Chrome Extension
WeChat Mini Program
Use on ChatGLM

Managing Patients with COVID-19 in Armenia Using a Remote Monitoring System: Descriptive Study

JMIR Public Health and Surveillance(2024)

Turpanjian College of Health Sciences American University of Armenia Yerevan Armenia | Innovation Studio Children's Hospital Los Angeles Los Angeles | Department of Surgery Children's Hospital Los Angeles Los Angeles | New York Medical College Valhalla | Allied Anesthesia Medical Group

Cited 0|Views4
Abstract
BackgroundThe COVID-19 pandemic has imposed immense stress on global health care systems, especially in low- and middle-income countries (LMICs). Armenia, a middle-income country in the Caucasus region, contended with the pandemic and a concurrent war, resulting in significant demand on its already strained health care infrastructure. The COVID@home program was a multi-institution, international collaboration to address critical hospital bed shortages by implementing a home-based oxygen therapy and remote monitoring program. ObjectiveThe objective of this study was to describe the program protocol and clinical outcomes of implementing an early discharge program in Armenia through a collaboration of partner institutions, which can inform the future implementation of COVID-19 remote home monitoring programs, particularly in LMICs or low-resource settings. MethodsSeven hospitals in Yerevan participated in the COVID@home program. A web app based on OpenMRS was developed to facilitate data capture and care coordination. Patients meeting eligibility criteria were enrolled during hospitalization and monitored daily while on oxygen at home. Program evaluation relied on data extraction from (1) eligibility and enrollment forms, (2) daily monitoring forms, and (3) discharge forms. ResultsOver 11 months, 439 patients were screened, and 221 patients were managed and discharged. Around 94% (n=208) of participants safely discontinued oxygen therapy at home, with a median home monitoring duration of 26 (IQR 15-45 days; mean 32.33, SD 25.29) days. Women (median 28.5, mean 35.25 days) had similar length of stay to men (median 26, mean 32.21 days; P=.75). Despite challenges in data collection and entry, the program demonstrated feasibility and safety, with a mortality rate below 1% and low re-admission rate. Opportunities for operational and data quality improvements were identified. ConclusionsThis study contributes practical evidence on the implementation and outcomes of a remote monitoring program in Armenia, offering insights into managing patients with COVID-19 in resource-constrained settings. The COVID@home program’s success provides a model for remote patient care, potentially alleviating strain on health care resources in LMICs. Policymakers can draw from these findings to inform the development of adaptable health care solutions during public health crises, emphasizing the need for innovative approaches in resource-limited environments.
More
Translated text
Key words
COVID-19,remote patient monitoring,Armenia,web platform,home oxygen therapy,pandemic,global health care,low and middle-income countries,health care infrastructure,Yerevan,home monitoring,resource-constrained
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest