Effect of SARS-CoV-2 Infection and Infection Severity on Longer-Term Glycemic Control and Weight in People With Type 2 Diabetes.

OBJECTIVE: To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation. RESULTS: There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19. CONCLUSIONS: In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.

Rapid creation of an emergency department telehealth program during the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) has spread to nearly every continent, with over 2.6 m cases confirmed worldwide. Emergency departments care for a significant number of patients who are under investigation for COVID-19 or are COVID-19-positive. When patients present in the emergency department, there is an increased risk of spreading the virus to other patients and staff. We designed an emergency department telehealth program for patients physically in the emergency department, to reduce exposure and conserve personal protective equipment. While traditional telehealth is designed to be patient-specific and device-independent, our emergency department telehealth program was device-specific and patient-independent. In this article, we describe how we rapidly implemented our emergency department telehealth program, used for 880 min of contact time and 523 patient encounters in a 30-day period, which decreased exposure to COVID-19 and conserved personal protective equipment. We share our challenges, successes and recommendations for designing an emergency department telehealth program, building the technological aspects, and deploying telehealth devices in the emergency department environment. Our recommendations can be adopted by other emergency departments to create and run their own emergency department telehealth initiatives.

Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach.

BACKGROUND: Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the "gold standard" reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. OBJECTIVE: This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. METHODS: We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. RESULTS: When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve-receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. CONCLUSIONS: This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

The transformation of hospital medicine to tackle the COVID-19 pandemic crisis.

The coronavirus disease 2019 (COVID-19) pandemic is placing extraordinary strains not only on hospital-wide systems but most especially on hospital medicine across the nation. The specific challenges faced by our hospitalist services are unfathomable. Hospitalist leaders are tasked to rapidly restructure clinical operations to accommodate the large surge in COVID-19 patients. In this perspective, we focus on the management strategies conducted by the Division of Hospital Medicine to tackle the major crisis that specifically impacted the general medicine services.