Opportunistic decision making and complexity in emergency care

被引：47

作者：

Franklin, Amy ^{[1
]}

Liu, Ying ^{[1
]}

Li, Zhe ^{[1
]}

Nguyen, Vickie ^{[1
]}

Johnson, Todd R. ^{[1
]}

Robinson, David ^{[1
,2
]}

Okafor, Nnaemeka ^{[1
,2
]}

King, Brent ^{[1
,2
]}

Patel, Vimla L. ^{[1
]}

Zhang, Jiajie ^{[1
]}

机构：

[1] Univ Texas Hlth Sci Ctr Houston, Ctr Cognit Informat & Decis Making, Sch Hlth Informat Sci, Houston, TX 77030 USA

[2] Univ Texas Hlth Sci Ctr Houston, Dept Emergency Med, Sch Med, Houston, TX 77030 USA

来源：

JOURNAL OF BIOMEDICAL INFORMATICS | 2011年 / 44卷 / 03期

关键词：

Opportunistic decision making; Opportunistic planning; Complexity; Emergency department; Methodology; Taxonomy of decision types; Across-task decisions; TASK COMPLEXITY; COGNITION;

D O I：

10.1016/j.jbi.2011.04.001

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In critical care environments such as the emergency department (ED), many activities and decisions are not planned. In this study, we developed a new methodology for systematically studying what are these unplanned activities and decisions. This methodology expands the traditional naturalistic decision making (NDM) frameworks by explicitly identifying the role of environmental factors in decision making. We focused on decisions made by ED physicians as they transitioned between tasks. Through ethnographic data collection, we developed a taxonomy of decision types. The empirical data provide important insight to the complexity of the ED environment by highlighting adaptive behavior in this intricate milieu. Our results show that half of decisions in the ED we studied are not planned, rather decisions are opportunistic decision (34%) or influenced by interruptions or distractions (21%). What impacts these unplanned decisions have on the quality, safety, and efficiency in the ED environment are important research topics for future investigation. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：469 / 476

页数：8

共 45 条

[1] Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty [J].