SPI-Hub™: a gateway to scholarly publishing information

Abstract

Background: Advances in the health sciences rely on sharing research and data through publication. As information professionals are often asked to contribute their knowledge to assist clinicians and researchers in selecting journals for publication, the authors recognized an opportunity to build a decision support tool, SPI-Hub: Scholarly Publishing Information Hub™, to capture the team’s collective publishing industry knowledge, while carefully retaining the quality of service.Case Presentation: SPI-Hub’s decision support functionality relies on a data framework that describes journal publication policies and practices through a newly designed metadata structure, the Knowledge Management Journal Record™. Metadata fields are populated through a semi-automated process that uses custom programming to access content from multiple sources. Each record includes 25 metadata fields representing best publishing practices. Currently, the database includes more than 24,000 health sciences journal records. To correctly capture the resources needed for both completion and future maintenance of the project, the team conducted an internal study to assess time requirements for completing records through different stages of automation.Conclusions: The journal decision support tool, SPI-Hub, provides an opportunity to assess publication practices by compiling data from a variety of sources in a single location. Automated and semi-automated approaches have effectively reduced the time needed for data collection. Through a comprehensive knowledge management framework and the incorporation of multiple quality points specific to each journal, SPI-Hub provides prospective users with both recommendations for publication and holistic assessment of the trustworthiness of journals in which to publish research and acquire trusted knowledge.

Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

“…It is also worth noting that in addition to the population-level analysis features offered by the Word Cloud as described in our research collaboration for PheWAS analysis, the CKM also uses its capability of providing summary views of individual patient charts for other projects, such as our synthesized evidence provision services [ 14 , 26 ]. In response to providers’ complex clinical questions, information scientists consult the visual display of the Word Cloud to gain a holistic understanding of each patient’s comorbidities, medications, and other prominent clinical history.…”

“…In response to providers’ complex clinical questions, information scientists consult the visual display of the Word Cloud to gain a holistic understanding of each patient’s comorbidities, medications, and other prominent clinical history. This greatly facilitates our ability to generate tailored syntheses of the published evidence that are personalized to each specific patient case [ 26 ]. Additional applications of the Word Cloud and other AI tools are also under exploration at our center, including the use of AI for scaling the maintenance of evidence syntheses over time [ 27 - 29 ].…”

“…This greatly facilitates our ability to generate tailored syntheses of the published evidence that are personalized to each specific patient case [ 26 ]. Additional applications of the Word Cloud and other AI tools are also under exploration at our center, including the use of AI for scaling the maintenance of evidence syntheses over time [ 27 - 29 ]. Through both of these approaches—leveraging the Word Cloud NLP for population-level concept analysis and individual patient-level assessment—the CKM achieves the rapid knowledge acquisition strategy critical for informing clinical health care and research at our institution.…”

Evaluating a Large Language Model’s Ability to Answer Clinicians’ Requests for Evidence Summaries

“…At the Center for Knowledge Management at Vanderbilt University Medical Center (VUMC), our team of medical librarians has, for over twenty years, provided evidence syntheses of the biomedical literature to respond to clinicians' complex questions arising from clinical encounters. These questions were gathered initially through rounding with clinical teams and, since 2004, via a message basket service linked within the electronic health record (EHR) to facilitate clinicians' ability to send requests at the time and place when they most need an answer [10][11][12][13][14]. A previous study found high levels of physician satisfaction with evidence summaries provided by our team [15].…”

Jot: guiding journal selection with suitability metrics

“…Journal selection is thus a difficult balancing act that precludes a one-size-fits-all ranking system. To support researchers, tools have been developed that match manuscripts with journals by manuscript title, abstract, and (in some cases) citations [6][7][8]. These tools vary in matching strategy, in the journal information they provide (such as publication speed, processing charges, and impact metrics), and in which journals are included (e.g., well-marketed tools developed by Elsevier, Springer, Wiley, and IEEE evaluate only the journals that they publish).…”