2.50
Hdl Handle:
http://hdl.handle.net/10755/157716
Type:
Presentation
Title:
Intensive Care Unit Domain Analysis Guides Monitoring Display Design
Abstract:
Intensive Care Unit Domain Analysis Guides Monitoring Display Design
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Doig, Alexa K., RN, PhD
P.I. Institution Name:University of Utah, College of Nursing
Title:Assistant Professor
Contact Address:10 S 2000 E, Salt Lake City, UT, 84112, USA
Contact Telephone:801-581-4381
Co-Authors:James Agutter, MS, Chief Executive Officer; Robert Albert, MS, Senior Researcher; Michael Behrens, MD, Assistant Professor; Noah Syroid, MS, Researcher
Aims: Conduct a domain analysis of the intensive care unit (ICU) for the purposes of identifying 1) types of clinical data utilized by different personnel, 2) sources of data preferred, and 3) dynamics of interdisciplinary communications concerning clinical data. Background: Understanding the users' information requirements, control-information feedback requirements, actions taken, mental model of the process, and surrounding environmental and situational constraints is essential for designing a visual information display to support the decision making process. Thus, prior to designing and implementing new monitoring displays, a detailed analysis of the user's domain is prerequisite. Methods: Two investigator observed patient care in the ICU and compiled observations into a structured report. Next, structured interviews with 8 clinicians were conducted. In these semi-structured interviews we examined controllable clinical processes, variables needed to diagnose significant clinical events, information needed to evaluate the effectiveness of treatment, the heuristic rules that guide clinicians' diagnosis and treatment of these events and the cognitive maps used to understand physiologic function, pharmacologic action, and pathophysiology of the events. The findings from the interviews were used to develop a survey. The survey, completed by 28 ICU clinicians, focused on characterizing the clinical importance of different variables, the variable's normal range and upper and lower safe limits, conditions when the variable cannot be measured, and conditions when measurements might be biased and noisy. Results: The domain analysis produced a rank order of frequently occurring life-threatening events in each domain and the key variables associated with each event. The results of the domain analysis will specified which variables to highlight in the display; causal relationships between variables; and the display structure including data trending that matches clinicians' mental models. It also identified mental calculations clinicians must make, and the models needed to transform data into useable information to understand physiologic function, pharmacologic action, and pathophysiology, and interrelationships between these variables over time (i.e. trends). The results identified emergent display features that could assist clinicians to detect the most frequent life-threatening events. Implications: Incorporating specific findings from this study into the design of a new monitoring display should ensure that the display fits the mental model of the user.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleIntensive Care Unit Domain Analysis Guides Monitoring Display Designen_GB
dc.identifier.urihttp://hdl.handle.net/10755/157716-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Intensive Care Unit Domain Analysis Guides Monitoring Display Design</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Doig, Alexa K., RN, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Utah, College of Nursing</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">10 S 2000 E, Salt Lake City, UT, 84112, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">801-581-4381</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">alexa.doig@nurs.utah.edu</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">James Agutter, MS, Chief Executive Officer; Robert Albert, MS, Senior Researcher; Michael Behrens, MD, Assistant Professor; Noah Syroid, MS, Researcher</td></tr><tr><td colspan="2" class="item-abstract">Aims: Conduct a domain analysis of the intensive care unit (ICU) for the purposes of identifying 1) types of clinical data utilized by different personnel, 2) sources of data preferred, and 3) dynamics of interdisciplinary communications concerning clinical data. Background: Understanding the users' information requirements, control-information feedback requirements, actions taken, mental model of the process, and surrounding environmental and situational constraints is essential for designing a visual information display to support the decision making process. Thus, prior to designing and implementing new monitoring displays, a detailed analysis of the user's domain is prerequisite. Methods: Two investigator observed patient care in the ICU and compiled observations into a structured report. Next, structured interviews with 8 clinicians were conducted. In these semi-structured interviews we examined controllable clinical processes, variables needed to diagnose significant clinical events, information needed to evaluate the effectiveness of treatment, the heuristic rules that guide clinicians' diagnosis and treatment of these events and the cognitive maps used to understand physiologic function, pharmacologic action, and pathophysiology of the events. The findings from the interviews were used to develop a survey. The survey, completed by 28 ICU clinicians, focused on characterizing the clinical importance of different variables, the variable's normal range and upper and lower safe limits, conditions when the variable cannot be measured, and conditions when measurements might be biased and noisy. Results: The domain analysis produced a rank order of frequently occurring life-threatening events in each domain and the key variables associated with each event. The results of the domain analysis will specified which variables to highlight in the display; causal relationships between variables; and the display structure including data trending that matches clinicians' mental models. It also identified mental calculations clinicians must make, and the models needed to transform data into useable information to understand physiologic function, pharmacologic action, and pathophysiology, and interrelationships between these variables over time (i.e. trends). The results identified emergent display features that could assist clinicians to detect the most frequent life-threatening events. Implications: Incorporating specific findings from this study into the design of a new monitoring display should ensure that the display fits the mental model of the user.</td></tr></table>en_GB
dc.date.available2011-10-26T20:08:11Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:08:11Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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