scholarly journals Common things are common, but what is common? A foundation for probabilistic diagnosis.

2021 ◽  
Author(s):  
Scott Aberegg ◽  
Sean Callahan
Keyword(s):  
Decision Making ◽  
Medical Education ◽  
Differential Diagnosis ◽  
Diagnostic Accuracy ◽  
Common Disease ◽  
Diagnostic Decision ◽  
Definition Of ◽  
Diagnostic Decision Making ◽  
Probabilistic Diagnosis

The well-known clinical axiom stating that “common things are common” attests to the pivotal role of probability in diagnosis. Despite the popularity of this and related axioms, there is no operationalized definition of a common disease, and no practicable way of incorporating actual disease frequencies into differential diagnosis. In this expository essay, we aim to reduce the ambiguity surrounding the definition of a common (or rare) disease and show that incidence – not prevalence – is the proper metric of disease frequency for diagnosis. We explore how a numerical estimates of disease frequencies based on incidence can be incorporated into differential diagnosis as well as the inherent limitations of this method. These concepts have important implications for diagnostic decision making and medical education, and hold promise as a method to improve diagnostic accuracy.

scholarly journals Cloud decision support service in cardiology based on formalized knowledge

2020 ◽  
Vol 35 (4) ◽  
pp. 32-38
Author(s):  
V. V. Gribova ◽  
M. V. Petryaeva ◽  
E. A. Shalfeeva
Keyword(s):  
Decision Making ◽  
Differential Diagnosis ◽  
Decision Support ◽  
Support Service ◽  
Cardiac Diseases ◽  
Software Components ◽  
Cloud Platform ◽  
Diagnostic Decision ◽  
Smart Service ◽  
Diagnostic Decision Making

The paper presents the service for support of diagnostic decision-making in cardiology. The general principles for the development and conceptual architecture of smart service and its information and software components are described. The features of diagnosis and differential diagnosis of cardiac diseases on the medical portal of the IACPaaS cloud platform are presented.

Decision-Making in Psychological Assessment

2021 ◽  
Author(s):  
David L. Streiner
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
False Negative ◽  
Final Diagnosis ◽  
Number Of Factors ◽  
New Information ◽  
Diagnostic Decision ◽  
Wrong Diagnosis ◽  
Diagnostic Decision Making ◽  
Biases And Heuristics

This chapter describes a number of factors that may influence a clinician’s judgment and conclusions while conducting an assessment, and it discusses others that make interpretation of the results less than straightforward. It begins by discussing the effects of the prevalence, or base rate, of the disorder on the diagnostic accuracy of the findings. Even in the presence of seemingly unequivocal results pointing to a given diagnosis, the findings may lead to a false-positive conclusion if the prevalence is low and to a false-negative one if the prevalence is high. The chapter shows how using Bayes’ theorem can tell us the likelihood of a wrong diagnosis. It next discusses incremental validity—whether adding another test to the battery increases diagnostic accuracy. If the new test is correlated with ones already administered, then the amount of new information it provides is limited and may increase unwarranted confidence in the final diagnosis. Third, the chapter discusses various biases and heuristics that may affect diagnostic decision-making, such as anchoring, diagnostic momentum, premature closure, and the influence of patient and assessor characteristics. It concludes by presenting a number of steps that should be taken to minimize the effects of these biases.

scholarly journals Decision support tools to improve cancer diagnostic decision making in primary care: a systematic review

2019 ◽  
Vol 69 (689) ◽  
pp. e809-e818 ◽  
Author(s):  
Sophie Chima ◽  
Jeanette C Reece ◽  
Kristi Milley ◽  
Shakira Milton ◽  
Jennifer G McIntosh ◽  
...  
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Decision Making ◽  
Decision Support ◽  
Cost Effectiveness ◽  
Cancer Diagnosis ◽  
Decision Support Tools ◽  
Diagnostic Decision ◽  
Support Tools ◽  
Diagnostic Decision Making

BackgroundThe diagnosis of cancer in primary care is complex and challenging. Electronic clinical decision support tools (eCDSTs) have been proposed as an approach to improve GP decision making, but no systematic review has examined their role in cancer diagnosis.AimTo investigate whether eCDSTs improve diagnostic decision making for cancer in primary care and to determine which elements influence successful implementation.Design and settingA systematic review of relevant studies conducted worldwide and published in English between 1 January 1998 and 31 December 2018.MethodPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched, and a consultation of reference lists and citation tracking was carried out. Exclusion criteria included the absence of eCDSTs used in asymptomatic populations, and studies that did not involve support delivered to the GP. The most relevant Joanna Briggs Institute Critical Appraisal Checklists were applied according to study design of the included paper.ResultsOf the nine studies included, three showed improvements in decision making for cancer diagnosis, three demonstrated positive effects on secondary clinical or health service outcomes such as prescribing, quality of referrals, or cost-effectiveness, and one study found a reduction in time to cancer diagnosis. Barriers to implementation included trust, the compatibility of eCDST recommendations with the GP’s role as a gatekeeper, and impact on workflow.ConclusioneCDSTs have the capacity to improve decision making for a cancer diagnosis, but the optimal mode of delivery remains unclear. Although such tools could assist GPs in the future, further well-designed trials of all eCDSTs are needed to determine their cost-effectiveness and the most appropriate implementation methods.

scholarly journals The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework

2021 ◽  
Vol 13 (4) ◽  
pp. 2332
Author(s):  
Lena Bjørlo ◽  
Øystein Moen ◽  
Mark Pasquine
Keyword(s):  
Decision Making ◽  
Decision Aids ◽  
Privacy Regulation ◽  
Advantages And Disadvantages ◽  
Fundamental Significance ◽  
Research Gap ◽  
Online Decision Making ◽  
Definition Of ◽  
Consumer Autonomy

Artificial intelligence (AI)-based decision aids are increasingly employed by businesses to assist consumers’ decision-making. Personalized content based on consumers’ data brings benefits for both consumers and businesses, i.e., with regards to more relevant content. However, this practice simultaneously enables increased possibilities for exerting hidden interference and manipulation on consumers, reducing consumer autonomy. We argue that due to this, consumer autonomy represents a resource at the risk of depletion and requiring protection, due to its fundamental significance for a democratic society. By balancing advantages and disadvantages of increased influence by AI, this paper addresses an important research gap and explores the essential challenges related to the use of AI for consumers’ decision-making and autonomy, grounded in extant literature. We offer a constructive, rather than optimistic or pessimistic, outlook on AI. Hereunder, we present propositions suggesting how these problems may be alleviated, and how consumer autonomy may be protected. These propositions constitute the fundament for a framework regarding the development of sustainable AI, in the context of online decision-making. We argue that notions of transparency, complementarity, and privacy regulation are vital for increasing consumer autonomy and promoting sustainable AI. Lastly, the paper offers a definition of sustainable AI within the contextual boundaries of online decision-making. Altogether, we position this paper as a contribution to the discussion of development towards a more socially sustainable and ethical use of AI.

Diagnostic Decision-Making Processes

2019 ◽  
pp. 79-85
Author(s):  
Julia Hodgson ◽  
Kevin Moore ◽  
Trisha Acri ◽  
Glenn Jordan Treisman
Keyword(s):  
Decision Making ◽  
Diagnostic Decision ◽  
Decision Making Processes ◽  
Diagnostic Decision Making

scholarly journals Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

10.2196/16047 ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. e16047 ◽  
Author(s):  
Don Roosan ◽  
Anandi V Law ◽  
Mazharul Karim ◽  
Moom Roosan
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Correct Diagnosis ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Positive Direction ◽  
Team Members ◽  
Diagnostic Decision ◽  
Diagnostic Decision Making ◽  
Collaborative Decision

Background According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis. Objective The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management. Methods To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3). Results Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021. Conclusions The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics. International Registered Report Identifier (IRRID) DERR1-10.2196/16047

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