scholarly journals How Clinicians Perceive Artificial Intelligence-Assisted Technologies in Diagnostic Decision-making: A Mixed Methods Approach (Preprint)

10.2196/33540 ◽  
2021 ◽  
Author(s):  
Hyeyoung Hah ◽  
Deana Goldin
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Mixed Methods ◽  
Mixed Methods Approach ◽  
Diagnostic Decision ◽  
Diagnostic Decision Making ◽  
Assisted Technologies

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.

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 Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace

2017 ◽  
Vol 24 (2) ◽  
pp. 239-257 ◽  
Author(s):  
David Brougham ◽  
Jarrod Haar
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods ◽  
Career Satisfaction ◽  
Turnover Intentions ◽  
Well Being ◽  
Career Progression ◽  
Organisational Commitment ◽  
Smart Technology ◽  
Mixed Methods Approach ◽  
Technology Knowledge

AbstractFuturists predict that a third of jobs that exist today could be taken by Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) by 2025. However, very little is known about how employees perceive these technological advancements in regards to their own jobs and careers, and how they are preparing for these potential changes. A new measure (STARA awareness) was created for this study that captures the extent to which employees feel their job could be replaced by these types of technology. Due to career progression and technology knowledge associated with age, we also tested age as a moderator of STARA. Using a mixed-methods approach on 120 employees, we tested STARA awareness on a range of job and well-being outcomes. Greater STARA awareness was negatively related to organisational commitment and career satisfaction, and positively related to turnover intentions, cynicism, and depression.

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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