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

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.

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

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 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: The CDSS-YD has the potential to be a widely accepted and useful tool for personalized treatment planning. Successful implementation will require addressing the system-level barrier of having sufficient time and energy to integrate it into practice.

Depression screening and management among adolescents in primary care: factors associated with best practice.

OBJECTIVE: To compare depression identification and management perceptions and practices between professions and disciplines in primary care and examine factors that increase the likelihood of administering a standardized depression screening instrument, asking about patients' depressive symptoms, and using best practice when managing depressed adolescents. METHODS: Data came from an online survey of clinicians in Minnesota (20% response rate). Analyses involved bivariate tests and linear regressions. RESULTS: The analytic sample comprised 260 family medicine physicians, 127 pediatricians, 96 family nurse practitioners, and 54 pediatric nurse practitioners. Overall, few differences emerged between physicians and nurse practitioners or family and pediatric clinicians regarding addressing depression among adolescents. Two factors associated with administering a standardized instrument included having clear protocols for follow-up after depression screening and feeling better prepared to address depression among adolescents. CONCLUSIONS: Enhancing clinicians' competence to address depression and developing postscreening protocols could help providers implement universal screening in primary care.

Nonsuicidal self-injury among adolescents: a training priority for primary care providers.

Primary care providers were surveyed to determine how prepared they feel to address nonsuicidal self-injury (NSSI) among adolescents, their interest in training on NSSI, and factors associated with routinely asking about NSSI when providing health supervision. Participants included family medicine physicians (n = 260), pediatricians (n = 127), family nurse practitioners (n = 96), and pediatric nurse practitioners (n = 54). Almost 50% felt unprepared to address NSSI, and over 70% wanted training in this area. Overall, relative to other areas of mental health care, clinicians felt least prepared to address and wanted more training on NSSI. Just 27% reported they routinely inquired about NSSI during health supervision. Factors associated with routinely asking about NSSI were identifying as female (OR = 2.37; 95% CI = 1.25-4.49), feeling better prepared to address NSSI (OR = 1.51; 95% CI = 1.04-2.20), and more frequently using a psychosocial interview to identify adolescents in distress (OR = 1.23; 95% CI = 1.02-1.48). Teaching clinicians to assess NSSI within a psychosocial interview may increase screening for and identification of the behavior among adolescents in primary care.