Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression
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 fo...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02410-1 |
| _version_ | 1797363242730782720 |
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| author | Meredith Gunlicks-Stoessel Yangchenchen Liu Catherine Parkhill Nicole Morrell Mimi Choy-Brown Christopher Mehus Joel Hetler Gerald August |
| author_facet | Meredith Gunlicks-Stoessel Yangchenchen Liu Catherine Parkhill Nicole Morrell Mimi Choy-Brown Christopher Mehus Joel Hetler Gerald August |
| author_sort | Meredith Gunlicks-Stoessel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:17:28Z |
| format | Article |
| id | doaj.art-8c3b3c27119843668705d3be6aea6549 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-08T16:17:28Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-8c3b3c27119843668705d3be6aea65492024-01-07T12:29:15ZengBMCBMC Medical Informatics and Decision Making1472-69472024-01-0124111110.1186/s12911-023-02410-1Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depressionMeredith Gunlicks-Stoessel0Yangchenchen Liu1Catherine Parkhill2Nicole Morrell3Mimi Choy-Brown4Christopher Mehus5Joel Hetler6Gerald August7Department of Psychiatry & Behavioral Sciences, University of MinnesotaDepartment of Psychology, University of MinnesotaDepartment of Psychiatry & Behavioral Sciences, University of MinnesotaCenter for Applied Research and Educational Improvement, University of MinnesotaSchool of Social Work, University of MinnesotaCenter for Applied Research and Educational Improvement, University of MinnesotaDepartment of Family Social Science, University of MinnesotaDepartment of Family Social Science, University of MinnesotaAbstract 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.https://doi.org/10.1186/s12911-023-02410-1Clinical decision support systemsDepressionAdolescentsHealth care providersAttitudes |
| spellingShingle | Meredith Gunlicks-Stoessel Yangchenchen Liu Catherine Parkhill Nicole Morrell Mimi Choy-Brown Christopher Mehus Joel Hetler Gerald August Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression BMC Medical Informatics and Decision Making Clinical decision support systems Depression Adolescents Health care providers Attitudes |
| title | Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression |
| title_full | Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression |
| title_fullStr | Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression |
| title_full_unstemmed | Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression |
| title_short | Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression |
| title_sort | adolescent parent and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression |
| topic | Clinical decision support systems Depression Adolescents Health care providers Attitudes |
| url | https://doi.org/10.1186/s12911-023-02410-1 |
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