Emerging investigators series: a critical review of decision support systems for water treatment: making the case for incorporating climate change and climate extremes

2017 ◽  
Vol 3 (1) ◽  
pp. 18-36 ◽  
Author(s):  
William J. Raseman ◽  
Joseph R. Kasprzyk ◽  
Fernando L. Rosario-Ortiz ◽  
Jenna R. Stewart ◽  
Ben Livneh
Keyword(s):  
Climate Change ◽  
Drinking Water ◽  
Decision Support ◽  
Water Treatment ◽  
Decision Support Systems ◽  
Critical Review ◽  
Support Systems ◽  
Drinking Water Treatment ◽  
Climate Extremes

We present potential impacts of climate on drinking water treatment and review decision support systems for these utilities.

Variations of disinfection by-product levels in small drinking water utilities according to climate change scenarios: a first assessment

2015 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
I. Delpla ◽  
A. Scheili ◽  
S. Guilherme ◽  
G. Cool ◽  
M. J. Rodriguez
Keyword(s):  
Climate Change ◽  
Drinking Water ◽  
Water Treatment ◽  
Drinking Water Treatment ◽  
Water Utilities ◽  
Climate Change Scenarios ◽  
Case Scenario ◽  
Worst Case ◽  
Small Water ◽  
Water Treatment Process

In Québec, Canada, shifts in climate patterns (i.e., rainfall increase) could have consequences on source water quality due to the intensification of surface/groundwater runoff contamination events, leading to a decline in drinking water treatment efficiency and ultimately disinfection by-products (DBPs) formation following treatment. To assess the impacts of climate change (CC) scenarios on DBP formation, a suite of models linking climate to DBPs was used. This study applies three emissions scenarios (B1, A1B and A2) for three 30-year horizons (2020, 2050 and 2080) in order to produce inputs to test several DBP models (total trihalomethanes (TTHMs), haloacetic acids and haloacetonitriles). An annual increase is estimated for all DBPs for each CC scenario and horizon. The highest seasonal increases were estimated for winter for all DBP groups or species. In the worst-case scenario (A2-2080), TTHMs could be affected more particularly during winter (+34.0%), followed by spring (+16.1%) and fall (+4.4%), whereas summer concentrations would remain stable (−0.3 to +0.4%). Potentially, small water utilities applying only a disinfection step could be more affected by rising TTHMs concentrations associated with CC than those having implemented a complete water treatment process (coagulation–flocculation, filtration and disinfection) with +13.6% and +8.2% increases respectively (A2-2080).

scholarly journals Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis (Preprint)

2017 ◽  
Author(s):  
Saif Khairat ◽  
David Marc ◽  
William Crosby ◽  
Ali Al Sanousi
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Critical Review ◽  
Task Analysis ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Care Providers

BACKGROUND Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall. OBJECTIVE The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance. METHODS A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician). RESULTS Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs. CONCLUSIONS This research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.

Application of decision support systems in scheduling/planning of manufacturing/service systems: a critical review

2016 ◽  
Vol 15 (3/4) ◽  
pp. 248 ◽  
Author(s):  
Ehsan Ardjmand ◽  
Iman Ghalehkhondabi ◽  
Gary R. Weckman ◽  
William A. Young <suffix>II</suffix>
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Critical Review ◽  
Support Systems ◽  
Service Systems
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