RESUMEN
In the landscape of sleep surgery, the Inspire® Upper Airway Stimulation (UAS) device has gained prominence as an increasingly popular treatment option for obstructive sleep apnea, prompting significant discourse across social media platforms. This study explores the social media narrative of the UAS device, particularly the nature of multimedia content, author demographics, and audience engagement on Instagram, Facebook, and TikTok. Our analysis encompassed 423 public posts, revealing images (67.4%) and videos (28.1%) as the dominant content types, with over a third of posts authored by physicians. A notable 40% of posts were advertisements, whereas patient experiences comprised 34.5%. TikTok, although presenting a smaller sample size, showed a substantially higher engagement rate, with posts averaging 152.9 likes, compared with Instagram and Facebook at 32.7 and 41.2 likes, respectively. The findings underscore the need for otolaryngologists and healthcare professionals to provide clear, evidence-based information on digital platforms. Given social media's expanding role in healthcare, medical professionals must foster digital literacy and safeguard the accuracy of health information online. In this study, we concluded that maintaining an evidence-based, transparent digital dialogue for medical innovations such as the UAS device necessitates collaborative efforts among physicians, health institutions, and technology companies.
RESUMEN
The rise in nonmedical opioid overdoses over the last two decades necessitates improved detection technologies. Manual opioid screening exams can exhibit excellent sensitivity for identifying the risk of opioid misuse but can be time-consuming. Algorithms can help doctors identify at-risk people. In the past, electronic health record (EHR)-based neural networks outperformed Drug Abuse Manual Screenings in sparse studies; however, recent data shows that it may perform as well or less than manual screenings. Herein, a discussion of several different manual screenings and recommendations is contained, along with suggestions for practice. A multi-algorithm approach using EHR yielded strong predictive values of opioid use disorder (OUD) over a large sample size. A POR (Proove Opiate Risk) algorithm provided a high sensitivity for categorizing the risk of opioid abuse within a small sample size. All established screening methods and algorithms reflected high sensitivity and positive predictive values. Neural networks based on EHR also showed significant effectiveness when corroborated with Drug Abuse Manual Screenings. This review highlights the potential of algorithms for reducing provider costs and improving the quality of care by identifying nonmedical opioid use (NMOU) and OUD. These tools can be combined with traditional clinical interviewing, and neural networks can be further refined while expanding EHR.