ◽  
Design Deficiencies ◽  
Uncertain Time

Computer-based decision support systems are increasingly used to aid human decision makers in dynamic, uncertain, time-stressed and high-stakes contexts. The decision of whether, and if so, when to evacuate New Orleans as Hurricane Katrina approached landfall is a prime example. An evaluation of the “HURREVAC” decision support system (DSS) used during Katrina is presented. The evaluation is based on real-time screen-shots of the graphical and numerical information displayed to emergency response managers and other users. While the system is clearly an improvement over methods used prior to advances in information technology and realtime networking, design deficiencies were identified as well. The most crucial of these concern insufficient resources provided by the design to support users in reasoning effectively about uncertainty, and about the interactions among uncertainty and other aspects of the decision situation. The paper concludes by providing lessons learned and by identifying needs for cognitive engineering research to improve future DSS design in operational contexts.

scholarly journals A Review of Information Fusion Methods for Gas Turbine Diagnostics

2019 ◽  
Vol 11 (22) ◽  
pp. 6202 ◽  
Author(s):  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Gas Turbine ◽  
Information Fusion ◽  
Support System ◽  
Probabilistic Reasoning ◽  
Decision Fusion ◽  
Lessons Learned ◽  
Diagnostic Systems ◽  
Fusion Methods

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

scholarly journals Tools for Risk Management of Technical Facilities Operation

2020 ◽  
Vol 5 (4) ◽  
pp. 494-500
Author(s):  
Dana Prochazkova ◽  
Jan Prochazka
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Management Plan ◽  
Life Cycles ◽  
Present Knowledge ◽  
Lessons Learned ◽  
Risk Management Plan ◽  
The Past

The article shows the results of research directed to detection of technical facilities accidents and failures sources at their operation. The research aim is to create the effective tools for management of risks so the coexistence of technical facilities with their vicinity would be ensured throughout their life cycles. The problems solution way is based on the simultaneously preferred concept, in which the safety is preferred over the reliability.  Respecting the present knowledge on technical facilities´ safety and the lessons learned from the past technical facilities accidents and  failures, the causes of which were connected with their operation, two tools are developed:  Decision Support System and Risk Management Plan that were reviewed by experts and tested in practice.

scholarly journals A Collaborative Decision Support System Framework for Vertical Farming Business Developments

2021 ◽  
Vol 13 (1) ◽  
pp. 34-66
Author(s):  
Francis J. Baumont De Oliveira ◽  
Scott Ferson ◽  
Ronald Dyer
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Environmental Sustainability ◽  
Financial Risk ◽  
Business Models ◽  
Partial Information ◽  
Lessons Learned ◽  
Business Decisions ◽  
Vertical Farming

The emerging industry of vertical farming (VF) faces three key challenges: standardisation, environmental sustainability, and profitability. High failure rates are costly and can stem from premature business decisions about location choice, pricing strategy, system design, and other critical issues. Improving knowledge transfer and developing adaptable economic analysis for VF is necessary for profitable business models to satisfy investors and policy makers. A review of current horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. Data from the literature alongside lessons learned from industry practitioners are centralised in the proposed DSS, using imprecise data techniques to accommodate for partial information. The DSS evaluates business sustainability using financial risk assessment. This is necessary for complex/new sectors such as VF with scarce data.

Spruce budworm decision support system: lessons learned in development and implementation

2000 ◽  
Vol 27 (1-3) ◽  
pp. 293-314 ◽  
Author(s):  
David A MacLean ◽  
Kevin B Porter ◽  
Wayne E MacKinnon ◽  
Kathy P Beaton
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Spruce Budworm ◽  
Lessons Learned

Non-user physician perspectives about an oncology clinical decision-support system: A qualitative study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14061-e14061
Author(s):  
Hermano Alexandre Lima Rocha ◽  
Srinivas Emani ◽  
Carlos Andre Moura Arruda ◽  
Rubina Rizvi ◽  
Pamela Garabedian ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Social Contexts ◽  
Lessons Learned ◽  
Structured Interviews ◽  
Extensive Experience

e14061 Background: Advances in artificial intelligence (AI) continue to expand capabilities within the healthcare domain, particularly in the discipline of oncology. Watson For Oncology (WfO) is an AI-enabled clinical decision support system that presents potential therapeutic options for cancer-treating physicians. The objectives of this study were to identify non-user physicians’ expectations, perceived challenges and benefits of WfO use in Brazil. Methods: The study took place at Instituto do Câncer do Ceará (ICC), a Brazilian oncology hospital that implemented WfO in December 2017, but not all physicians adopted the tool. Physicians who had not used WfO (n = 5) were recruited through purposive sampling identified with the assistance of local research personnel. Semi-structured interviews were conducted in Portuguese and later de-identified and transcribed into English. A thematic analysis of interview data based on grounded theory by two members of the research team with extensive experience in qualitative data analysis was conducted. Results: Non-user physicians had positive perceptions about WfO, along with several concerns and uncertainties. They expected that WfO would be easy to learn, useful, and helpful. Physicians perceived that WfO would provide a more standardized approach to treatment than care without it. They also believed that WfO would play a supportive and not a substitute role in care especially for complex cases in which the physicians had more in-depth knowledge of a patient and already had an established patient-provider relationship. Physicians did expect WfO use to negatively impact productivity, specifically through longer office times per patient because of the need to enter data and review recommendations. Physicians questioned whether the use of WfO would negatively impact their autonomy and role in providing care. Finally, physicians also questioned whether the treatment suggested by WfO would fit the social context of a low-middle income country such as Brazil with limited technological and economic resources. Conclusions: The implementation of US-developed AI technologies, such as WfO, should be further explored in different social and economic contexts. Physician concerns about productivity and autonomy need to be assessed and addressed in AI implementation; one strategy is to leverage previous lessons learned from electronic health record (EHR) implementations. This study is a critical step in understanding potential user perspectives in adopting a new AI tool in different social contexts.

FHWA's Maintenance Decision Support System Project

2005 ◽  
Vol 1911 (1) ◽  
pp. 133-142
Author(s):  
William P. Mahoney ◽  
Ben Bernstein ◽  
Jamie Wolff ◽  
Seth Linden ◽  
William L. Myers ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Management Program ◽  
Lessons Learned ◽  
Road Maintenance ◽  
The Status ◽  
Maintenance Decision ◽  
Functional Prototype ◽  
System Project

The Federal Highway Administration's Office of Transportation Operations Road Weather Management Program began a project in FY 1999 to develop a prototype winter road maintenance decision support system (MDSS). The MDSS capabilities are based on feedback received by the FHWA in 2001 from maintenance managers at a number of state departments of transportation (DOTs) as part of an initiative to capture surface transportation weather decision support requirements. The MDSS project goal is to seed the implementation of advanced decision support services provided by the private sector for state DOTs. This has been achieved by developing core software capabilities that serve as a basis for these tailored products. After the 2001 user needs assessment was completed, the MDSS program was extended with the objective of developing and demonstrating a functional prototype MDSS. Field demonstrations of the prototype MDSS were conducted in Iowa between February and April 2003, and during the winter of 2004. The performance of the prototype MDSS was much improved during the second winter. The weather and road condition predictions were more accurate, and the treatment recommendations generated by the system were reasonable given the predicted conditions. Iowa garage supervisors actively considered the treatment guidance, and on occasion they successfully used the recommended treatments without modification. This paper describes the status of the MDSS project, results and lessons learned from the field demonstrations, and future development efforts.

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