Bonner G
Thames Valley University, Slough, Berkshire, UK.
J Adv Nurs. 2001 Aug;35(3):349-56. doi: 10.1046/j.1365-2648.2001.01851.x.
To examine the application of the decision tree approach to collaborative clinical decision-making in mental health care in the United Kingdom (UK).
While this approach to decision-making has been examined in the acute care setting, there is little published evidence of its use in clinical decision-making within the mental health setting. The complexities of dual diagnosis (schizophrenia and substance misuse in this case example) and the varied viewpoints of different professionals often hamper the decision-making process. This paper highlights how the approach was used successfully as a multiprofessional collaborative approach to decision-making in the context of British community mental health care.
A selective review of the relevant literature and a case study application of the decision tree framework.
The process of applying the decision tree framework to clinical decision-making in mental health practice can be time consuming and client inclusion within the process is not always appropriate. The approach offers a method of assigning numerical values to support complex multiprofessional decision-making as well as considering underpinning literature to inform the final decision. Use of the decision tree offers a common framework that can assist professionals to examine the options available to them in depth, while considering the complex variables that influence decision-making in collaborative mental health practice. Use of the decision tree warrants further consideration in mental health care in terms of practice and education.
探讨决策树方法在英国精神卫生保健合作临床决策中的应用。
虽然这种决策方法已在急性护理环境中得到研究,但在精神卫生环境下用于临床决策的公开证据很少。双重诊断(在此案例中为精神分裂症和药物滥用)的复杂性以及不同专业人员的不同观点常常阻碍决策过程。本文强调了该方法如何作为一种多专业合作决策方法在英国社区精神卫生保健背景下成功应用。
对相关文献进行选择性综述,并对决策树框架进行案例研究应用。
将决策树框架应用于精神卫生实践中的临床决策过程可能很耗时,而且在该过程中纳入患者并不总是合适的。该方法提供了一种赋予数值的方法,以支持复杂的多专业决策,并考虑基础文献为最终决策提供信息。使用决策树提供了一个通用框架,可帮助专业人员深入研究他们可用的选项,同时考虑影响精神卫生合作实践决策的复杂变量。在精神卫生保健的实践和教育方面,决策树的使用值得进一步考虑。