Information transformation: Some missing links

2007 ◽  
Vol 26 (3) ◽  
pp. 157-172
Author(s):  
Ivan P. Vaghely ◽  
Pierre-André Julien ◽  
André Cyr
Keyword(s):  
Decision Making ◽  
Grounded Theory ◽  
Information Processing ◽  
Knowledge Creation ◽  
Best Practice ◽  
Participant Observation ◽  
Human Information Processing ◽  
Process Information ◽  
Information Transformation ◽  
Efficient Information

Using grounded theory along with participant observation and interviews the authors explore how individuals in organizations process information. They build a model of human information processing which links the cognitivist-constructionist perspective to an algorithmic-heuristic continuum. They test this model using non-parametric procedures and find interesting results showing links to efficient information processing outcomes such as contributions to decision-making, knowledge-creation and innovation. They also identify some elements of best practice by efficient human information processing individuals whom they call the “information catalysts”.

The critical step to reduce diagnostic errors in medicine: addressing the limitations of human information processing

Diagnosis ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 139-141 ◽  
Author(s):  
Laura Zwaan
Keyword(s):  
Information Processing ◽  
Diagnostic Testing ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Human Information Processing ◽  
Process Information ◽  
Critical Step ◽  
Common Diseases ◽  
Over Time ◽  
The Way

AbstractOver the last 50 years diagnostic testing has improved dramatically and we are now able to diagnose patients faster and more precisely than ever before. However, the incidence of diagnostic errors, particularly of common diseases, has remained relatively stable over time. In this paper, I argue that the intrinsic limitations of human information processing are crucial. The way people process information has not changed over the years and is the main cause of diagnostic error. To take a decisive step forward and substantially reduce the number of diagnostic errors in medicine, we need to create an environment which takes the intrinsic limitations of in human information processing into account.

scholarly journals How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices

2021 ◽  
Vol 28 (1) ◽  
pp. e100301
Author(s):  
David Lyell ◽  
Enrico Coiera ◽  
Jessica Chen ◽  
Parina Shah ◽  
Farah Magrabi
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Information Processing ◽  
Ejection Fraction ◽  
Medical Devices ◽  
Human Information Processing ◽  
Information Analysis ◽  
Input And Output ◽  
Descriptive Information ◽  
The Us

ObjectiveTo examine how and to what extent medical devices using machine learning (ML) support clinician decision making.MethodsWe searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used ML. Descriptive information about the clinical task, device task, device input and output, and ML method were extracted. The stage of human information processing automated by ML-based devices and level of autonomy were assessed.ResultsOf 137 candidates, 59 FDA approvals for 49 unique devices were included. Most approvals (n=51) were since 2018. Devices commonly assisted with diagnostic (n=35) and triage (n=10) tasks. Twenty-three devices were assistive, providing decision support but left clinicians to make important decisions including diagnosis. Twelve automated the provision of information (autonomous information), such as quantification of heart ejection fraction, while 14 automatically provided task decisions like triaging the reading of scans according to suspected findings of stroke (autonomous decisions). Stages of human information processing most automated by devices were information analysis, (n=14) providing information as an input into clinician decision making, and decision selection (n=29), where devices provide a decision.ConclusionLeveraging the benefits of ML algorithms to support clinicians while mitigating risks, requires a solid relationship between clinician and ML-based devices. Such relationships must be carefully designed, considering how algorithms are embedded in devices, the tasks supported, information provided and clinicians’ interactions with them.

Business Intelligence and Analytics Research

2016 ◽  
Vol 7 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Gregory S. Richards
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Business Intelligence ◽  
Black Box ◽  
Human Information Processing ◽  
Decision Making Process ◽  
Sociotechnical System ◽  
Informed Decisions ◽  
Core Questions

The tremendous growth of data of all forms has led to an increase in research on the uses and outcomes of Business Intelligence and Analytics (BI&A). Much of the current research however, focuses on the technological aspects. The process of decision making with data is treated more or less like the proverbial black box. If one is to better understand how BI&A can help managers make informed decisions, then more effort is needed to explore the decision making process. This paper argues that decision-making in organizations is enacted by a sociotechnical system in which human information processing forms the key constraint. By considering the stages of cognition and the use of rules-based versus heuristic-based decision making, the paper identifies a number of core questions related to the contribution of a BI&A system to the decision making process in organizations.

An E-Documenting Approach to Conceptual Design

2000 ◽  
Author(s):  
Oren Benami ◽  
Yan Jin
Keyword(s):  
Information Processing ◽  
Conceptual Design ◽  
Transformation Process ◽  
Human Information Processing ◽  
Design Phase ◽  
Processing Capability ◽  
Overall Design ◽  
Conceptual Design Phase ◽  
Information Transformation ◽  
Design Ideas

Abstract Conceptual design is the most creative, most informal, most ambiguous, and least understood phase of design. During conceptual design, ideas are in a state of transformation and relationships between concepts are often unclear or not well developed. This paper presents our view of conceptual design as an information transformation process and proposes an approach to support conceptual design. The basic idea is the following. Limitations in human information processing capability frequently prevents designers from being effective in retaining, managing, and applying the large amount of information generated during the conceptual design phase. Therefore, if one can provide a system that receives the flow of ideas generated, and arranges the ideas in a way that can be effectively retained, managed, and applied, then fewer ideas will be lost, connections between ideas can be easily identified, and consequently the overall design will be more effective and efficient.

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