Decision making for health care professionals: use of decision trees within the community mental health setting
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Updated: Oct 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
1Thames Valley University, Slough, Berkshire, UK. gwen.bonner@tvu.ac.uk
Aim Of The Paper:
To examine the application of the decision tree approach to collaborative clinical decision-making in mental health care in the United Kingdom (UK).
Background:
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.
Design:
A selective review of the relevant literature and a case study application of the decision tree framework.
Conclusions:
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.
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