Evidence map›Paper›PMID 38756405›Full record

ArticleHealth affairs scholar2024

Challenges and dynamics of public health reporting and data exchange during COVID-19: insights from US hospitals.

John Xuefeng Jiang, Peter Cram, Kangkang Qi, Ge Bai

Abstract read
In one paragraph

Article in Health affairs scholar, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

John Xuefeng JiangEli Broad College of Business, Michigan State University, East Lansing, MI 48824, United States.ORCID https://orcid.org/0000-0001-6459-6411
Peter CramThe University of Texas Medical Branch School of Public and Population Health, Galveston, TX 77555, United States.ORCID https://orcid.org/0000-0002-1910-346X
Kangkang QiHarbert College of Business, Auburn University, Auburn, AL 36849, United States.ORCID https://orcid.org/0000-0001-8551-2758
Ge BaiJohns Hopkins Carey Business School, Baltimore, MD 21202, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

capacityCOVID-19public health reportingvaccine adverse effect

Identifiers

PMID38756405
PMCPMC10986213

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.