Digital Solutions to Alleviate the Burden on Health Systems During a Public Health Care Crisis: COVID-19 as an Opportunity
Bibliographic record
Abstract
The COVID-19 pandemic has generated unprecedented and sustained health management challenges worldwide. Health care systems continue to struggle to support the needs of the majority of infected individuals that are either asymptomatic or have mild symptoms. In addition, long-term effects in the form of long-lasting COVID-19 symptoms or widespread mental health issues aggravated by the pandemic pose a burden on health care systems worldwide. This viewpoint article considers aspects of digital health care solutions and how they can play an ongoing role in safely addressing gaps in the health care support available from initially and repeatedly overwhelmed providers and systems. Digital solutions can be readily designed to address this need and can be flexible enough to adapt to the evolving management requirements of various stakeholders to reduce COVID-19 infection rates, acute hospitalizations, and mortality. Multiplatform solutions provide a hybrid model of care, which can include mobile and online platforms accompanied by direct clinician input and feedback. Desirable components to be included are discussed, including symptom tracking, patient education, well-being support, and bidirectional communication between patients and clinicians. Customizable and scalable digital health platforms not only can be readily adapted to further meet the needs of employers and public health stakeholders during the ongoing pandemic, but also hold relevance for flexibly meeting broader care management needs into the future.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".