Using reduced-processing training to improve decision efficiency among perfectionists

2021 ◽  
Author(s):  
Rebecca Y. M. Liu ◽  
Ben W. Morrison ◽  
Mark W. Wiggins ◽  
Nathan C. Perry
Keyword(s):  
Decision Efficiency

scholarly journals Profiles of human capital and strategic technological decisions on turbulence environment

2019 ◽  
Vol 7 (1) ◽  
pp. 39
Author(s):  
D. Pérez ◽  
L. Saiz-Bárcena ◽  
M.A. Manzanedo ◽  
A. Pérez
Keyword(s):  
Human Capital ◽  
Business Strategies ◽  
Strategic Decisions ◽  
Organizational Contexts ◽  
Industrial Companies ◽  
Technology Industry ◽  
Development Unit ◽  
Principal Finding ◽  
The Relationship ◽  
Decision Efficiency

<p>The objective of this article is to examine the absorptive capacity in the technology industry and aspires to recognize how firms can manage their strategic decisions in the turbulence contexts. In particular, we examine how organizations can strengthen their organizational contexts in order to absorb knowledge. From the knowledge management literature, this investigation extends our perception of the relationship between the human capital profiles (organization, research and development unit, and recent incorporations) and technological decision-making. Through the SEPI Foundation, a balanced panel of 1,220 Spanish industrial companies has used that answer to the Survey of Business Strategies (SBS) for a threeyear period, which signifies a total of 3,660 cases. The principal finding is the presence of high levels of human resources to understand a decision efficiency process. It also highlights its relationship to the firm’s technological committee. These contributions are notable for both researchers and practitioners. It could be stimulating to expand the study to the association between human capital profiles and other strategic technological decisions, as the preparation of an innovation plan or the measurement of innovation performance.</p>

Using Intelligent Tools to Support Clinical Decision Making

2021 ◽  
pp. 555-567
Author(s):  
Nilmini Wickramasinghe ◽  
Jonathan L Schaffer
Keyword(s):  
Decision Making ◽  
Clinical Case ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Care Pathways ◽  
Detection Model ◽  
Risk Detection ◽  
Intelligent Tools ◽  
Specific Care ◽  
Decision Efficiency

Intelligent tools and collaborative systems can be used in healthcare contexts to support clinical decision making. Such an approach is concerned with identifying the way in which information is gathered and decisions are made along specific care pathways. This study develops a real-time collaborative system using an intelligent risk detection model (IRD) to improve decision efficiency in the clinical case of patients undergoing hip or knee arthroplasty. The benefits of adopting this improved clinical decision-making solution include increasing awareness, supporting communication, improving the decision making process for patients and caregivers while also improving information sharing between surgeons as key collaborative parties in the research case. This in turn leads to higher levels of patient and clinical satisfaction and better clinical outcomes.

Leverage Healthcare Data Assets with Predictive Analytics

2017 ◽  
pp. 823-837
Author(s):  
Nilmini Wickramasinghe ◽  
Hoda Moghimi ◽  
Jonathan L. Schaffer
Keyword(s):  
Predictive Analytics ◽  
Healthcare Sector ◽  
Hospital Data ◽  
Healthcare Organizations ◽  
Data Set ◽  
Private Hospitals ◽  
Healthcare Data ◽  
Disparate Data ◽  
Tools And Techniques ◽  
Decision Efficiency

Multi-spectral data residing in disparate data bases represents a critical raw asset for today's healthcare organizations (). However, in order to gain maximum value from such data, it is essential to apply prudent technology solutions and tailored analytic techniques. The following chapter proposes how the application of bespoke predictive analytic tools and techniques can be designed and then applied to a hospital data warehouse, called the Hospital Casemix Protocol (HCP) Extended data set, in order to improve decision efficiency in the private healthcare sector in Australia. The main objective of this chapter is to present the developed conceptual model to demonstrate inputs, outputs, components, principles and services of predictive analytics for private hospitals.

scholarly journals Polarization and extremism emerge from rational choice

2020 ◽  
Author(s):  
Peter D. Kvam ◽  
Matthew Baldwin
Keyword(s):  
Rational Choice ◽  
Information Search ◽  
Empirical Studies ◽  
Motivated Reasoning ◽  
Decision Makers ◽  
Irrational Thinking ◽  
Preferential Choice ◽  
New Information ◽  
Decision Efficiency ◽  
Biased Information

{Polarization is often thought to be the product of biased information search, motivated reasoning, or other psychological biases. However, polarization and extremism can still occur in the absence of any bias or irrational thinking. In this paper, we show that polarization occurs among groups of decision makers who are implementing rational choice strategies that maximize decision efficiency. This occurs because extreme information enables decision makers to make up their minds and stop considering new information, whereas moderate information is unlikely to trigger a decision. Furthermore, groups of decision makers will generate extremists -- individuals who hold strong views despite being uninformed and impulsive. In re-analyses of seven previous empirical studies on both perceptual and preferential choice, we show that both polarization and extremism manifest across a wide variety of choice paradigms. We conclude by offering theoretically-motivated interventions that could reduce polarization and extremism by altering the incentives people have when gathering information.

scholarly journals Polarization and extremism emerge from rational choice: Estimation as a solution to biased sampling

2021 ◽  
Author(s):  
Peter D. Kvam ◽  
Abhay Alaukik ◽  
Callie E. Mims ◽  
Arina Martemyanova ◽  
Matthew Baldwin
Keyword(s):  
Rational Choice ◽  
Information Search ◽  
Empirical Studies ◽  
Decision Makers ◽  
Degree Of Polarization ◽  
Biased Sampling ◽  
Preferential Choice ◽  
New Information ◽  
The Difference ◽  
Decision Efficiency

Polarization is often described as the product of biased information search, motivated reasoning, or other psychological biases. However, polarization and extremism can still occur in the absence of any bias or irrational thinking. In this paper, we show that polarization occurs among groups of decision makers who are implementing rational choice strategies that maximize decision efficiency. This occurs because extreme information enables decision makers to make up their minds and stop considering new information, whereas moderate information is unlikely to trigger a decision and is thus under-represented in the information decision-makers collect. Furthermore, groups of decision makers will generate extremists -- individuals who hold strong views despite being uninformed and impulsive. In re-analyses of seven empirical studies spanning perceptual and preferential choice and a new study examining politically and affectively charged decisions, we show that both polarization and extremism manifest when decision makers gather information to make a choice. Polarization did not occur, however, when participants made an inference about the difference between two quantities as opposed to deciding which one is superior. Estimation therefore offers a theoretically-motivated intervention that can increase the amount of information people consider and reduce the degree of polarization and extremism among groups of individuals.

Scientific justification of need of use of a psychological platform of auxiliary reproductive technologies

2018 ◽  
pp. 36-39
Author(s):  
O.O. Berestovy ◽  
Keyword(s):  
Clinical Laboratory ◽  
Reproductive Technologies ◽  
Control Group ◽  
Vegetative Nervous System ◽  
Scientific Justification ◽  
High Level ◽  
Psychosomatic Diseases ◽  
Decision Efficiency

The objective: to present scientific justification of need of use of psychological platform when carrying out various programs the auxiliary reproductive technologies. Materials and methods. 227 women with sterility by which auxiliary reproductive technologies were shown were surveyed. To all of them psychopathologic examination by criteria of the international classification of illnesses of the 10th revision is conducted. It was thus taped that 100 women suffered boundary alienations – the main group. The control group was made by 50 fertilny mentally healthy women. To all patients full clinical-laboratory examination according to the scheme accepted in clinics of reproductive was conducted. For clarification of the reason of emergence of boundary alienations the algorithm of inspection included clinical-psychopathologic interviewing and the standard psychological tests. Results. Development of boundary alienations is promoted by combination of several factors, such as not adaptive behavior which promoted intensifying of an internal strain, dysfunction of vegetative nervous system that was shown in vegetative disturbances. Long internally the strain led to development of high level of alarm, emission corticosteriodes and further to an immunoscarce state that promoted an exacerbation of chronic somatopathies. A srategiya «flight in illness» led the koping to development of the psychosomatic diseases which main goal there was treatment avoidance. Conclusion. The above is convincing scientific justification allocation of psychological platform of auxiliary reproductive technologies – as an independent scientific problem on which quality of the decision efficiency of treatment of sterility depends. Key words: auxiliary reproductive technologies, psychological platform, scientific justification.

Leverage Healthcare Data Assets with Predictive Analytics

2016 ◽  
pp. 237-251
Author(s):  
Nilmini Wickramasinghe ◽  
Hoda Moghimi ◽  
Jonathan L. Schaffer
Keyword(s):  
Predictive Analytics ◽  
Healthcare Sector ◽  
Hospital Data ◽  
Healthcare Organizations ◽  
Data Set ◽  
Private Hospitals ◽  
Healthcare Data ◽  
Disparate Data ◽  
Tools And Techniques ◽  
Decision Efficiency

Multi-spectral data residing in disparate data bases represents a critical raw asset for today's healthcare organizations (). However, in order to gain maximum value from such data, it is essential to apply prudent technology solutions and tailored analytic techniques. The following chapter proposes how the application of bespoke predictive analytic tools and techniques can be designed and then applied to a hospital data warehouse, called the Hospital Casemix Protocol (HCP) Extended data set, in order to improve decision efficiency in the private healthcare sector in Australia. The main objective of this chapter is to present the developed conceptual model to demonstrate inputs, outputs, components, principles and services of predictive analytics for private hospitals.

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