biostudies-literatureDavis MNIMH NIH HHS645-653https://www.ebi.ac.uk/biostudies/studies/S-EPMC11056301biostudies-literatureUnknown24(4)<h4>Objective</h4>To understand adolescent, parent, and provider perceptions of a machine learning algorithm for detecting adolescent suicide risk prior to its implementation primary care.<h4>Methods</h4>We conducted semi-structured, qualitative interviews with adolescents (n = 9), parents (n = 12), and providers (n = 10; mixture of behavioral health and primary care providers) across two major health systems. Interviews were audio recorded and transcribed with analyses supported by use of NVivo. A codebook was developed combining codes derived inductively from interview transcripts and deductively from implementation science frameworks for content analysis.<h4>Results</h4>Reactions to the algorithm were mixed. While many participants expressed privacy concerns, they believed the algorithmAcademic pediatricsAdolescent, Parent, and Provider Perceptions of a Predictive Algorithm to Identify Adolescent Suicide Risk in Primary Care.PMC11056301P50 MH115838Ryan NDDoupnik SKBrent DASchwartz KTGYoung JFMelhem NMStepp SDHamm MEGeorge-Milford BDysart GCDavis MfalseAdolescent, Parent, and Provider Perceptions of a Predictive Algorithm to Identify Adolescent Suicide Risk in Primary Care.<h4>Objective</h4>To understand adolescent, parent, and provider perceptions of a machine learning algorithm for detecting adolescent suicide risk prior to its implementation primary care.<h4>Methods</h4>We conducted semi-structured, qualitative interviews with adolescents (n = 9), parents (n = 12), and providers (n = 10; mixture of behavioral health and primary care providers) across two major health systems. Interviews were audio recorded and transcribed with analyses supported by use of NVivo. A codebook was developed combining codes derived inductively from interview transcripts and deductively from implementation science frameworks for content analysis.<h4>Results</h4>Reactions to the algorithm were mixed. While many participants expressed privacy concerns, they believed the algorithm2024-01-01T00:00:00Z2024 May-Jun2025-07-12T03:05:53.721Z2025-07-12T03:05:53.721ZS-EPMC110563013819088510.1016/j.acap.2023.12.015