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Tracking Scientific Publications in Non-traditional Academic Medical Centers.
1Medical Sciences, Hackensack Meridian School of Medicine, Nutley, USA.
Cureus
|March 20, 2026
Summary
Tracking publications from human subject research is difficult for academic centers. A semi-automated method using principal investigator surveys found a 30.5% publication rate, highlighting the need for better tools.
Area of Science:
- Healthcare Administration
- Research Management
- Publication Metrics
Background:
- Accurate tracking of academic publications is vital for institutional success but remains challenging, especially within large healthcare networks.
- A standardized solution for tracking publications linked to human subject research is currently lacking.
- Non-traditional academic centers face particular difficulties in managing and reporting research output.
Purpose of the Study:
- To develop and evaluate a home-grown, semi-automated approach for tracking publications.
- To calculate the publication rate of studies approved by the institutional review board (IRB) at a large hospital network.
- To assess the feasibility and limitations of using principal investigator (PI) surveys for publication tracking.
Main Methods:
- A semi-automated system was implemented at a large U.S. hospital network.
- Principal investigators (PIs) of studies approved by the institutional review board (IRB) between 2021-2023 were surveyed via REDCap.
- Data collected included full-text articles, abstracts, posters, and conference presentations.
Main Results:
- The study identified a publication rate of 30.5% for IRB-approved studies.
- The semi-automated approach, while facilitating data collection, proved to be time- and resource-intensive.
- Reliance on PI self-reporting may have impacted the accuracy of the calculated publication rate.
Conclusions:
- Tracking publications in complex healthcare settings is challenging.
- Home-grown solutions can be adapted, but require further automation for wider adoption by non-traditional academic centers.
- There is a significant need for adaptable and accessible publication tracking tools, with future potential in AI and machine learning applications.
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