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Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

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Clinicians want to know the specific reasons behind AI-generated suicide risk flags to effectively guide patient treatment and safety planning. Understanding these features is crucial for implementing AI tools in mental healthcare.

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Area of Science:

  • Clinical Psychology
  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Machine learning algorithms can identify patient suicide risk using electronic medical records.
  • Clinician perceptions of these AI-generated suicide risk flags are largely unexplored.
  • Understanding these perceptions is vital for successful algorithm implementation in clinical practice.

Purpose of the Study:

  • To evaluate mental health clinicians' perceptions of suicide risk flags generated by machine learning algorithms.
  • To determine how these flags and their underlying features influence clinical decision-making.

Main Methods:

  • Online surveys were administered to 139 mental health clinicians (68 with complete data).
  • Data were collected between October 2018 and April 2019.
  • Statistical analyses, including chi-squared tests, were used to compare clinician responses.

Main Results:

  • Most clinicians (94.12%) preferred knowing the specific features triggering a suicide risk flag.
  • Clinicians indicated that flag features (e.g., suicidal thoughts) more strongly influenced decisions than others (e.g., age).
  • Safety/crisis response plans were the most frequently chosen intervention in response to flags.

Conclusions:

  • Clinician decision-making is significantly influenced by AI-generated suicide risk flags.
  • The impact of these flags depends on the specific clinical features highlighted, not just the flag's presence.
  • Algorithm utility hinges on transparency of features and clinicians' perceived intuitiveness of these predictors.