Use of Decision Support Systems to Address Contaminated Coastal Sediments: Experience in the United States

Abstract Web services providing machine-accessible interfaces to environmental data are now commonplace. Building on this, a current trend is to expand these web services to provide on-demand access to model and analysis services. This progression suggests the future possibility of cloud-based decision support systems (DSSs) integrating distributed data and analysis services delivered through a host of providers. Such distributed environmental DSSs have many potential benefits, but would require highly scalable and responsive web services. The objective of this study is to assess the current feasibility of building distributed environmental DSSs from existing web services in the United States. Results show that, of the many available web services providing information about soils, river network topology, watersheds, streamflow, etc., response times are often only a few seconds for a small project area, but can grow exponentially as the project area increases. On-demand watershed delineation remains a slow-to-respond service relative to the other services tested. Also, the results suggest the need to better co-locate servers near client applications to speed up response times. Collectively, these results provide specific areas where future research is needed in order to achieve the vision of on-demand distributed environmental DSSs.

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Historical Overview of Decision Support Systems (DSS)

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch275 ◽

2011 ◽

pp. 1753-1758 ◽

Cited By ~ 1

Author(s):

Udo Richard Averweg

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

United States Of America ◽

Support Systems ◽

The United States ◽

Future Trends ◽

Historical Overview ◽

Essential Information ◽

Organizational Perspective ◽

The Subject

During the late 1970s the term “decision support systems” was first coined by P. G. W. Keen, a British Academic then working in the United States of America. In 1978, Keen and Scott Morton published a book entitled, Decision Support Systems: An Organizational Perspective (Keen & Scott Morton, 1978), wherein they defined the subject title as computer systems having an impact on decisions where computer and analytical aids can be of value but where the manager’s judgment is essential. Information systems (IS) researchers and technologists have developed and investigated decision support systems (DSS) for more than 35 years (Power, 2003b). The structure of this article is as follows: The background to DSS will be given. Some DSS definitions, a discussion of DSS evolution, development of the DSS field and frameworks are then presented. Some future trends for DSS are then suggested.

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An interactive online database for the selection of woody ornamental plants

HortTechnology ◽

10.21273/horttech.13.3.0562 ◽

2003 ◽

Vol 13 (3) ◽

pp. 562-568

Author(s):

Jonathan M. Lehrer ◽

Mark H. Brand

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Survey Data ◽

Web Site ◽

Support Systems ◽

Ornamental Plants ◽

The United States ◽

Online Questionnaire ◽

Woody Ornamental ◽

Strongly Agree

Web sites such as the University of Connecticut (UConn) Plant Database allow large volumes of information and images to be stored, published and accessed by users for the purpose of informed decision-making. Sorting information on the World Wide Web (Web) can be difficult, especially for novice users and those interested in quick results. The advent of Internet search and retrieval software fosters the creation of interactive decision support systems. The Plant Selector was designed to complement the UConn Plant Database plant encyclopedia by allowing Web site users to generate lists of woody ornamental plants that match specific criteria. On completion of an HTML-based search form by users, a Web-enabled database is searched and lists of matching plants are presented for review. To facilitate analysis of the Plant Selector's efficacy, an online questionnaire was implemented to solicit user feedback. Survey data from 426 responses to the online evaluation tool were analyzed both to understand user demographics and gauge satisfaction with the Plant Selector module. Survey data revealed that most Plant Selector users are between 40 to 65 years of age and homeowners with minimal horticultural experience. A large percentage of Web site visitors (68%) is located across the United States beyond Connecticut and the New England region. The great majority of survey respondents (65%) use this tool to select plants for the home landscape. Most (77%) either agree or strongly agree that the Plant Selector is easy to use and delivers results that are useful (66%), while 70% agree or strongly agree that the categories used by the Plant Selector are sufficient. The survey results in general suggest that Web-based decision support systems may serve useful roles in the field of horticulture education.

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The Human Dimensions of Spatial, Pre-Wildfire Planning Decision Support Systems: A Review of Barriers, Facilitators, and Recommendations

Forests ◽

10.3390/f12040483 ◽

2021 ◽

Vol 12 (4) ◽

pp. 483

Author(s):

Melanie Colavito

Keyword(s):

Risk Assessment ◽

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

The United States ◽

Human Dimensions ◽

Wildfire Risk ◽

Forest Fuels ◽

Fuels Treatment ◽

Wildfire Planning

Decision support systems (DSSs) are increasingly common in forest and wildfire planning and management in the United States. Recent policy direction and frameworks call for collaborative assessment of wildfire risk to inform fuels treatment prioritization using the best available science. There are numerous DSSs applicable to forest and wildfire planning, which can support timely and relevant information for decision making, but the use and adoption of these systems is inconsistent. There is a need to elucidate the use of DSSs, specifically those that support pre-wildfire, spatial planning, such as wildfire risk assessment and forest fuels treatment prioritization. It is important to understand what DSSs are in use, barriers and facilitators to their use, and recommendations for improving their use. Semi-structured interviews with key informants were used to assess these questions. Respondents identified numerous barriers, as well as recommendations for improving DSS development and integration, specifically with respect to capacity, communication, implementation, question identification, testing, education and training, and policy, guidance, and authorities. These recommendations can inform DSS use for wildfire risk assessment and treatment prioritization to meet the goals of national policies and frameworks. Lastly, a framework for organizing spatial, pre-wildfire planning DSSs to support end-user understanding and use is provided.

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Computerised decision support systems: Aids to rational and sustainable weed management

Canadian Journal of Plant Science ◽

10.4141/cjps96-002 ◽

1996 ◽

Vol 76 (1) ◽

pp. 3-7 ◽

Cited By ~ 3

Author(s):

John T. O'Donovan

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Weed Management ◽

Support Systems ◽

The United States ◽

Fertilizer Application ◽

Weed Species ◽

Relative Time ◽

Weed Density ◽

Crop Density

Computerised decision support systems offer an ideal means of achieving economical, environmentally safe, and sustainable weed management. Systems based on weed economic threshold concepts have been developed in the United States and Europe. In several of these, weed density is the sole variable used for estimating crop yield losses due to weeds. In Canada, there have been few attempts to develop computerised decision support systems to facilitate rational weed management. A notable exception is a microcomputer program developed by the Manitoba Department of Agriculture for assessing the economics of wild oat (Avena fatua L.) control in cereal and oilseed crops. A relative time of emergence variable is a crucial component of the program There is a need to develop a more comprehensive system for managing multiple weed species in a range of crops grown in Canada. For reliable recommendations to be made, input requirements for the system should include information on the relative time of emergence of the crop and weed, crop density, environmental factors and method of fertilizer application, as well as weed density. In terms of output, the system should indicate realistic weed monitoring procedures, and the long-term bioeconomic implications of seed production by uncontrolled weeds. Key words: Bioeconomic models, weed economic thresholds, decision support systems, rational weed management

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Impact of Protocol Watch on Compliance With the Surviving Sepsis Campaign

American Journal of Critical Care ◽

10.4037/ajcc2011421 ◽

2011 ◽

Vol 20 (4) ◽

pp. 313-321 ◽

Cited By ~ 20

Author(s):

Karen K. Giuliano ◽

Michele Lecardo ◽

LuAnn Staul

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

The United States ◽

Teaching Hospitals ◽

Surviving Sepsis Campaign ◽

Care Protocol

Purpose Clinical decision support systems are intended to improve patients’ care and outcomes, particularly when such systems are present at the point of care. Protocol Watch was developed as a bedside clinical decision support system to improve clinicians’ adherence to the Surviving Sepsis Campaign guidelines. This pre/post-intervention pilot study was done to evaluate the effect of Protocol Watch on compliance with 5 guidelines from the Surviving Sepsis Campaign. Methods Preintervention data on rates and time to complete the resuscitation and management bundles from the Surviving Sepsis Campaign and time to administer antibiotics were collected from intensive care units at 2 large teaching hospitals in the United States. Training on the Protocol Watch application was then provided to clinical staff in the units, and Protocol Watch was installed at all critical care beds in both hospitals. Data were collected on rates and time to completion for 5 Surviving Sepsis Campaign guidelines after installation of Protocol Watch, and univariate analyses were done to evaluate the effect of Protocol Watch on compliance with the guidelines. Results Implementation of Protocol Watch was associated with significant improvements in compliance with the resuscitation bundle (P = .01) and decreased time to administer antibiotics (P = .006). No significant changes were achieved for compliance with the management bundle or time to complete the resuscitation or management bundles. Conclusions Clinical decision support systems such as Protocol Watch may improve adherence to the Surviving Sepsis Campaign guidelines, which potentially may contribute to reduced morbidity and mortality for critically ill patients with sepsis.

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Clinical Decision Support Systems and Computerized Provider Order Entry: Contributions from 2020

Yearbook of Medical Informatics ◽

10.1055/s-0041-1726534 ◽

2021 ◽

Vol 30 (01) ◽

pp. 172-175

Author(s):

Damian Borbolla ◽

Grégoire Ficheur ◽

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

The United States ◽

Order Entry ◽

Public Health Response ◽

Computerized Provider Order Entry

Summary Objectives: To summarize research contributions published in 2020 in the field of clinical decision support systems (CDSS) and computerized provider order entry (CPOE), and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2021. Methods: Two bibliographic databases were searched for papers referring to clinical decision support systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by seven external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation. Results: A total of 1,919 articles were retrieved. 15 best paper candidates were selected, the reviews of which resulted in the selection of two best papers. One paper reports on the use of electronic health records to support a public health response to the COVID-19 pandemic in the United States. The second paper proposes a combination of CDSS and telemedicine as a technology-based intervention to improve the outcomes of depression as part of a cluster trial. Conclusions: As shown by the number and the variety of works related to clinical decision support, research in the field is very active. This year's selection highlighted the application of CDSS to fight COVID-19 and a combined technology-based strategy to improve the treatment of depression.

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