5335 days of Implementation Science: using natural language processing to examine publication trends and topics

Area of Science:

• Implementation Science
• Health Services Research
• Knowledge Translation

Background:

• The rapid growth of scientific publications challenges researchers' ability to stay current with evidence-based practices.
• Natural Language Processing (NLP) offers a potential solution for analyzing large volumes of academic literature.

Purpose of the Study:

• To apply NLP to identify publication trends, topic clusters, and temporal distributions within the journal Implementation Science.
• To demonstrate NLP's utility in research synthesis and translation.

Main Methods:

• A database of 1711 Implementation Science abstracts (2006-2020) was compiled using the PubMed API.
• Latent Dirichlet allocation (LDA) topic modeling was employed to cluster abstracts after standard preprocessing.

Main Results:

• Analyses identified 30 distinct topics, reflecting research characteristics and practice domains.
• HIV and stroke were the most frequent clinical areas. Systematic reviews increased in prominence, while knowledge translation articles declined after 2013.
• Articles on HIV and implementation effectiveness showed increased topic exclusivity over time.

Conclusions:

• NLP effectively synthesizes and identifies trends in large-scale academic literature, accelerating research dissemination.
• Future implementation science research should prioritize equity-focused studies to enhance impact on underserved communities.