Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action
Bibliographic record
Abstract
BACKGROUND: As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis. AIM: In this paper, we describe a joint call for action to use and leverage evidence-based health informatics as the foundation for the COVID-19 response and public health interventions. Tangible examples are provided for how the working groups and special interest groups of the International Medical Informatics Association (IMIA) are helping to build an evidence-based response to this crisis. METHODS: Leaders of working and special interest groups of the IMIA, a total of 26 groups, were contacted via e-mail to provide a summary of the scientific-based efforts taken to combat COVID-19 pandemic and participate in the discussion toward the creation of this manuscript. A total of 13 groups participated in this manuscript. RESULTS: Various efforts were exerted by members of IMIA including (1) developing evidence-based guidelines for the design and deployment of digital health solutions during COVID-19; (2) surveying clinical informaticians internationally about key digital solutions deployed to combat COVID-19 and the challenges faced when implementing and using them; and (3) offering necessary resources for clinicians about the use of digital tools in clinical practice, education, and research during COVID-19. DISCUSSION: Rigor and evidence need to be taken into consideration when designing, implementing, and using digital tools to combat COVID-19 to avoid delays and unforeseen negative consequences. It is paramount to employ a multidisciplinary approach for the development and implementation of digital health tools that have been rapidly deployed in response to the pandemic bearing in mind human factors, ethics, data privacy, and the diversity of context at the local, national, and international levels. The training and capacity building of front-line workers is crucial and must be linked to a clear strategy for evaluation of ongoing experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".