Use of eHealth Platforms and Apps to Support Monitoring and Management of Home-Quarantined Patients With COVID-19 in the Province of Trento, Italy: App Development and Implementation
Bibliographic record
Abstract
BACKGROUND: Italy was the first country to largely experience the COVID-19 epidemic among other Western countries during the so-called first wave of the COVID-19 pandemic. Proper management of an increasing number of home-quarantined individuals created a significant challenge for health care authorities and professionals. This was especially true when considering the importance of remote surveillance to detect signs of disease progression and consequently regulate access to hospitals and intensive care units on a priority basis. OBJECTIVE: In this paper, we report on an initiative promoted to cope with the first wave of the COVID-19 epidemic in the Spring/Summer of 2020, in the Autonomous Province of Trento, Italy. A purposefully built app named TreCovid19 was designed to provide dedicated health care staff with a ready-to-use tool for remotely monitoring patients with progressive symptoms of COVID-19, who were home-quarantined during the first wave of the epidemic, and to focus on those patients who, based on their self-reported clinical data, required a quick response from health care professionals. METHODS: TreCovid19 was rapidly developed to facilitate the monitoring of a selected number of home-quarantined patients with COVID-19 during the very first epidemic wave. The app was built on top of an existing eHealth platform, already in use by the local health authority to provide home care, with the following functionalities: (1) to securely collect and link demographic and clinical information related to the patients and (2) to provide a two-way communication between a multidisciplinary health care team and home-quarantined patients. The system supported patients to self-assess their condition and update the multidisciplinary team on their health status. The system was used between March and June 2020 in the province of Trento. RESULTS: A dedicated multidisciplinary group of health care professionals adopted the platform over a period of approximately 3 months (from March-end to June 2020) to monitor a total of 170 patients with confirmed COVID-19 during home quarantine. All patients used the system until the end of the initiative. The TreCovid19 system has provided useful insights of possible viability and impact of a technological-organizational asset to manage a potentially critical workload for the health care staff involved in the periodic monitoring of a relevant number of quarantined patients, notwithstanding its limitations given the rapid implementation of the whole initiative. CONCLUSIONS: The technological and organizational model adopted in response to the COVID-19 pandemic was developed and finalized in a relatively short period during the initial few weeks of the epidemic. The system successfully supported the health care staff involved in the periodic monitoring of an increasing number of home-quarantined patients and provided valuable data in terms of disease surveillance.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".