Family Caregiver Comfort with Telehealth Technologies: Differences by Race and Ethnicity in a Cross-Sectional Survey.

Background: Telehealth has seen widespread use since the onset of the COVID-19 pandemic, and 82% patients required assistance in accessing their telehealth appointments. This assistance commonly comes from a family caregiver who may or may not be comfortable using the technologies associated with telehealth. The objective of our study was to analyze a demographically representative survey of U.S. family caregivers to understand the level of comfort using telehealth technologies among family caregivers. Methods: A secondary analysis of survey data collected during the COVID-19 pandemic in 2020. Level of caregiver comfort using computers, smartphones, and tablets was determined through three Likert-style questions. Proportional odds logistic regression was used to understand the associations between demographic variables and level of caregiver comfort using each technology, when adjusting for covariates. Results: A total of 340 caregivers were included in the analysis. Compared with non-Hispanic white caregivers, Asian caregivers had higher odds (odds ratio [OR] 3.14; 95% confidence interval [CI] 1.36, 8.02; p = 0.01) of expressing comfort using computers; black caregivers (OR 0.46; 95% CI 0.21, 0.98; p = 0.04) and Hispanic caregivers (OR 0.36; 95% CI 0.17, 0.79; p = 0.01) expressed lower odds of comfort using smartphones; and Asian caregivers had higher odds (OR 4.64; 95% CI 2.05, 11.69; p = 0.001) of expressing comfort using tablets. Conclusion and Implications: There are identified disparities in the level of technological comfort using computers, smartphones, and tablets by different racial and ethnic groups. Health systems should consider early stakeholder involvement in the design of telehealth technologies, culturally responsive training materials on telehealth technology use to reduce disparities in comfort using telehealth technologies.

Natural language processing for automated annotation of medication mentions in primary care visit conversations.

OBJECTIVES: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. MATERIALS AND METHODS: Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. RESULTS: Sixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set. DISCUSSION: Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. CONCLUSION: Integration of our annotation system with clinical recording applications has the potential to improve patients' understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.