SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89–0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Background: The coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. Objective: The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. Methods: Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively. Results: All biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged (P<.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively. Conclusions: Our results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.

Covid-19 is a new highly contagious RNA viral disease that has caused a global pandemic. Human-to-human transmission occurs primarily through oral and nasal droplets and possibly through the airborne route. The disease may be asymptomatic or the course may be mild with upper respiratory symptoms, moderate with non-life-threatening pneumonia, or severe with pneumonia and acute respiratory distress syndrome. The severe form is associated with significant morbidity and mortality. While patients who are unstable and in acute distress need immediate in-person attention, many patients can be evaluated at home by telemedicine or videoconferencing. The more benign manifestations of Covid-19 may be managed from home to maintain quarantine, thus avoiding spread to other patients and health care workers. This document provides an overview of the clinical presentation of Covid-19, emphasizing telemedicine strategies for assessment and triage of patients. Advantages of the virtual visit during this time of social distancing are highlighted.

Objectives: To identify ICD-10-CM di­agnostic codes associated with the social de­terminants of health (SDOH), determine fre­quency of use of the code for homelessness across time, and examine the frequency of interrupted periods of Medicaid eligibility (ie, Medicaid churn) for beneficiaries with and without this code.Design: Retrospective data analyses of New York State (NYS) Medicaid claims data for years 2006-2017 to determine reliable indicators of SDOH hypothesized to affect Medicaid churn, and for years 2016-2017 to examine frequency of Medicaid churn among patients with and without an indica­tor for homelessness.Methods: Analyses were conducted to assess the frequency of use and pattern of New York State Medicaid claims submission for SDOH codes. Analyses were conducted for Medicaid claims submitted for years 2016-2017 for Medicaid patients with and without a homeless code (ie, ICD-10-CM Z59.0) in 2017.Main Outcome Measures: Any interrup­tion in the eligibility for Medicaid insurance (Medicaid churn), assessed via client identi­fication numbers (CIN) for continuity.Results: ICD-9-CM / ICD-10-CM codes for lack of housing / homelessness demonstrat­ed linear reliability over time (ie, for years 2006-2017) with increased usage. In 2016- 2017, 22.9% of New York Medicaid patients with a homelessness code in 2017 experi­enced at least one interruption of Medicaid eligibility, while 18.8% of Medicaid patients without a homelessness code experienced Medicaid churn.Conclusions: Medicaid policies would do well to take into consideration the barriers to continued enrollment for the Medicaid population. Measures ought to be enacted to reduce Medicaid churn, especially for individuals experiencing homelessness. Ethn Dis.2021;31(1):89-96; doi:10.18865/ ed.31.1.89

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Remote patient monitoring (RPM) has been shown to support adults with treated but uncontrolled hypertension (HTN) outside the clinic setting, yielding significant benefits in the treatment and control of blood pressure (BP). Despite its proven efficacy and recommendation as guideline-concordant care, adoption of RPM is suboptimal, particularly among marginalized populations, who face structural barriers to HTN control. A barrier to equitable adoption among marginalized populations is the lack of digital inclusivity in the design and deployment of RPM. Digitally inclusive tools consider factors such as affordability, access, digital literacy, and skills. To address this challenge, the authors describe a digitally inclusive model of RPM for HTN management within the Family Health Centers (FHCs) at NYU Langone, a federally qualified health center (FQHC) that serves more than 110,000 patients each year. The model uses protocols from the Target: BP initiative in combination with team-based care and digitally inclusive strategies to improve HTN control. Specifically, care teams work collaboratively to identify patients with uncontrolled HTN and order RPM using electronic health record–embedded clinical decision support; provide patients with free home BP monitors and training in accurate BP measurement; deliver language-concordant health coaching and optimize the antihypertensive regimen via a virtual high-risk clinic (VHRC); and monitor patient progress through shared communications. Patients also receive support from community health workers (CHWs) to address digital barriers and unmet social needs. The authors present utilization and preliminary outcome data of their model, involving 429 patients who were enrolled in RPM and the VHRC across five FHC practices between January 1, 2022, and December 31, 2023. Enrolled patients attended a mean of 4.9 (standard deviation [SD]: 0.5) visits with a nurse practitioner for medication adjustments and counseling; 5.7 (SD: 0.5) health coaching visits with a nurse; and 1 visit (SD: 0.2) with a CHW for digital and social needs over a mean of 5.7 months (SD: 0.8). Enrolled patients sent a mean number of 65 BP readings (SD: 96.4) over their period of participation. On average, enrolled patients exhibited a −13.5/−8.0 mmHg reduction from their enrollment date to the date that they were discharged from the VHRC (approximately 5.7 months). This is in comparison to a −0.5/+0.6 mmHg change in mean BP exhibited by patients with uncontrolled HTN not enrolled in the Advancing Long-term Improvements in Hypertension Outcomes through a Team-based Care Approach (ALTA) program and receiving care at the practices during the same period (n=2,843). Across the practices, BP control had also increased from the pre-ALTA baseline period (January 1, 2021, to December 31, 2021) of 68.44%–82.99%, by the end of December 31, 2023, among all patients with uncontrolled HTN. While the implementation of this digitally inclusive RPM model has shown success in a large FQHC that cares for a diverse population of patients, there remain digital inequity barriers that must be addressed at the policy level to ensure this efficacious approach reaches all patients.