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

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.

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 Ebola US Patient Zero: lessons on misdiagnosis and effective use of electronic health records

Diagnosis ◽  
2014 ◽  
Vol 1 (4) ◽  
pp. 283-287 ◽  
Author(s):  
Divvy K. Upadhyay ◽  
Dean F. Sittig ◽  
Hardeep Singh
Keyword(s):  
Public Health ◽  
Health Care ◽  
Decision Making ◽  
Disease Control ◽  
Diagnostic Errors ◽  
Health Records ◽  
The Public ◽  
Diagnostic Decision ◽  
Effective Use ◽  
Diagnostic Decision Making

AbstractOn September 30th, 2014, the Centers for Disease Control and Prevention (CDC) confirmed the first travel-associated case of US Ebola in Dallas, TX. This case exposed two of the greatest concerns in patient safety in the US outpatient health care system: misdiagnosis and ineffective use of electronic health records (EHRs). The case received widespread media attention highlighting failures in disaster management, infectious disease control, national security, and emergency department (ED) care. In addition, an error in making a correct and timely Ebola diagnosis on initial ED presentation brought diagnostic decision-making vulnerabilities in the EHR era into the public eye. In this paper, we use this defining “teachable moment” to highlight the public health challenge of diagnostic errors and discuss the effective use of EHRs in the diagnostic process. We analyze the case to discuss several missed opportunities and outline key challenges and opportunities facing diagnostic decision-making in EHR-enabled health care. It is important to recognize the reality that EHRs suffer from major usability and inter-operability issues, but also to acknowledge that they are only tools and not a replacement for basic history-taking, examination skills, and critical thinking. While physicians and health care organizations ultimately need to own the responsibility for addressing diagnostic errors, several national-level initiatives can help, including working with software developers to improve EHR usability. Multifaceted approaches that account for both technical and non-technical factors will be needed. Ebola US Patient Zero reminds us that in certain cases, a single misdiagnosis can have widespread and costly implications for public health.

scholarly journals Machine Learning to Monitor Diagnostic Safety Risks in Emergency Departments: A Study Protocol (Preprint)

2020 ◽  
Author(s):  
Moein Enayati ◽  
Mustafa Sir ◽  
Xingyu Zhang ◽  
Sarah Parker ◽  
Elizabeth Duffy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Making ◽  
Emergency Departments ◽  
Diagnostic Errors ◽  
Care Team ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diagnostic Decision ◽  
Diagnostic Decision Making

BACKGROUND Diagnostic decision-making, especially in emergency departments (EDs), is a highly complex cognitive process involving uncertainty and susceptibility to error. A combination of parameters including patient factors (e.g. history, behaviors, complexity, and comorbidity), provider/care-team factors (e.g. cognitive load, information gathering, and synthesis), and system factors (e.g. health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Records with potential diagnostic errors have been identified using electronic triggers that flag certain patterns of care (i.e., triggers), such as the escalation of care or death after ED discharge. Sophisticated data analytics and machine learning techniques that can be applied to existing electronic health record (EHR) datasets could shed light on potential risk factors influencing diagnostic decision-making. OBJECTIVE To identify variables contributing to potential diagnostic errors in the ED using large scale EHR data. METHODS We will apply trigger algorithms to EHR data repositories to generate a large dataset of trigger-positive and trigger-negative encounters. Samples from both sets will be validated using medical record reviews where we expect to find a higher number of diagnostic safety problems in the trigger positive subset. Advanced data mining and machine learning techniques will be used to evaluate relationships between certain patient, provider/care-team, and system risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. RESULTS This study received funding in February 2019, and is approved by the Institutional Review Board at two health systems. Trigger queries are being developed at both organizations and sample cohorts are being labeled using the triggers. Once completed, study data can inform important parameters for future clinical decision support systems to help identify risks that contribute to diagnostic errors. CONCLUSIONS Using large datasets to investigate risk factors (patient, provider/care team, and system-level) in the diagnostic process can provide mechanisms for future monitoring of diagnostic safety.

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.

scholarly journals Factors influencing diagnostic decision-making

2012 ◽  
Vol 4 (3) ◽  
pp. 223
Author(s):  
Kathleen Callaghan
Keyword(s):  
Decision Making ◽  
Support Groups ◽  
Rasch Analysis ◽  
Diagnostic Errors ◽  
Clinical Information ◽  
Factors Influencing ◽  
Delphi Round ◽  
Diagnostic Decision ◽  
Prescriptive Model ◽  
Diagnostic Decision Making

INTRODUCTION: Identifying influences on diagnostic decisions is important because diagnostic errors often have far-reaching consequences for an individual’s future within the workforce and their eligibility for Accident Compensation Corporation–funded treatment. Most investigations of factors biasing decision making have used quantitative techniques rather than qualitative methods. AIM: To identify factors influencing GPs’ diagnostic decision-making and to develop a valid questionnaire to determine the desirability and importance of each factor’s influence. METHODS: Focus groups and the Delphi method were combined with Rasch analysis to identify factors influencing GPs’ diagnostic decision-making and then examine the strength and stability of ratings of the factors’ desirability and importance. RESULTS: Thirty-nine factors were identified. Factors demonstrating high stability but no consensus included the importance of evidence-based medicine, the potential ramifications of a diagnosis, and the desirability of medicolegal issues. Factors for which there was disagreement in the first Delphi round but consensus in the second round included the importance of patient advocacy/support groups and the desirability of examination findings. Rasch analysis indicated that the questionnaire was close to the model (88.6% and 86.2% of variance in the ratings of importance and desirability explained). DISCUSSION: Participants readily identified factors influencing GPs’ diagnostic decision-making. Their ratings did not appear to support a prescriptive model of medicine, yet two cornerstones of prescriptive medicine, clinical information and probability of disease, were rated as highly desirable and important. KEYWORDS: Decision-making; diagnosis; bias; Rasch analysis; general practitioners

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.

TMD Diagnostic Decision-Making and Probability Theory. Part II

CRANIO® ◽  
1996 ◽  
Vol 14 (4) ◽  
pp. 312-319
Author(s):  
L.V. Christensen ◽  
D.C. McKay
Keyword(s):  
Decision Making ◽  
Probability Theory ◽  
Diagnostic Decision ◽  
Diagnostic Decision Making

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
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