Decision-making in oncology

多学科方法 团队合作 医学 利益相关者 心理干预 过程(计算) 知识管理 管理科学 过程管理 护理部 计算机科学 工程类 社会科学 公共关系 社会学 政治学 法学 操作系统
作者
Catherine Ménard,Isabelle Merckaert,Darius Razavi,Yves Libert
出处
期刊:Current Opinion in Oncology [Lippincott Williams & Wilkins]
卷期号:24 (4): 381-390 被引量:4
标识
DOI:10.1097/cco.0b013e328354b2f6
摘要

Purpose of review Decision-making in oncology is associated with uncertainty and potential decisional conflict. The purpose of this paper is to review strategies suggested to improve treatment decision-making, discuss their limits and describe recommendations that have been made to improve the decision-making process. Recent findings To improve the decision-making process, uncertainty reduction, shared decision-making and multidisciplinary teamwork have been initially proposed. Due to their limits, alternative approaches such as uncertainty management, collaborative decision-making and collaborative multidisciplinary teamwork have been recommended. Uncertainty management considers uncertainty as a multilevel concept. It may be achieved through collaborative decision-making and collaborative multidisciplinary teamwork. Collaborative decision-making is an in-depth personalized iterative assessment of patient medical, psychological and social status. It promotes the patient's proactive role as a key stakeholder of decision-making and the physician's proactive role as a key support to patient decision-making. Collaborative multidisciplinary teamwork promotes an optimal environment for collaborative decision-making in which patients are key stakeholders and all relevant healthcare professionals are actively involved. These approaches require developing interventions for patients, and trainings for physicians and multidisciplinary teams. Summary On the basis of these recent approaches, we propose a 'three-step model of multidisciplinary collaborative treatment decision-making' in oncology. This model should be tested for its validity.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
爆米花应助代婉婉采纳,获得10
1秒前
脑洞疼应助风中的海雪采纳,获得10
1秒前
piaoyingzhiyu完成签到,获得积分10
2秒前
huangqqk发布了新的文献求助10
3秒前
丂枧发布了新的文献求助10
3秒前
3秒前
Alicer发布了新的文献求助20
4秒前
janejane发布了新的文献求助10
4秒前
4秒前
我是老大应助陶醉平松采纳,获得10
4秒前
王珊完成签到,获得积分10
5秒前
oxo关注了科研通微信公众号
5秒前
超级李包包完成签到,获得积分10
5秒前
酷酷发布了新的文献求助10
5秒前
5秒前
桐桐应助zhaoxiao采纳,获得10
6秒前
Akim应助xiaowang采纳,获得10
6秒前
不住完成签到,获得积分20
6秒前
zsc668完成签到 ,获得积分10
6秒前
6秒前
liverbool发布了新的文献求助10
7秒前
神说应助ffff采纳,获得10
7秒前
kx发布了新的文献求助10
7秒前
7秒前
7秒前
8秒前
8秒前
情怀应助一只耳采纳,获得10
8秒前
8秒前
无花果应助科研进化中采纳,获得10
9秒前
cnulee完成签到,获得积分10
9秒前
科研通AI5应助化羽归尘采纳,获得10
9秒前
沉默的小天鹅完成签到,获得积分10
9秒前
zhouting发布了新的文献求助10
9秒前
10秒前
研友_VZG7GZ应助暖阳采纳,获得10
10秒前
wanci应助德德采纳,获得10
11秒前
Wacky发布了新的文献求助10
11秒前
神勇猕猴桃完成签到,获得积分10
12秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Machine Learning Methods in Geoscience 1000
Resilience of a Nation: A History of the Military in Rwanda 888
Musculoskeletal Pain - Market Insight, Epidemiology And Market Forecast - 2034 666
Crystal Nonlinear Optics: with SNLO examples (Second Edition) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3735180
求助须知:如何正确求助?哪些是违规求助? 3279071
关于积分的说明 10012998
捐赠科研通 2995649
什么是DOI,文献DOI怎么找? 1643610
邀请新用户注册赠送积分活动 781338
科研通“疑难数据库(出版商)”最低求助积分说明 749369