Managing Patients With COVID-19 in Armenia Using a Remote Monitoring System: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: The 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. OBJECTIVE: The 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. METHODS: Seven 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. RESULTS: Over 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. CONCLUSIONS: This 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".