Impact of Telemedicine on Prenatal Counseling at a Tertiary Fetal Center: A Mixed Methods Study.

INTRODUCTION: COVID-19 spurred an unprecedented transition from in-person to telemedicine visits in March 2020 at our institution for all prenatal counseling sessions. This study aims to explore differences in demographics of expectant mothers evaluated pre- and post-telemedicine implementation and to explore the patient experience with telemedicine. METHODS: A mixed methods study was completed for mothers with a pregnancy complicated by a fetal surgical anomaly who visited a large tertiary fetal center. Using medical records as quantitative data, patient information was collected for all prenatal visits from 3/2019 to 3/2021. The sample was grouped into pre- and post-telemedicine implementation (based on transition date of 3/2020). Univariate analysis was used to compare demographics between the study groups. Statistical significance was defined as P < 0.05. Eighteen semi-structured interviews were conducted from 8/2021 to 12/2021 to explore patients' experiences. Line-by-line coding and thematic analysis was performed to develop emerging themes. RESULTS: 292 pregnancies were evaluated from 3/2019 to 3/2021 (pre-telemedicine 123, post-telemedicine 169). There was no significant difference in self-reported race (P = 0.28), ethnicity (P = 0.46), or primary language (P = 0.98). In qualitative interviews, patients reported advantages to telemedicine, including the convenience of the modality with the option to conduct their session in familiar settings (e.g., home) and avoid stressors (e.g., travel to the medical center and finding childcare). Some women reported difficulties establishing a physician-patient connection and a preference for in-person consultations. CONCLUSIONS: There was no difference in patient demographics at our fetal center in the year leading up to, and the time following, a significant transition to telemedicine. However, patients had unique perspectives on the advantages and disadvantages of the telemedicine experience. To ensure patient centered care, these findings suggest patient preference should be considered when scheduling outpatient surgical counseling and visits.

"Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?

There is great interest in the application of 'artificial intelligence' (AI) to pharmacovigilance (PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application of AI to the processing and evaluation of Individual Case Safety Reports (ICSRs) submitted to the FDA Adverse Event Reporting System (FAERS). We describe a general framework for considering the readiness of AI for PV, followed by some examples of the application of AI to ICSR processing and evaluation in industry and FDA. We conclude that AI can usefully be applied to some aspects of ICSR processing and evaluation, but the performance of current AI algorithms requires a 'human-in-the-loop' to ensure good quality. We identify outstanding scientific and policy issues to be addressed before the full potential of AI can be exploited for ICSR processing and evaluation, including approaches to quality assurance of 'human-in-the-loop' AI systems, large-scale, publicly available training datasets, a well-defined and computable 'cognitive framework', a formal sociotechnical framework for applying AI to PV, and development of best practices for applying AI to PV. Practical experience with stepwise implementation of AI for ICSR processing and evaluation will likely provide important lessons that will inform the necessary policy and regulatory framework to facilitate widespread adoption and provide a foundation for further development of AI approaches to other aspects of PV.

Fatal Co-infections with SARS-CoV-2 and Legionella pneumophila, England.

Both Legionella pneumophila and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause pneumonia. L. pneumophila is acquired from water sources, sometimes in healthcare settings. We report 2 fatal cases of L. pneumophila and SARS-CoV-2 co-infection in England. Clinicians should be aware of possible L. pneumophila infections among SARS-CoV-2 patients.

A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System.

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post-market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID-19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID-19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data. Early in the pandemic, Sentinel developed a multi-pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID-19, validated a diagnosis-code based algorithm for identifying patients with COVID-19 in administrative claims data, and coordinated with other national and international initiatives. Sentinel is poised to answer important questions about the natural history of COVID-19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID-19 prevention and treatment.