Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients

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Abstract

Objective We deployed a Remote Patient Monitoring (RPM) program to monitor patients with coronavirus disease 2019 (COVID-19) upon hospital discharge. We describe the patient characteristics, program characteristics, and clinical outcomes of patients in our RPM program. Methods We enrolled COVID-19 patients being discharged home from the hospital. Enrolled patients had an app, and were provided with a pulse oximeter and thermometer. Patients self-reported symptoms, O 2saturation, and temperature daily. Abnormal symptoms or vital signs were flagged and assessed by a pool of nurses. Descriptive statistics were used to describe patient and program characteristics. A mixed-effects logistic regression model was used to determine the odds of a combined endpoint of emergency department (ED) or hospital readmission. Results A total of 295 patients were referred for RPM from five participating hospitals, and 225 patients were enrolled. A majority of enrolled patients (66%) completed the monitoring period without triggering an abnormal alert. Enrollment was associated with a decreased odds of ED or hospital readmission (adjusted odds ratio: 0.54; 95% confidence interval: 0.3-0.97; p = 0.039). Referral without enrollment was not associated with a reduced odds of ED or hospital readmission. Conclusion RPM for COVID-19 provides a mechanism to monitor patients in their home environment and reduce hospital utilization. Our work suggests that RPM reduces readmissions for patients with COVID-19 and provides scalable remote monitoring capabilities upon hospital discharge. RPM for postdischarge patients with COVID-19 was associated with a decreased risk of readmission to the ED or hospital, and provided a scalable mechanism to monitor patients in their home environment.

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APA

Gordon, W. J., Henderson, D., Desharone, A., Fisher, H. N., Judge, J., Levine, D. M., … Boxer, R. (2020). Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients. Applied Clinical Informatics, 11(5), 792–801. https://doi.org/10.1055/s-0040-1721039

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