Piloting a novel medical student virtual discharge counseling process in the time of the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, we identified a gap in adequate discharge counseling for COVID-19 patients in the Emergency Department. This was due to high patient volumes and lack of patient education regarding a novel disease. Medical students were also restricted from clinical areas due to safety concerns, compromising their clinical experience. We piloted a novel program in which medical students served as virtual discharge counselors for COVID-19 patients via teleconference. We aimed to demonstrate an impact on patient care by examining the patient bounce back rate as well as assessing medical student education and experience. METHODS: This program was piloted in a tertiary care Emergency Department. Medical student volunteers served as virtual discharge counselors. Students were trained in discharge counseling with a standardized protocol and a discharge script. Eligible patients for virtual discharge counseling were 18 years old or greater with a diagnosis of confirmed or suspected COVID-19 and no impediment precluding them from participating in a telemedicine encounter. Counseling was provided via secure teleconference in the patient's preferred language. Counseling included diagnosis, supportive care with medication dosing, quarantine instructions, return precautions, follow up, and time to ask questions. Duration of counseling was recorded and medical students were anonymously surveyed regarding their experience. RESULTS: Over an 18-week period, 45 patients were counseled for a median of 20 min. The 72-hr ED revisit rate was 0%, versus 4.2% in similarly-matched, not counseled COVID-19 patients. 90% of medical students believed this project increased their confidence when speaking with patients while 80% indicated this was their first telemedicine experience. CONCLUSION: Our pilot discharge program provided patients with an extensive discharge counseling experience that would not otherwise be possible in an urban ED setting and demonstrated benefit to patient care. Medical students received a safe clinical experience that improved their communication skills.

Development of an influenza pandemic decision support tool linking situational analytics to national response policy.

National influenza pandemic plans have evolved substantially over recent decades, as has the scientific research that underpins the advice contained within them. While the knowledge generated by many research activities has been directly incorporated into the current generation of pandemic plans, scientists and policymakers are yet to capitalise fully on the potential for near real-time analytics to formally contribute to epidemic decision-making. Theoretical studies demonstrate that it is now possible to make robust estimates of pandemic impact in the earliest stages of a pandemic using first few hundred household cohort (FFX) studies and algorithms designed specifically for analysing FFX data. Pandemic plans already recognise the importance of both situational awareness i.e., knowing pandemic impact and its key drivers, and the need for pandemic special studies and related analytic methods for estimating these drivers. An important next step is considering how information from these situational assessment activities can be integrated into the decision-making processes articulated in pandemic planning documents. Here we introduce a decision support tool that directly uses outputs from FFX algorithms to present recommendations on response options, including a quantification of uncertainty, to decision makers. We illustrate this approach using response information from within the Australian influenza pandemic plan.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Electronic Health Record-Based Surveillance for Community Transmitted COVID-19 in the Emergency Department.

INTRODUCTION: SARS-CoV-2, a novel coronavirus, manifests as a respiratory syndrome (COVID-19) and is the cause of an ongoing pandemic. The response to COVID-19 in the United States has been hampered by an overall lack of diagnostic testing capacity. To address uncertainty about ongoing levels of SARS-CoV-2 community transmission early in the pandemic, we aimed to develop a surveillance tool using readily available emergency department (ED) operations data extracted from the electronic health record (EHR). This involved optimizing the identification of acute respiratory infection (ARI)-related encounters and then comparing metrics for these encounters before and after the confirmation of SARS-CoV-2 community transmission. METHODS: We performed an observational study using operational EHR data from two Midwest EDs with a combined annual census of over 80,000. Data were collected three weeks before and after the first confirmed case of local SARS-CoV-2 community transmission. To optimize capture of ARI cases, we compared various metrics including chief complaint, discharge diagnoses, and ARI-related orders. Operational metrics for ARI cases, including volume, pathogen identification, and illness severity, were compared between the preand post-community transmission timeframes using chi-square tests of independence. RESULTS: Compared to our combined definition of ARI, chief complaint, discharge diagnoses, and isolation orders individually identified less than half of the cases. Respiratory pathogen testing was the top performing individual ARI definition but still only identified 72.2% of cases. From the pre to post periods, we observed significant increases in ED volumes due to ARI and ARI cases without identified pathogen. CONCLUSION: Certain methods for identifying ARI cases in the ED may be inadequate and multiple criteria should be used to optimize capture. In the absence of widely available SARS-CoV-2 testing, operational metrics for ARI-related encounters, especially the proportion of cases involving negative pathogen testing, are useful indicators for active surveillance of potential COVID-19 related ED visits.