Huang, Ming

566 Background: Video telemedicine visits (VTVs) have the potential to overcome disparities in access to cancer care for many socially, geographically, and economically disadvantaged populations. Internet connectivity, digital literacy, and socioeconomic factors are traditionally cited as influencing VTV utilization. However, digital diffusion has occurred rapidly across several sectors, including healthcare, since the onset of the COVID-19 pandemic, and less is known about how these factors have evolved to influence VTV utilization behavior over this time. Through a qualitative approach, we aimed to: (1) explore the lived experiences of cancer patients who transitioned to VTV use as compared to those who persistently did not, and (2) identify similarities and differences between these two groups that could inform targeted interventions to support access and patient acceptance of VTVs in the setting of cancer care. Methods: During June 2024, we conducted semi-structured interviews with 24 patients (Mean age 71.3 years, range 42-88; female 50%) receiving ongoing, longitudinal cancer care at our academic medical center since July 2021 who had not used VTVs through at least October 2022. Using purposeful sampling, patients were equally divided according to VTV utilization behaviors defined as “transitioned users” (TU; i.e. those who transitioned to VTV utilization after October 2022) and “persistent non-users” (PNU). Transcripts were analyzed using a combined deductive–inductive thematic approach and categorized into themes of barriers and facilitators across three domains consistent with the COM-B model: capabilities (C), opportunities (O), and motivations (M). Results: TU patients frequently cited a reduction in travel and logistical considerations as a primary reason for uptake of VTVs. PNU patients frequently reported digital literacy barriers, particularly among older patients, as primary reasons for lack of VTV utilization. TU patients reported more experience and comfort in technical skills as compared to PNU participants. PNU patients also reported less frequent offering of VTV as an option by their care teams over time, implying they may have transitioned to VTV utilization if it had been offered with more regularity. Both study groups desired improved internet access and technical support to enhance (or, in the case of PNU patients, enable) VTV utilization. Conclusions: These findings suggest that multi-modal care delivery may best serve current patient populations, not requiring, yet offering VTV services as an adjunct to in-person care. Longer term, individual barriers to VTV use will also need to be addressed to further optimize access for patients who desire to incorporate this care modality.

Abstract Objectives The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs—a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. Materials and Methods We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. Results The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. Discussion This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. Conclusions The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.

PURPOSE In response to the COVID-19 pandemic, many cancer practices rapidly adopted telehealth services. However, there is a paucity of data regarding ongoing telehealth visit utilization beyond this initial response. The purpose of this study was to assess changes in variables associated with telehealth visit utilization over time. METHODS This is a cross-sectional, year-over-year, retrospective analysis of telehealth visits conducted across a multisite, multiregional cancer practice in the United States. Multivariable models examined the association of patient- and provider-level variables with telehealth utilization across outpatient visits conducted over three 8-week periods from July to August in 2019 (n = 32,537), 2020 (n = 33,399), and 2021 (n = 35,820). RESULTS The rate of telehealth utilization increased from <0.01% (2019) to 11% (2020) to 14% (2021). The most significant patient-level factors associated with increased telehealth utilization included nonrural residence and age ≤65 years. Among patients residing in rural settings, video visit utilization rates were significantly lower and phone visit utilization rates were significantly higher compared with patients from nonrural residences. Regarding provider-level factors, widening differences in telehealth utilization were observed at tertiary versus community-based practice settings. Increased telehealth utilization was not associated with duplicative care as per-patient and per-physician visit volumes in 2021 remained consistent with prepandemic levels. CONCLUSION We observed continuous expansion in telehealth visit utilization from 2020 to 2021. Our experiences suggest that telehealth can be integrated into cancer practices without evidence of duplicative care. Future work should examine sustainable reimbursement structures and policies to ensure accessibility of telehealth as a means to facilitate equitable, patient-centered cancer care.

AbstractIntroductionThere is some evidence that social media interventions can promote smoking cessation. This randomized controlled pilot study is the first to evaluate the feasibility and potential efficacy of a Facebook smoking cessation intervention among Alaska Native (AN) adults.Aims and MethodsRecruitment and data collection occurred from December 2019 to March 2021. Participants were recruited statewide in Alaska using Facebook advertisements with a targeted sample of 60 enrolled. Participants were stratified by gender, age, and rural or urban residence and randomly assigned to receive referral resources on evidence-based cessation treatments (EBCTs) (control, n = 30) or these resources plus a 3-month, closed (private), culturally tailored, Facebook group (intervention, n = 31) that connected participants to EBCT resources and was moderated by two Alaska Native Trained Tobacco Specialists. Assessments were conducted online post-randomization at 1, 3, and 6 months. Outcomes were feasibility (recruitment, retention, and intervention engagement), self-reported use of EBCTs, and biochemically confirmed seven-day point-prevalence smoking abstinence.ResultsOf intervention participants, 90% engaged (eg posted, commented) more than once. Study retention was 57% at 6 months (no group differences). The proportion utilizing EBCTs was about double for intervention compared with the control group participants at 3 and 6 months. Smoking abstinence was higher for intervention than control participants at 3 months (6.5% vs. 0%, p = .16) but comparable at 6 months (6.4% vs. 6.7%, p = .97).ConclusionsWhile additional research is needed to promote long-term cessation, this pilot trial supports recruitment feasibility during the Coronavirus Disease 2019 (COVID-19) pandemic, consumer uptake, and a signal for intervention efficacy on the uptake of cessation treatment and short-term smoking abstinence.ImplicationsThis study is the first evaluation of a social media intervention for smoking cessation among Indigenous people. We learned that statewide Facebook recruitment of AN adults who smoke was feasible and there was a signal for the efficacy of a Facebook intervention on the uptake of EBCT and short-term (3 months) biochemically verified smoking abstinence. Clinically, social media platforms may complement current care models by connecting AN individuals and others living in hard-to-reach communities to cessation treatment resources.