scholarly journals Evaluation of Eye-tracking Methods for a Decision Support Application

2020 ◽  
Author(s):  
Shyam Visweswaran ◽  
Andrew J King ◽  
Mohammadamin Tajgardoon ◽  
Luca Calzoni ◽  
Gilles Clermont ◽  
...  
Keyword(s):  
Decision Support ◽  
Eye Tracking ◽  
Clinical Decision Support ◽  
Information Seeking ◽  
Low Cost ◽  
Clinical Decision ◽  
Training Data ◽  
Seeking Behavior ◽  
Electronic Medical Record Systems ◽  
Tracking Device

AbstractEye-tracking is used widely to investigate visual and cognitive processes in the context of electronic medical record systems. We investigated a novel application of eye tracking to collect training data for machine learning-based clinical decision support. Specifically, we recorded the information-seeking behavior of physicians while they used electronic medical records in the context of a specific clinical task. Using data captured by a low-cost eye tracking device, we evaluated the performance of several methods for processing gaze points that were recorded using the device. Our results support the use of a low-cost eye tracking device and relatively simple methods for processing gaze points to record the information-seeking behavior of physicians. The eye-tracking methods and scripts that we developed offer a first step in developing novel uses for eye-tracking for clinical decision support.Lay summaryIn the context of electronic medical record systems, eye-tracking is used extensively to explore visual and cognitive processes. We investigated a novel application of eye tracking to collect training data for machine learning-based clinical decision support. We developed and evaluated several methods for processing gaze points that were recorded using a low-cost eye tracking device. The eye-tracking methods and scripts that we developed offer a first step in developing novel uses for eye-tracking for clinical decision support.

scholarly journals Evaluation of eye tracking for a decision support application

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Shyam Visweswaran ◽  
Andrew J King ◽  
Mohammadamin Tajgardoon ◽  
Luca Calzoni ◽  
Gilles Clermont ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Eye Tracking ◽  
Clinical Decision Support ◽  
Low Cost ◽  
Clinical Decision ◽  
Patient Data ◽  
Training Data ◽  
Manual Annotation ◽  
Support Tool

Abstract Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.

scholarly journals Considerations for Designing EHR-Embedded Clinical Decision Support Systems for Antimicrobial Stewardship in Pediatric Emergency Departments

2020 ◽  
Vol 11 (04) ◽  
pp. 589-597
Author(s):  
Mustafa Ozkaynak ◽  
Noel Metcalf ◽  
Daniel M. Cohen ◽  
Larissa S. May ◽  
Peter S. Dayan ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Information Seeking ◽  
Emergency Departments ◽  
Imperfect Information ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
The United States ◽  
Pediatric Emergency ◽  
Antibiotic Prescribing

Abstract Objective This study was aimed to explore the intersection between organizational environment, workflow, and technology in pediatric emergency departments (EDs) and how these factors impact antibiotic prescribing decisions. Methods Semistructured interviews with 17 providers (1 fellow and 16 attending faculty), and observations of 21 providers (1 physician assistant, 5 residents, 3 fellows, and 12 attendings) were conducted at three EDs in the United States. We analyzed interview transcripts and observation notes using thematic analysis. Results Seven themes relating to antibiotic prescribing decisions emerged as follows: (1) professional judgement, (2) cognition as a critical individual resource, (3) decision support as a critical organizational resource, (4) patient management with imperfect information, (5) information-seeking as a primary task, (6) time management, and (7) broad process boundaries of antibiotic prescribing. Discussion The emerging interrelated themes identified in this study can be used as a blueprint to design, implement, and evaluate clinical decision support (CDS) systems that support antibiotic prescribing in EDs. The process boundaries of antibiotic prescribing are broader than the current boundaries covered by existing CDS systems. Incongruities between process boundaries and CDS can under-support clinicians and lead to suboptimal decisions. We identified two incongruities: (1) the lack of acknowledgment that the process boundaries go beyond the physical boundaries of the ED and (2) the lack of integration of information sources (e.g., accessibility to prior cultures on an individual patient outside of the organization). Conclusion Significant opportunities exist to improve appropriateness of antibiotic prescribing by considering process boundaries in the design, implementation, and evaluation of CDS systems.

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

Sign in / Sign up

Export Citation Format

Share Document