Exploring Patient and Staff Experiences of Video Consultations During COVID-19 in an English Outpatient Care Setting: Secondary Data Analysis of Routinely Collected Feedback Data

Abstract Background: Video consultations (VCs) were rapidly implemented in response to COVID-19, despite modest progress prior to the pandemic. Objectives: To explore staff and patient experiences of VCs implemented during COVID-19, and use feedback insights to support quality improvement and service development. Methods: Secondary data analysis was conducted on 955 (22.6%) patient responses and 521 (12.3%) staff responses routinely collected following a VC between June-July 2020 in a rural, aging and outpatient care setting at a single NHS Trust. Patient and staff feedback were summarised using descriptive statistics and inductive thematic analysis and presented to Trust stakeholders. Results: Most (93.2%) patients reported having ‘good’ (n=210, 22.0%), or ‘very good’ (n=680, 71.2%) experience with VCs and felt listened to and understood (n=904, 94.7%). Most patients accessed their VC alone (n=806, 84.4%), except for those aged 71+ (n=23/58, 39.7%), with ease of joining VCs negatively associated with age (P<.001). Despite more difficulties joining, older people were most likely to be satisfied with the technology (n=46/58, 79.3%). Both patients and staff generally felt patients’ needs had been met (n=860, 90.1%, n=453, 86.9% respectively), although staff appeared to overestimate patient dissatisfaction with VC outcome (P=.021). Patients (n=848, 88.8%) and staff (n=419, 80.5%) generally felt able to communicate everything they wanted, although patients were significantly more positive than staff (P<.001). Patient satisfaction with communication was positively associated with technical performance satisfaction (P<.001). Most staff (89.8%) reported positive (n=185, 35.5%), or very positive (n=281, 54.3%) experiences of joining and managing a VC. Staff reported reductions in carbon footprint (n=380, 72.9%) and time (n=373, 71.6%). Most (n=880, 92.1%) patients would choose VCs again. Inductive thematic analysis of patient and staff responses identified three themes: i) barriers including technological difficulties, patient information and suitability concerns; ii) potential benefits including reduced stress, enhanced accessibility, cost and time savings; and iii) suggested improvements including trial calls, turning music off, photo uploads, expanding written character limit, supporting other internet browsers and shared interactive screen. This routine feedback, including evidence to suggest patients were more satisfied than clinicians had anticipated, was presented to relevant Trust stakeholders allowing improved processes and supporting development of a business case to inform the Trust decision on continuing VCs beyond COVID-19 restrictions. Conclusions: Findings highlight the importance of regularly reviewing and responding to routine feedback following the implementation of a new digital service. Feedback helped the Trust improve the VC service, challenge clinician held assumptions about patient experience and inform future use of VCs. The feedback has focussed improvement efforts on patient information, technological improvements such as blurred backgrounds and interactive white boards, and responding to the needs of patients with dementia, communication or cognitive impairment or lack of appropriate technology. Findings have implications for other health providers

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction

The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease

Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysisCentral MessagePerspective

Objective: The effects of Coronavirus disease 2019 (COVID-19) infection and altered processes of care on nonelective coronary artery bypass grafting (CABG) outcomes remain unknown. We hypothesized that patients with COVID-19 infection would have longer hospital lengths of stay and greater mortality compared with COVID-negative patients, but that these outcomes would not differ between COVID-negative and pre-COVID controls. Methods: The National COVID Cohort Collaborative 2020-2022 was queried for adult patients undergoing CABG. Patients were divided into COVID-negative, COVID-active, and COVID-convalescent groups. Pre-COVID control patients were drawn from the National Surgical Quality Improvement Program database. Adjusted analysis of the 3 COVID groups was performed via generalized linear models. Results: A total of 17,293 patients underwent nonelective CABG, including 16,252 COVID-negative, 127 COVID-active, 367 COVID-convalescent, and 2254 pre-COVID patients. Compared to pre-COVID patients, COVID-negative patients had no difference in mortality, whereas COVID-active patients experienced increased mortality. Mortality and pneumonia were higher in COVID-active patients compared to COVID-negative and COVID-convalescent patients. Adjusted analysis demonstrated that COVID-active patients had higher in-hospital mortality, 30- and 90-day mortality, and pneumonia compared to COVID-negative patients. COVID-convalescent patients had a shorter length of stay but a higher rate of renal impairment. Conclusions: Traditional care processes were altered during the COVID-19 pandemic. Our data show that nonelective CABG in patients with active COVID-19 is associated with significantly increased rates of mortality and pneumonia. The equivalent mortality in COVID-negative and pre-COVID patients suggests that pandemic-associated changes in processes of care did not impact CABG outcomes. Additional research into optimal timing of CABG after COVID infection is warranted