Implementing remote monitoring for COVID-19 patients in primary care
Bibliographic record
Abstract
Abstract Background In Germany, most patients with coronavirus disease 2019 (COVID-19) are treated in an outpatient setting. To improve assessments of the health status of COVID-19 outpatients, various remote monitoring models have been developed. However, little information exists on experiences acquired with remote monitoring in an outpatient setting, particularly from a patient perspective. The aim of our ‘COVID-19@home’ study was therefore to implement and evaluate an app-based remote monitoring concept for acute and post-acute COVID-19-patients in primary care. In this paper, we focus on the patients’ evaluation of our remote monitoring approach. Methods Patients with acute COVID-19 measured heart rate, blood pressure, oxygen saturation, and body temperature daily for 28 days. Patients with post-acute COVID-19 determined the same parameters for 12 weeks, supplemented by lung parameters and daily step count. The data were documented using the ‘SaniQ’ smartphone app. COVID-19 symptoms were assessed daily using an app-based questionnaire. Patients’ GPs could access the data on the ‘SaniQ Praxis’ telemedicine platform. We used an app-based questionnaire consisting of 11 questions presented with a 4-point Likert scale to evaluate patient satisfaction. Data were analyzed descriptively. Results Of the 51 patients aged 19-77 years that participated in the study, 42 completed the questionnaire. All patients rated home monitoring as ‘very good’ or ‘rather good’ and were able to integrate the measuring processes into their daily routines. Overall, 93% would recommend the app and the measuring devices to their family and friends. About 60% felt that their COVID-19 treatment had benefited from home monitoring. Only few patients were unsettled by the app and use of the measuring devices. During the course of the study, the implementation process was optimized. Conclusions The use of remote monitoring in COVID-19 patients is feasible and was evaluated positively by most study patients. However, it is difficult to imagine how general practices could cope with monitoring patients with acute diseases without any further organizational support. Future research should address cost-effectiveness and changes in such clinical outcomes as hospitalization and mortality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".