Using Natural Language Processing to Optimize Engagement of Those with Behavioral Health Conditions that Worsen Chronic Medical Disease
文献类型： 会议论文
第一作者： Peter Bearse
作者： Peter Bearse 1 ; Atif Farid Mohammad 1 ; Intisar Rizwan I. Haque 1 ; Susan Kuypers 1 ; Rachel Fournier 1 ;
作者机构： 1.Catasys Inc.
会议名称： International Conference on Image Processing, Computer Vision, & Pattern Recognition
主办单位：
页码： 601-610
摘要： Untreated behavioral health conditions worsen chronic medical disease, leading to poor member outcomes and low-value, high-cost medical utilization. Supporting health plan members with health coaching to address behaviors and social determinants of health leads to improved outcomes and high-value care. For this purpose, member engagement specialists (MESs) employ a skilled call center model to perform the initial outreach to members. Given the sensitive nature of the topics, MESs are highly trained. We hypothesized that MESs use words and phrases that are both positively and negatively associated with engagement and that natural language processing on call transcripts would uncover those words and phrases.
分类号： TP3-53
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