Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression

Meredith Gunlicks-Stoessel; Yangchenchen Liu; Catherine Parkhill; Nicole Morrell; Mimi Choy-Brown; Christopher Mehus; Joel Hetler; Gerald August

doi:10.1186/s12911-023-02410-1

Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression

BMC Med Inform Decis Mak. 2024 Jan 2;24(1):4. doi: 10.1186/s12911-023-02410-1.

Authors

Meredith Gunlicks-Stoessel¹, Yangchenchen Liu², Catherine Parkhill³, Nicole Morrell⁴, Mimi Choy-Brown⁵, Christopher Mehus^{4

6}, Joel Hetler⁶, Gerald August⁶

Affiliations

¹ Department of Psychiatry & Behavioral Sciences, University of Minnesota, 2025 E River Parkway, 55414, Minneapolis, MN, USA. mgunlick@umn.edu.
² Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
³ Department of Psychiatry & Behavioral Sciences, University of Minnesota, 2025 E River Parkway, 55414, Minneapolis, MN, USA.
⁴ Center for Applied Research and Educational Improvement, University of Minnesota, St. Paul, MN, USA.
⁵ School of Social Work, University of Minnesota, St. Paul, MN, USA.
⁶ Department of Family Social Science, University of Minnesota, St. Paul, MN, USA.

Abstract

Background: Machine learning based clinical decision support systems (CDSSs) have been proposed as a means of advancing personalized treatment planning for disorders, such as depression, that have a multifaceted etiology, course, and symptom profile. However, machine learning based models for treatment selection are rare in the field of psychiatry. They have also not yet been translated for use in clinical practice. Understanding key stakeholder attitudes toward machine learning based CDSSs is critical for developing plans for their implementation that promote uptake by both providers and families.

Methods: In Study 1, a prototype machine learning based Clinical Decision Support System for Youth Depression (CDSS-YD) was demonstrated to focus groups of adolescents with a diagnosis of depression (n = 9), parents (n = 11), and behavioral health providers (n = 8). Qualitative analysis was used to assess their attitudes towards the CDSS-YD. In Study 2, behavioral health providers were trained in the use of the CDSS-YD and they utilized the CDSS-YD in a clinical encounter with 6 adolescents and their parents as part of their treatment planning discussion. Following the appointment, providers, parents, and adolescents completed a survey about their attitudes regarding the use of the CDSS-YD.

Results: All stakeholder groups viewed the CDSS-YD as an easy to understand and useful tool for making personalized treatment decisions, and families and providers were able to successfully use the CDSS-YD in clinical encounters. Parents and adolescents viewed their providers as having a critical role in the use the CDSS-YD, and this had implications for the perceived trustworthiness of the CDSS-YD. Providers reported that clinic productivity metrics would be the primary barrier to CDSS-YD implementation, with the creation of protected time for training, preparation, and use as a key facilitator.

Conclusions: Machine learning based CDSSs, if proven effective, have the potential to be widely accepted tools for personalized treatment planning. Successful implementation will require addressing the system-level barrier of having sufficient time and energy to integrate it into practice.

Keywords: Adolescents; Attitudes; Clinical decision support systems; Depression; Health care providers.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Adolescent
Decision Support Systems, Clinical*
Depression
Focus Groups
Humans
Machine Learning
Parents

Grants and funding

UL1 TR002494/TR/NCATS NIH HHS/United States