Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach - Kirby, Joslyn; Kim, Katherine; Zivkovic, Marko; Wang, Siwei; Garg, Vishvas; Danavar, Akash; Li, Chao; Chen, Naijun; Garg, Amit (Frontiers in Medical Technology, 2024)
Hidradenitis suppurativa (HS) is a chronic, inflammatory follicular skin condition contributing to significant psychosocial and economic burden and a diminished quality-of-life and work productivity. With unclear etiology, accurately diagnosing HS can be challenging, leading to common underdiagnosis or misdiagnosis that results in increased patient and healthcare system burden. We developed a novel model to promote understanding of HS underdiagnosis on a healthcare system level by applying machine learning (ML) to a medical and pharmacy claims database using data from 2000 through 2018. Primary results demonstrated that high-performing models for predicting HS diagnosis can be constructed using claims data, with area under the curve (AUC) of 81-82% observed among the top-performing models. Results of the models developed in this study could be input into the development of an impact of inaction model that determines cost implications of HS diagnosis and treatment delay to healthcare system.