A Qualitative Study on Using Telemedicine for Precepting and Teaching in the Academic Setting.

PURPOSE: To examine the impact of telemedicine use on precepting and teaching among preceptors and patients during the COVID-19 pandemic. METHOD: The authors conducted a secondary analysis of a qualitative study focusing on providers' and patients' experiences with and attitudes toward telemedicine at 4 academic health centers. Teaching and precepting were emergent codes from the data and organized into themes. Themes were mapped to domains from the 2009 Consolidated Framework for Implementation Research (CFIR), a framework that assists with effective implementation and consists of 5 domains: intervention characteristics, outer settings, inner settings, characteristics of individuals, and process. RESULTS: In total, 86 interviews were conducted with 65 patients and 21 providers. Nine providers and 3 patients recounted descriptions related to teaching and precepting with telemedicine. Eight themes were identified, mapping across all 5 CFIR domains, with the majority of themes (n = 6) within the domains of characteristics of individuals, processes, and intervention characteristics. Providers and patients described how a lack of prepandemic telemedicine experience and inadequate processes in place to precept and teach with telemedicine affected the learning environment and perceived quality of care. They also discussed how telemedicine exacerbated existing difficulties in maintaining resident continuity. Providers described ways communication changed with telemedicine use during the pandemic, including having to wear masks while in the same room as the trainee and sitting closely to remain within range of the camera, as well as the benefit of observing trainees with the attending's camera off. Providers expressed a lack of protected structure and time for teaching and supervising with telemedicine, and a general view that telemedicine is here to stay. CONCLUSIONS: Efforts should focus on increasing knowledge of telemedicine skills and improving processes to implement telemedicine in the teaching setting in order to best integrate it into undergraduate and graduate medical education.

Provider and Patient Experiences of Delays in Primary Care During the Early COVID-19 Pandemic.

BACKGROUND: The necessary suspension of nonacute services by healthcare systems early in the COVID-19 pandemic was predicted to cause delays in routine care in the United States, with potentially serious consequences for chronic disease management. However, limited work has examined provider or patient perspectives about care delays and their implications for care quality in future healthcare emergencies. OBJECTIVE: This study explores primary care provider (PCP) and patient experiences with healthcare delays during the COVID-19 pandemic. METHODS: PCPs and patients were recruited from four large healthcare systems in three states. Participants underwent semistructured interviews asking about their experiences with primary care and telemedicine. Data were analyzed using interpretive description. RESULTS: Twenty-one PCPs and 65 patients participated in interviews. Four main topics were identified: (1) types of care delayed, (2) causes for delays, (3) miscommunication contributing to delays, and (4) patient solutions to unmet care needs. CONCLUSIONS: Both patients and providers reported delays in preventive and routine care early in the pandemic, driven by healthcare system changes and patient concerns about infection risk. Primary care practices should develop plans for care continuity and consider new strategies for assessing care quality for effective chronic disease management in future healthcare system disruptions.

The TeleKidSeq pilot study: incorporating telehealth into clinical care of children from diverse backgrounds undergoing whole genome sequencing.

BACKGROUND: The COVID-19 pandemic forced healthcare institutions and many clinical research programs to adopt telehealth modalities in order to mitigate viral spread. With the expanded use of telehealth, there is the potential to increase access to genomic medicine to medically underserved populations, yet little is known about how best to communicate genomic results via telehealth while also ensuring equitable access. NYCKidSeq, a multi-institutional clinical genomics research program in New York City, launched the TeleKidSeq pilot study to assess alternative forms of genomic communication and telehealth service delivery models with families from medically underserved populations. METHODS: We aim to enroll 496 participants between 0 and 21 years old to receive clinical genome sequencing. These individuals have a neurologic, cardiovascular, and/or immunologic disease. Participants will be English- or Spanish-speaking and predominantly from underrepresented groups who receive care in the New York metropolitan area. Prior to enrollment, participants will be randomized to either genetic counseling via videoconferencing with screen-sharing or genetic counseling via videoconferencing without screen-sharing. Using surveys administered at baseline, results disclosure, and 6-months post-results disclosure, we will evaluate the impact of the use of screen-sharing on participant understanding, satisfaction, and uptake of medical recommendations, as well as the psychological and socioeconomic implications of obtaining genome sequencing. Clinical utility, cost, and diagnostic yield of genome sequencing will also be assessed. DISCUSSION: The TeleKidSeq pilot study will contribute to innovations in communicating genomic test results to diverse populations through telehealth technology. In conjunction with NYCKidSeq, this work will inform best practices for the implementation of genomic medicine in diverse, English- and Spanish-speaking populations.

Conducting clinical genomics research during the COVID-19 pandemic: Lessons learned from the CSER consortium experience.

Clinical research studies have navigated many changes throughout the COVID-19 pandemic. We sought to describe the pandemic's impact on research operations in the context of a clinical genomics research consortium that aimed to enroll a majority of participants from underrepresented populations. We interviewed (July to November 2020) and surveyed (May to August 2021) representatives of six projects in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, which studies the implementation of genome sequencing in the clinical care of patients from populations that are underrepresented in genomics research or are medically underserved. Questions focused on COVID's impact on participant recruitment, enrollment, and engagement, and the transition to teleresearch. Responses were combined and thematically analyzed. Projects described factors at the project, institutional, and community levels that affected their experiences. Project factors included the project's progress at the pandemic's onset, the urgency of in-person clinical care for the disease being studied, and the degree to which teleresearch procedures were already incorporated. Institutional and community factors included institutional guidance for research and clinical care and the burden of COVID on the local community. Overall, being responsive to community experiences and values was essential to how CSER navigated evolving challenges during the COVID-19 pandemic.

Design and rationale of GUARDD-US: A pragmatic, randomized trial of genetic testing for APOL1 and pharmacogenomic predictors of antihypertensive efficacy in patients with hypertension.

RATIONALE AND OBJECTIVE: APOL1 risk alleles are associated with increased cardiovascular and chronic kidney disease (CKD) risk. It is unknown whether knowledge of APOL1 risk status motivates patients and providers to attain recommended blood pressure (BP) targets to reduce cardiovascular disease. STUDY DESIGN: Multicenter, pragmatic, randomized controlled clinical trial. SETTING AND PARTICIPANTS: 6650 individuals with African ancestry and hypertension from 13 health systems. INTERVENTION: APOL1 genotyping with clinical decision support (CDS) results are returned to participants and providers immediately (intervention) or at 6 months (control). A subset of participants are re-randomized to pharmacogenomic testing for relevant antihypertensive medications (pharmacogenomic sub-study). CDS alerts encourage appropriate CKD screening and antihypertensive agent use. OUTCOMES: Blood pressure and surveys are assessed at baseline, 3 and 6 months. The primary outcome is change in systolic BP from enrollment to 3 months in individuals with two APOL1 risk alleles. Secondary outcomes include new diagnoses of CKD, systolic blood pressure at 6 months, diastolic BP, and survey results. The pharmacogenomic sub-study will evaluate the relationship of pharmacogenomic genotype and change in systolic BP between baseline and 3 months. RESULTS: To date, the trial has enrolled 3423 participants. CONCLUSIONS: The effect of patient and provider knowledge of APOL1 genotype on systolic blood pressure has not been well-studied. GUARDD-US addresses whether blood pressure improves when patients and providers have this information. GUARDD-US provides a CDS framework for primary care and specialty clinics to incorporate APOL1 genetic risk and pharmacogenomic prescribing in the electronic health record. TRIAL REGISTRATION: ClinicalTrials.govNCT04191824.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.

OBJECTIVE: The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. DESIGN: Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. SETTING: All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. PARTICIPANTS: Participants over the age of 18 years were included. PRIMARY OUTCOMES: We investigated in-hospital mortality during the study period. RESULTS: A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 µg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 µg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. CONCLUSIONS: In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.