Describing Perspectives of Telehealth and the Impact on Equity in Access to Health Care from Community and Provider Perspectives: A Multimethod Analysis.

Introduction: The rapid adoption of telehealth as a result of the COVID-19 pandemic provided the opportunity to examine perceptions of health care access and use of telehealth for delivery of effective and equitable care in low-income and historically marginalized communities. Methods: Focusing on communities with high social vulnerability, a multimethod analysis of combined perspectives, collected February through August 2022 from 112 health care providers, obtained from surveys and interviews, and 23 community members via 3 focus groups on access to care and telehealth. Qualitative data were analyzed using the Health Equity and Implementation Framework to identify barriers, facilitators, and recommendations for the implementation of telehealth using a health equity lens. Results: Participants perceived that telehealth helped maintain access to health care during the pandemic by addressing barriers including provider shortages, transportation concerns, and scheduling conflicts. Additional benefits suggested were improved care quality and coordination attributed to convenient avenues for care delivery and communication among providers and patients. However, many barriers to telehealth were reported and considered to limit equitable access to care. These included restrictive or changing policies regarding allowable services provided via telehealth, and availability of technology and broadband services to enable telehealth visits. Recommendations provided insight into care delivery innovation opportunities and potential policy changes to address equitable access to care. Conclusion: Integration of telehealth into models of care delivery could improve access to health care services and promote communication among providers and patients to improve care quality. Our findings have implications that are critical for future policy reforms and telehealth research.

FIOLA: an accelerated pipeline for fluorescence imaging online analysis.

Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.