Objective To reduce pathogen exposure, conserve personal protective equipment, and facilitate health care personnel work participation in the setting of the COVID-19 pandemic, three affiliated institutions rapidly and independently deployed inpatient telemedicine programs during March 2020. We describe key features and early learnings of these programs in the hospital setting. Methods Relevant clinical and operational leadership from an academic medical center, pediatric teaching hospital, and safety net county health system met to share learnings shortly after deploying inpatient telemedicine. A summative analysis of their learnings was re-circulated for approval. Results All three institutions faced pressure to urgently standup new telemedicine systems while still maintaining secure information exchange. Differences across patient demographics and technological capabilities led to variation in solution design, though key technical considerations were similar. Rapid deployment in each system relied on readily available consumer-grade technology, given the existing familiarity to patients and clinicians and minimal infrastructure investment. Preliminary data from the academic medical center over one month suggested positive adoption with 631 inpatient video calls lasting an average (standard deviation) of 16.5 minutes (19.6) based on inclusion criteria. Discussion The threat of an imminent surge of COVID-19 patients drove three institutions to rapidly develop inpatient telemedicine solutions. Concurrently, federal and state regulators temporarily relaxed restrictions that would have previously limited these efforts. Strategic direction from executive leadership, leveraging off-the-shelf hardware, vendor engagement, and clinical workflow integration facilitated rapid deployment. Conclusion The rapid deployment of inpatient telemedicine is feasible across diverse settings as a response to the COVID-19 pandemic.

Heart failure with preserved ejection fraction (HFpEF) is a prevalent condition with no established prevention or treatment strategies. Furthermore, the pathophysiology and predisposing risk factors for HFpEF are incompletely understood. Therefore we sought to characterize the incidence and determinants of HFpEF in the multi-ethnic study of atherosclerosis (MESA). Our study included 6,781 MESA participants (White, Black, Chinese, and Hispanic men and women 45–84 years of age, free of baseline cardiovascular disease). The primary endpoint was time to diagnosis of HFpEF (left ventricular ejection fraction ≥ 45%). Multivariable adjusted hazard ratios (HR) with 95% confidence intervals (CI) were calculated to identify predictors of HFpEF. Over median follow-up of 11.2 years (10.6 – 11.7), 111 individuals developed HFpEF (cumulative incidence 1.7%). Incidence rates were similar across all races/ethnicities. Age (HR 2.3 [1.7–3.0]), hypertension (HR 1.8 [1.1 – 2.9]), diabetes (HR 2.3 [1.5–3.7]), BMI (HR 1.4 [1.1–1.7]), left ventricular hypertrophy by electrocardiography (HR 4.3 [1.7–11.0]), interim MI (HR 4.8 [2.7–8.6]), elevated NT-proBNP (HR 2.4 [1.5–4.0]), detectable troponin T (HR 4.5 [1.9–10.9]), and left ventricular mass index by MRI (1.3 [1.0–1.6]) were significant predictors of incident HFpEF. Worsening renal function, inflammatory markers, and coronary artery calcium were significant univariate, but not multivariate predictors of HFpEF. Gender was neither a univariate nor multivariate predictor of HFpEF. In conclusion, we demonstrate several risk factors and biomarkers associated with incident HFpEF that were consistent across different racial/ethnic groups, and may represent potential therapeutic targets for the prevention and treatment of HFpEF.

There is substantial interest in using presenting symptoms to prioritize testing for COVID-19 and establish symptom-based surveillance. However, little is currently known about the specificity of COVID-19 symptoms. To assess the feasibility of symptombased screening for COVID-19, we used data from tests for common respiratory viruses and SARS-CoV-2 in our health system to measure the ability to correctly classify virus test results based on presenting symptoms. Based on these results, symptom-based screening may not be an effective strategy to identify individuals who should be tested for SARS-CoV-2 infection or to obtain a leading indicator of new COVID-19 cases.

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The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models’ capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.