Challenges and dynamics of public health reporting and data exchange during COVID-19: insights from US hospitals
Bibliographic record
Abstract
The US health care response during the early stages of the COVID-19 pandemic unveiled challenges in public health reporting systems and electronic clinical data exchange. Using data from the 2020 and 2022 American Hospital Association information technology supplement surveys, this study examined US hospitals' experiences in public health reporting, accessing clinical data from external providers for COVID-19 patient care, and their success in reporting vaccine-related adverse events to relevant state and federal agencies. Results showcase significant disparities in reporting practices across government levels due to inconsistent requirements. Although many hospitals leaned toward automated data transmission, a substantial portion continued to depend on manual processes. Pertaining to electronic clinical data, while entities like large commercial laboratories outperformed others, a considerable number were sluggish in delivering critical information. Moreover, a small percentage of hospitals reported challenges in recording vaccine-related adverse events, emphasizing the need for transparent reporting systems. The study underscores the necessity for standardized reporting protocols, explicit directives, and a pivot from manual to automated processes. Tackling these challenges is pivotal for ensuring prompt and reliable data, bolstering future public health responses, and rejuvenating public trust in health institutions.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".