Using Cognitive Task Analysis to Identify Critical Decisions in the Laparoscopic Environment

Craig, Curtis M.; Klein, Martina I.; Griswold, John; Gaitonde, Krishnanath; McGill, Thomas E.; Halldorsson, Ari

doi:10.1177/0018720812448393

Cited by 34 publications

(21 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clinical experts have years of experiences that could have contributed towards selecting more appropriate patient information for reasoning in shorter time. This finding is congruent with similar findings about experts' ability to process information in search, perception and reasoning components of the task faster when compared with non-experts [79][80][81][82][83].…”

Section: Implications Of the Quantitative Analysissupporting

confidence: 89%

Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain

Roosan¹,

Fiol²,

Butler³

et al. 2016

Appl Clin Inform

View full text Add to dashboard Cite

show abstract

Section: Implications Of the Quantitative Analysissupporting

confidence: 89%

Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain

Roosan¹,

Fiol²,

Butler³

et al. 2016

Appl Clin Inform

View full text Add to dashboard Cite

show abstract

“…Previous studies were effective in not only analyzing cognitive challenges but also eliciting the organizational challenges and environmental ambiguities of complex, time-pressured, uncertain and high-risk situations using the technique [ 31 – 34 ]. In this study, we have used “Combinatorics” of cognitive task analysis, which involved utilizing the critical decision method with critical incident interviews [ 35 – 37 ]. We have used the RATS (Relevance of study question, Appropriateness of qualitative method, Transparency of procedure and Soundness of interpretive approach) protocol for qualitative data analysis for the transcriptions of the interviews [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Islam

Weir

Jones

et al. 2015

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

BackgroundClinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment.MethodsWe used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants.ResultsThe following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners’ perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases.ConclusionThe cognitive strategies to deal with decision complexity found in this study have important implications for design future decision support systems for the management of complex patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0221-z) contains supplementary material, which is available to authorized users.

show abstract

“…9 Trajectory tracking is about getting ahead of the curve and projecting possible scenarios to be able to handle situations. 10 In medicine, this process requires a functional perspective of how the disease will progress and what is expected based on what is observed.…”

Section: Resultsmentioning

confidence: 99%

Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition

Roosan

Samore

Jones

et al. 2016

2016 IEEE International Conference on Healthcare Informatics (ICHI)

View full text Add to dashboard Cite

Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians’ cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems.

show abstract

Using Cognitive Task Analysis to Identify Critical Decisions in the Laparoscopic Environment

Abstract: The information elicited in this study is applicable to laparoscopic training.

Cited by 34 publications

References 38 publications

Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain

Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain

Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition

Contact Info

Product

Resources

About