Role-Driven Context-Based Decision Support: Approach, Implementation and Lessons Learned

2015 ◽  
pp. 525-540
Author(s):  
Alexander Smirnov ◽  
Tatiana Levashova ◽  
Nikolay Shilov
Keyword(s):  
Decision Support ◽  
Lessons Learned

Malignant hyperthermia susceptibility: utilization of genetic results in an electronic medical record to increase safety

2020 ◽  
Vol 21 (17) ◽  
pp. 1207-1215
Author(s):  
Jordan F Baye ◽  
Natasha J Petry ◽  
Shauna L Jacobson ◽  
Michelle M Moore ◽  
Bethany Tucker ◽  
...  
Keyword(s):  
Decision Support ◽  
Malignant Hyperthermia ◽  
Clinical Decision Support ◽  
Perioperative Complications ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Malignant Hyperthermia Susceptibility ◽  
Alert System ◽  
Genotyping Platform

Aim: This manuscript describes implementation of clinical decision support for providers concerned with perioperative complications of malignant hyperthermia susceptibility. Materials & methods: Clinical decision support for malignant hyperthermia susceptibility was implemented in 2018 based around our pre-emptive genotyping platform. We completed a brief descriptive review of patients who underwent pre-emptive testing, focused particularly on RYR1 and CACNA1S genes. Results: To date, we have completed pre-emptive genetic testing on more than 10,000 patients; 13 patients having been identified as a carrier of a pathogenic or likely pathogenic variant of RYR1 or CACNA1S. Conclusion: An alert system for malignant hyperthermia susceptibility – as an extension of our pre-emptive genomics platform – was implemented successfully. Implementation strategies and lessons learned are discussed herein.

scholarly journals Assessing a Decision Support Tool for SOC Analysts

2021 ◽  
Author(s):  
Jassim Happa ◽  
Ioannis Agrafiotis ◽  
Martin Helmhout ◽  
Thomas Bashford-Rogers ◽  
Michael Goldsmith ◽  
...  
Keyword(s):  
Decision Support ◽  
Mixed Method ◽  
User Study ◽  
Situational Awareness ◽  
Decision Support Tool ◽  
Lessons Learned ◽  
Visualization Tool ◽  
Support Tool ◽  
Response Strategies ◽  
Method Approach

In recent years, many tools have been developed to understand attacks that make use of visualization, but few examples aims to predict real-world consequences. We have developed a visualization tool that aims to improve decision support during attacks. Our tool visualizes propagation of risks from IDS and AV-alert data by relating sensor alerts to Business Process (BP) tasks and machine assets: an important capability gap present in many Security Operation Centres (SOCs) today. In this paper we present a user study in which we evaluate the tool's usability and ability to deliver situational awareness to the analyst. Ten analysts from seven SOCs performed carefully designed tasks related to understanding risks and prioritising recovery decisions. The study was conducted in laboratory conditions, with simulated attacks, and used a mixed-method approach to collect data from questionnaires, eyetracking and voice-recorded interviews. The findings suggest that providing analysts with situational awareness relating to business priorities can help them prioritise response strategies. Finally, we provide an in-depth discussion on the wider questions related to user studies in similar conditions as well as lessons learned from our user study and developing a visualization tool of this type.

scholarly journals Bridging Science and Practice-Importance of Stakeholders in the Development of Decision Support: Lessons Learned

2021 ◽  
Vol 13 (10) ◽  
pp. 5744
Author(s):  
Innocent K. Tumwebaze ◽  
Joan B. Rose ◽  
Nynke Hofstra ◽  
Matthew E. Verbyla ◽  
Daniel A. Okaali ◽  
...  
Keyword(s):  
Decision Support ◽  
Stakeholder Participation ◽  
Scientific Data ◽  
Lessons Learned ◽  
Future Research ◽  
Decision Support Tools ◽  
Local Data ◽  
Funding Sources ◽  
Sanitation And Hygiene ◽  
Support Tools

User-friendly, evidence-based scientific tools to support sanitation decisions are still limited in the water, sanitation and hygiene (WASH) sector. This commentary provides lessons learned from the development of two sanitation decision support tools developed in collaboration with stakeholders in Uganda. We engaged with stakeholders in a variety of ways to effectively obtain their input in the development of the decision support tools. Key lessons learned included: tailoring tools to stakeholder decision-making needs; simplifying the tools as much as possible for ease of application and use; creating an enabling environment that allows active stakeholder participation; having a dedicated and responsive team to plan and execute stakeholder engagement activities; involving stakeholders early in the process; having funding sources that are flexible and long-term; and including resources for the acquisition of local data. This reflection provides benchmarks for future research and the development of tools that utilize scientific data and emphasizes the importance of engaging with stakeholders in the development process.

Lessons Learned From Data Mining, Decision Support and Collaboration

2003 ◽  
pp. 237-246
Author(s):  
Dunja Mladenić ◽  
Nada Lavrač ◽  
Marko Bohanec
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Lessons Learned

Visualization-Based Decision Support Systems

2013 ◽  
pp. 162-180
Author(s):  
Vicki L. Sauter ◽  
Srikanth Mudigonda ◽  
Ashok Subramanian ◽  
Ray Creely
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Lessons Learned ◽  
Decision Makers ◽  
Specific Information ◽  
Visualization Software ◽  
General Rules ◽  
Visualization Technology ◽  
Visualization Algorithms

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.

A Study of Organizational Narrative Simulation for Decision Support

2013 ◽  
pp. 179-192
Author(s):  
C. L. Yeung ◽  
C. F. Cheung ◽  
W. M. Wang ◽  
E. Tsui
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Pilot Study ◽  
Reference Site ◽  
Lessons Learned ◽  
Implementation Processes ◽  
Organizational Narrative ◽  
Decision Making Processes ◽  
New Challenges ◽  
Narrative Simulation

This paper presents an overview of current decision making approaches. For some approaches abstract information is provided, whereas others require a large amount of labor and time resources to facilitate decision making. However, few address the issues of assisting participants in learning how to make decisions and provide prompt responses to the situations. Harnessing lessons learned from making inappropriate decisions is expensive. To redress this problem, this paper presents a pilot study of the investigation of the psychological behaviors of humans to improve decision making processes with the use of organizational narrative simulation (ONS). By using the ONS method, possible and plausible narrative-based environments can be simulated. Participants can take actions based on their decisions; they can also observe the changes and the consequences. The decisions for handling new challenges generated purposely are validated in a trial that allows prompt responses to the situations. The ONS method is implemented in a selected reference site. The implementation processes, findings, and benefits are presented.

A Study of Organizational Narrative Simulation for Decision Support

2011 ◽  
Vol 2 (3) ◽  
pp. 26-41
Author(s):  
C. L. Yeung ◽  
C. F. Cheung ◽  
W. M. Wang ◽  
E. Tsui
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Pilot Study ◽  
Reference Site ◽  
Lessons Learned ◽  
Implementation Processes ◽  
Organizational Narrative ◽  
Decision Making Processes ◽  
New Challenges ◽  
Narrative Simulation

This paper presents an overview of current decision making approaches. For some approaches abstract information is provided, whereas others require a large amount of labor and time resources to facilitate decision making. However, few address the issues of assisting participants in learning how to make decisions and provide prompt responses to the situations. Harnessing lessons learned from making inappropriate decisions is expensive. To redress this problem, this paper presents a pilot study of the investigation of the psychological behaviors of humans to improve decision making processes with the use of organizational narrative simulation (ONS). By using the ONS method, possible and plausible narrative-based environments can be simulated. Participants can take actions based on their decisions; they can also observe the changes and the consequences. The decisions for handling new challenges generated purposely are validated in a trial that allows prompt responses to the situations. The ONS method is implemented in a selected reference site. The implementation processes, findings, and benefits are presented.

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.

Deployment and integration of a cognitive technology in China: Experiences and lessons learned.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6538-6538
Author(s):  
Tianle Li ◽  
Cheng Chen ◽  
Shan-shan Zhang ◽  
Irene Dankwa-Mullan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Decision Support ◽  
Treatment Options ◽  
Care Quality ◽  
Lessons Learned ◽  
Tumor Board ◽  
Case Review ◽  
Clinical Workflow ◽  
Ibm Watson ◽  
Risk Reducing

6538 Background: Cognitive technologies are rapidly being introduced in oncology for decision-support, prescribing therapy, predicting risk, reducing medical errors and for care management. Few studies have reported on successful approaches for clinical adoption. We report the early adopter experience of BahealIntelligenceTechnologyCo., Ltd. (Baheal), across China within a 2-year period (April 2017-January 2019). We also describe lessons and experiences of oncology users. Methods: Baheal developed collaborative agreements for use of IBM Watson for Oncology (WFO) in 96 hospitals across 8 provinces. Key opinion leaders who saw the potential for AI were recruited as champion advocates. A 29-item survey conducted included usability and integration within clinical workflow. 85 questionnaires were distributed to oncologists who were major WfO users; 51 were completed. All questionnaires were completed anonymously and de-identified prior to analysis. Results: As of January 31, 2019, 866 physicians have entered a total of 52,537 cancer cases into WfO. Most users approved of both the quality (44/51, 86.3%) and comprehensibility (45/51, 88.2%) of treatment options, rationales, and literature references. WfO was most frequently applied in the context of inpatient cases reviewed by a multidisciplinary tumor board (MDT) (44/51, 86.3%). A lack of locally available treatments in WfO was cited as an area for improvement by two-thirds of users (34/51, 66.7%). CDS was rated from 0 (lowest) to 10 (highest) for each of the following uses: EBM medical education (8.1); assistance with literature (7.7); medical care quality control (7.3); second opinion consultations (7.0); case review with tumor board (6.9); and decision support (6.4). Overall, users were willing to recommend CDS to patients and other clinicians (7.3). Conclusions: WfO CDS, employed in a variety of settings, was viewed positively by more than 86% of users, with perceived benefits differing by context. Future incorporation of locally available treatments and understanding reasons for their omission in CDS may increase perceived value, improve standardization, quality of cancer care and equity of care in China.

Sign in / Sign up

Export Citation Format

Share Document