Tracking Scientific Publications in Non-traditional Academic Medical Centers

TL;DR

This paper describes a method for tracking academic publications in non-traditional medical centers, highlighting challenges and potential improvements using automation.

Contribution

A semi-automated, home-grown method for tracking publication rates in a large healthcare network is introduced.

Findings

A 30.5% publication rate was identified for IRB-approved studies between 2021 and 2023.

The method involved surveying principal investigators via REDCap to collect publication data.

Self-reporting and resource intensity were limitations, but automation could improve accuracy.

Abstract

Tracking academic publications is inherently challenging for both academic and non-traditional academic centers. Publications are considered crucial for academic success; however, accurately identifying and reporting them is a resource-intensive process. There is a lack of a standardized, readily available solution that focuses on tracking publications linked to human subject research within a large healthcare network. This report presents a home-grown approach to tracking and reporting publications. The aim was to track publications and calculate the publication rate of studies approved by the institutional review board (IRB) between 2021 and 2023 at a large United States hospital network by surveying principal investigators (PI) via REDCap about resulting publications. This semi-automated approach revealed a 30.5% publication rate with publications including full-text articles, abstracts, posters, and conference presentations. Tracking publications within a complex healthcare network is challenging. While the described method facilitated data collection, it was time- and resource-intensive, and reliance on PI self-reporting could have limited the accuracy of the publication rate calculated. However, with further automation, home-grown approaches such as the one presented here could allow for a user-friendly application that other non-traditional academic medical centers can adopt. A widely acceptable and easily adaptable tool for publication tracking is needed, particularly for non-traditional academic centers. Future research should explore AI and machine learning applications to address this need.

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