SPI-Hub™: a gateway to scholarly publishing information

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.

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Spreadsheets as Knowledge Documents

Cases on Information Technology Series - Annals of Cases on Information Technology ◽

10.4018/978-1-59140-061-5.ch033 ◽

2011 ◽

pp. 521-537 ◽

Cited By ~ 1

Author(s):

Stephen Burgess ◽

Don Schauder

Keyword(s):

Knowledge Management ◽

Decision Support ◽

Small Business ◽

Information Management ◽

Explicit Knowledge ◽

Decision Support Tool ◽

Decision Support Tools ◽

Support Tool ◽

Applied Model ◽

Support Tools

How should a small business decide whether and in what ways to use Web technology for interactions with customers? This case describes the creation of a practical decision support tool (using a spreadsheet) for the initiation and development of small business Web sites. Decisions arise from both explicit and tacit knowledge. Using selected literature from a structuration theory, information management and knowledge management, decision support tools are characterized as knowledge documents (communication agents for explicit knowledge). Understanding decision support tools as knowledge documents sheds light on their potentialities and limitations for knowledge transfer, and assists in maximizing their potentialities. The case study deploys three levels of modeling: a high-level structuration model of the interplay between information management and knowledge management, a conceptual model of small-business decision-making, and an applied model the practical decision support tool, itself. An action-research methodology involving experts and stakeholders validated the development of conceptual categories and their instantiation in the practical tool.

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CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

10.1101/180109 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nicola Whiffin ◽

Roddy Walsh ◽

Risha Govind ◽

Matthew Edwards ◽

Mian Ahmad ◽

...

Keyword(s):

Decision Support ◽

Best Practice ◽

Decision Support Tool ◽

Specific Information ◽

Multiple Sources ◽

Support Tool ◽

User Input ◽

Best Practice Guidelines ◽

Interactive Interface ◽

Genome Interpretation

ABSTRACTPurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support varian interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fisher’s P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

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Utilization and uptake of the UpToDate clinical decision support tool at the Makerere University College of Health Sciences (MakCHS), Uganda

African Health Sciences ◽

10.4314/ahs.v21i2.52 ◽

2021 ◽

Vol 21 (2) ◽

pp. 904-911

Author(s):

Alison Annet Kinengyere ◽

Julie Rosenberg ◽

Olivia Pickard ◽

Moses Kamya

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Clinical Decision Support ◽

Point Of Care ◽

Decision Support Tool ◽

Clinical Decision ◽

Health Sciences ◽

Support Tool ◽

Makerere University ◽

Clinical Decision Support Tool

Background: The use of point-of-care, evidence-based tools is becoming increasingly popular. They can provide easy-to- use, high-quality information which is regularly updated and has been shown to improve clinical outcomes. Integrating such tools into clinical practice is an important component of improving the quality of health care. However, because such tools are rarely used in resource-limited settings, there is limited research on uptake especially among medical students. Objective: This paper explores the uptake of one such tool, Up-To-Date, when provided free of cost at a medical school in Africa. Methods: In partnership with the Better Evidence at Ariadne Labs free access to UpToDate was granted through the MakCHS IP address. On-site librarians facilitated training sessions and spread awareness of the tool. Usage data was aggre- gated, based on log ins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows evidence of meaningful usage, with 43,043 log ins and 15,591 registrations between August 2019 and August 2020. The most common topics viewed were in obstetrics and gynecology, pediatrics, drug information, and infectious diseases. Access occurred mainly through the mobile phone app. Conclusion: Findings show usage by various user categories, but with inconsistent uptake and low usage. Librarians can draw upon these results to encourage institutions to support uptake of point-of-care tools in clinical practice. Keywords: UpToDate clinical decision support tool; Makerere University College of Health Sciences; Uganda.

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