Effective decision-making: applying the theories to nursing practice

2020 ◽  
Vol 29 (2) ◽  
pp. 98-101
Author(s):  
Samantha Watkins
Keyword(s):  
Decision Making ◽  
Patient Assessment ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Decision Making Processes ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Future Practice

Many theories have been proposed for the decision-making conducted by nurses across all practices and disciplines. These theories are fundamental to consider when reflecting on our decision-making processes to inform future practice. In this article three of these theories are juxtaposed with a case study of a patient presenting with an ST-segment elevation myocardial infarction (STEMI). These theories are descriptive, normative and prescriptive, and will be used to analyse and interpret the process of decision-making within the context of patient assessment.

Clots Kill: Hematologic Pharmacology for ST-Segment Elevation Myocardial Infarction

2012 ◽  
Vol 32 (6) ◽  
pp. 35-41
Author(s):  
Stacy H. James
Keyword(s):  
Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Antiplatelet Agents ◽  
Thrombin Inhibitors ◽  
Fibrinolytic Agents ◽  
St Segment Elevation ◽  
Coronary Syndrome ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment

Drugs that work on the hematologic system play an important role in helping to limit the morbidity and mortality that can be associated with an acute coronary syndrome. The pharmacology of the fibrinolytic agents, thrombin inhibitors, and antiplatelet agents is described. A case study of a woman having an ST-segment elevation myocardial infarction is reviewed to highlight the importance of drugs that work on the hematologic system.

Designing a Business Analytics Culture in Organizations in India

2021 ◽  
pp. 670-678
Author(s):  
Tanushri Banerjee ◽  
Arindam Banerjee
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Data Organization ◽  
Business Analytics ◽  
Domain Specific ◽  
Consumer Lending ◽  
Effective Decision ◽  
Effective Decision Making

There are several challenges faced by decision makers while deploying Business Analytics in their organization. There may not be one resolution approach that is suitable for creating a Business Analytics culture in all organizations. However, it is easy to perceive that most India-based organizations may have similar issues of data organization that may be impeding their progression in the field of Analytics. Based on their research, the authors have proposed a framework for adoption of Analytics in Indian firms in their book “Weaving Analytics for Effective Decision Making” by SAGE. They propose to use that model for explaining certain domain specific adoption of Business Analytics in organizations in India. They have used a case study of a Global Bank which is in the process of establishing its consumer lending USA operations, an offshore captive operation, in India to describe the process of building an Analytics team in an organization in India. Data processed using R has been added as screenshots for supporting the findings.

A descriptive case study of effective decision-making of a local government organization in the United States of America

2020 ◽  
Vol 16 (3) ◽  
pp. 279-297
Author(s):  
Jennifer Capler
Keyword(s):  
United States ◽  
Decision Making ◽  
Local Government ◽  
United States Of America ◽  
The United States ◽  
Content Type ◽  
Government Organizations ◽  
Effective Decision ◽  
Effective Decision Making

PurposeThis article details a qualitative descriptive case study of affective factors of effective decision-making of one local government organization in the United States of America. The specific problem was that many elected American local government representatives lack effective decision-making strategies. This research focus indicated a lack of qualitative research on the real-world experience of factors that were taken into consideration during decision-making within American local government organizations.Design/methodology/approachUsing a local government organization in southwest Illinois, elected representatives were interviewed and observed. The interviews and observations surfaced how the representatives made decisions. Data were analyzed using manual coding and theming to determine themes and patterns.FindingsThe results produced six themes about factors, including emotional intelligence, which impacted decision-making. They are: (1) remembering the past, (2) communication and respect, (3) spurring economic growth and development, (4) fairness, (5) recognizing and removing emotions and bias and (6) accountability.Research limitations/implicationsBeing a single case study, this research is limited in generalization. The research was limited to the identification of current, real-world experience of elected local government representatives.Practical implicationsThe findings of this research can be used to create more effective decision-making practices for local government organizations of similar size.Originality/valueThis is the first study to review, in-depth, the decision-making and emotional intelligence factors of local government organizations in the United States of America. The conceptual background, discussion, implications to local government organizations, limitations and recommendations for future studies are discussed.

scholarly journals Decision-Making Enhancement in a Big Data Environment: Application of the K-Means Algorithm to Mixed Data

2019 ◽  
Vol 9 (4) ◽  
pp. 293-302
Author(s):  
Oded Koren ◽  
Carina Antonia Hallin ◽  
Nir Perel ◽  
Dror Bendet
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Mixed Data ◽  
Systems Research ◽  
Information Systems Research ◽  
Effective Decision ◽  
Data Ecosystem ◽  
Effective Decision Making ◽  
Business Requirements

Abstract Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.

Effective Decision Making for Knowledge Development in Higher Education

2018 ◽  
pp. 382-399
Author(s):  
Nwachukwu Prince Ololube ◽  
Erebagha Theophilus Ingiabuna ◽  
Undutimi Johnny Dudafa
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Poor Quality ◽  
Public And Private ◽  
The Poor ◽  
Decision Making Processes ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Types Of Information ◽  
University Leaders

Making decisions is the most important task of university leaders or managers and it is often the most difficult task. This chapter offers a step-by-step decision-making procedure for solving complex problems. It outlines the concept of decision-making and processes for both public and private decision-making agendas, using different decision criteria and different types of information. This chapter also describes barriers to effective decision making and decisions that must be made in conditions of certainty and uncertainty. Using a descriptive and suggestive research design, multiple statistical procedures; the results revealed that the types, styles and barrier to decision making processes are significantly related to the poor quality management of higher education in Nigeria? It is therefore imperative that institutional leaders are thoughtful and precise decision makers. This study recommends that the process of decision making ought not to be reactionary, but systematically planned and swift as well as planning for the unanticipated and unintentional situations as they arise.

Effective Decision Making for Knowledge Development in Higher Education

2021 ◽  
pp. 546-563
Author(s):  
Nwachukwu Prince Ololube ◽  
Erebagha Theophilus Ingiabuna ◽  
Undutimi Johnny Dudafa
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Poor Quality ◽  
Public And Private ◽  
The Poor ◽  
Decision Making Processes ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Types Of Information ◽  
University Leaders

Making decisions is the most important task of university leaders or managers and it is often the most difficult task. This chapter offers a step-by-step decision-making procedure for solving complex problems. It outlines the concept of decision-making and processes for both public and private decision-making agendas, using different decision criteria and different types of information. This chapter also describes barriers to effective decision making and decisions that must be made in conditions of certainty and uncertainty. Using a descriptive and suggestive research design, multiple statistical procedures; the results revealed that the types, styles and barrier to decision making processes are significantly related to the poor quality management of higher education in Nigeria? It is therefore imperative that institutional leaders are thoughtful and precise decision makers. This study recommends that the process of decision making ought not to be reactionary, but systematically planned and swift as well as planning for the unanticipated and unintentional situations as they arise.

Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

2012 ◽  
pp. 242-261 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Control ◽  
Future Research ◽  
Manufacturing Environment ◽  
Planning And Scheduling ◽  
Future Research Directions ◽  
Effective Decision ◽  
Effective Decision Making

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

2012 ◽  
pp. 171-192 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Control ◽  
Future Research ◽  
Manufacturing Environment ◽  
Planning And Scheduling ◽  
Future Research Directions ◽  
Effective Decision ◽  
Effective Decision Making

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

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