scholarly journals Modeling Physicians' Utterances to Explore Diagnostic Decision-making

2017 ◽  
Author(s):  
Xuan Guo ◽  
Rui Li ◽  
Qi Yu ◽  
Anne Haake
Keyword(s):  
Decision Making ◽  
Diagnostic Error ◽  
Psychological Research ◽  
Probabilistic Framework ◽  
Domain Experts ◽  
Domain Specific ◽  
Diagnostic Decision ◽  
Decision Making Processes ◽  
Diagnostic Decision Making ◽  
Medical Concepts

Diagnostic error prevention is a long-established but specialized topic in clinical and psychological research. In this paper, we contribute to the field by exploring diagnostic decision-making via modeling physicians' utterances of medical concepts during image-based diagnoses. We conduct experiments to collect verbal narratives from dermatologists while they are examining and describing dermatology images towards diagnoses. We propose a hierarchical probabilistic framework to learn domain-specific patterns from the medical concepts in these narratives. The discovered patterns match the diagnostic units of thought identified by domain experts. These meaningful patterns uncover physicians' diagnostic decision-making processes while parsing the image content. Our evaluation shows that these patterns provide key information to classify narratives by diagnostic correctness levels.

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

The Cognitive Psychology of Diagnostic Errors

2015 ◽  
Author(s):  
Lauren E. Benishek ◽  
Sallie J. Weaver ◽  
David E. Newman-Toker
Keyword(s):  
Decision Making ◽  
Cognitive Psychology ◽  
Scientific Evidence ◽  
Characteristic Curve ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Diagnostic Reasoning ◽  
Diagnostic Decision ◽  
Context Specific ◽  
Diagnostic Decision Making

Health care involves complex decision making, often under uncertain, ambiguous, and time-sensitive conditions. Clinicians typically face the greatest uncertainty when making diagnostic decisions; common, undifferentiated symptoms paired with increasing prevalence of complex comorbidities, continuously and rapidly evolving scientific evidence, and often fragmented information systems are just a few of the hurdles clinicians must navigate as part of daily diagnostic decision making. In this review, the current state of the science concerning the cognitive psychology of diagnostic errors is discussed, including models of diagnostic reasoning, common errors: heuristics and biases, and practical implications and interventions. Figures show a conceptual model for diagnostic errors; diagnostic and therapeutic cycles; relationships among heuristics, biases, premature closure, and diagnostic errors; Reason’s (2000) Swiss cheese model; and tradeoffs versus improvements in diagnostic performance as illustrated by the receiver operating characteristic curve. Tables list important reasons for understanding the foundational cognitive models of diagnostic reasoning; a glossary of key diagnostic error–related definitions; three models of cognitive decision making; a summary of clinical reasoning models; steps of diagnostic decision making; examples of diagnostic errors resulting from representativeness, availability, and anchoring and adjustment; categories of countermeasures for error reduction interventions; examples of cognitively, systems-, and patient-focused countermeasures for selected biases; a summary of cognitively focused countermeasures to cognitive bias; key problem “classes” where problem- or context-specific solutions might be applied; types of system-focused countermeasures; and patient-focused countermeasures to avoid diagnostic error.   This review contains 5 highly rendered figures, 12 tables, and 120 references.

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

2019 ◽  
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.

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.

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