Omicron detection with large language models and YouTube audio data

Publicly available audio data presents a unique opportunity for the development of digital health technologies with large language models (LLMs). In this study, YouTube was mined to collect audio data from individuals with self-declared positive COVID-19 tests as well as those with other upper respiratory infections (URI) and healthy subjects discussing a diverse range of topics. The resulting dataset was transcribed with the Whisper model and used to assess the capacity of LLMs for detecting self-reported COVID-19 cases and performing variant classification. Following prompt optimization, LLMs achieved accuracies of 0.89, 0.97, respectively, in the tasks of identifying self-reported COVID-19 cases and other respiratory illnesses. The model also obtained a mean accuracy of 0.77 at identifying the variant of self-reported COVID-19 cases using only symptoms and other health-related factors described in the YouTube videos. In comparison with past studies, which used scripted, standardized voice samples to capture biomarkers, this study focused on extracting meaningful information from public online audio data. This work introduced novel design paradigms for pandemic management tools, showing the potential of audio data in clinical and public health applications.

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics

BACKGROUND

Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.

OBJECTIVE

(1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.

MATERIALS AND METHODS

We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric.

RESULTS

Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.

CONCLUSION

Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.

Health Management in the Home: A Qualitative Study of Pregnant Women and Their Caregivers

With growth in consumer health technologies, patients and caregivers have become increasingly involved in their health and medical care. Such health-related engagement often occurs at home. Pregnancy is a common condition and, for many women, their first exposure to health management practices. This study examined how pregnant women and caregivers managed health in their homes. Participants completed sociodemographic surveys and semi-structured interviews about living situation, information needs, and technology use. Using an iterative, inductive coding approach, we identified themes about health management, including the physical home, help at home, community, the virtual home, and biggest concerns. Most expectant mothers encountered everyday problems with mobility and household management. Pregnant women desired more assistance from caregivers, who often did not know how to help. Caregivers who provided help took on new roles. Many expectant families did not trust advice found online. Over half of expectant families had biggest concerns that involved the home.