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.

Why equating all evidence searches to systematic reviews defies their role in information seeking.

All too often the quality and rigor of topic investigations is inaccurately conveyed to information professionals, resulting in a mischaracterization of the research, which, if left unchecked and published, may in turn mislead potential readers. Accurately understanding and categorizing the types of topic investigation searches that are requested of information professionals is critical to both meeting requestors' needs and reflecting their intended methodological approaches. Information professionals' expertise can be an invaluable resource to guide users through the investigative and publication process.

Collection of social determinants of health in the community clinic setting: a cross-sectional study.

BACKGROUND: Addressing social and behavioral determinants of health (SBDs) may help improve health outcomes of community clinic patients. This cross-sectional study explored how assessing SBDs can be used to complement health data collection strategies and provide clinicians with a more in-depth understanding of their patients. METHODS: Adult patients, ages 18 and older, at an urban community health care clinic in Tennessee, U.S.A., were asked to complete a questionnaire regarding health status, health history and SBDs while waiting for their clinic appointment. The SBD component included items from the National Academy of Medicine, the Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences instrument, and the Survey of Household Economics and Decisionmaking. Data collection and analysis occurred in 2017. RESULTS: One hundred participants completed the study. The questionnaire took approximately 11 min to complete, and the response rate was 90% or higher for all items except annual household income (unanswered by 40 participants). The median number of negative SBDs was 4 (IQR 2.75-7.0), 96 participants had at least one unmet need, and the most common negative SBD was physical activity (75%; 75/100). CONCLUSIONS: The hybrid questionnaire provided insight into a community clinic population's SBDs and allowed for a more complete understanding than a single questionnaire alone. The brief questionnaire administration time and low non-response rate support the questionnaire's feasibility in the community clinic setting, and results can be used by clinicians to further the personalization goals of precision medicine. Next steps include evaluating how to connect patients with appropriate resources for addressing their SBDs.

Guiding Oncology Patients Through the Maze of Precision Medicine.

As the role of genomics in health care grows, patients increasingly require adequate genetic literacy to fully engage in their care. This study investigated a model for delivering consumer-friendly genetic information to improve understanding of precision medicine using health literacy and learning style principles. My Cancer Genome (MCG), a freely available cancer decision support tool, was used as a testbed. MCG content on a melanoma tumor mutation, BRAF V600E, was translated to a 6th-grade reading level, incorporating multiple learning modalities. A total of 90 patients and caregivers were recruited from a melanoma clinic at an academic medical center and randomized to 3 groups. Group A (control) received an exact copy of text from MCG. Group B was given the same content with hyperlinks to videos explaining key genetic concepts, identified and labeled by the team as knowledge pearls. Group C received the translated content with the knowledge pearls embedded. Changes in knowledge were measured through pre and post questionnaires. Group C showed the greatest improvement in knowledge. The study results demonstrate that providing information based on health literacy and learning style principles can improve patients' understanding of genetic concepts, thus increasing their likelihood of taking an active role in any decision making concerning their health.

Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition.

Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.

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

Objective: This study investigated the performance of a generative artificial intelligence (AI) tool using GPT-4 in answering clinical questions in comparison with medical librarians' gold-standard evidence syntheses. Methods: Questions were extracted from an in-house database of clinical evidence requests previously answered by medical librarians. Questions with multiple parts were subdivided into individual topics. A standardized prompt was developed using the COSTAR framework. Librarians submitted each question into aiChat, an internally-managed chat tool using GPT-4, and recorded the responses. The summaries generated by aiChat were evaluated on whether they contained the critical elements used in the established gold-standard summary of the librarian. A subset of questions was randomly selected for verification of references provided by aiChat. Results: Of the 216 evaluated questions, aiChat's response was assessed as "correct" for 180 (83.3%) questions, "partially correct" for 35 (16.2%) questions, and "incorrect" for 1 (0.5%) question. No significant differences were observed in question ratings by question category (p=0.39). For a subset of 30% (n=66) of questions, 162 references were provided in the aiChat summaries, and 60 (37%) were confirmed as nonfabricated. Conclusions: Overall, the performance of a generative AI tool was promising. However, many included references could not be independently verified, and attempts were not made to assess whether any additional concepts introduced by aiChat were factually accurate. Thus, we envision this being the first of a series of investigations designed to further our understanding of how current and future versions of generative AI can be used and integrated into medical librarians' workflow.

Using SPI-Hub™ to Promote the Key Role of Prepublishing in Healthcare.

With the need to quickly advance knowledge dissemination in rapid-paced fields, and more recently in response to the urgency of the COVID-19 pandemic, prepublishing has been brought to the forefront. SPI-Hub™, a publicly available journal selection decision support tool, is being strategically enhanced to address prospective authors' critical needs in navigating and selecting the most appropriate preprint or traditional publication venue.

Systematic review of international studies evaluating MDRD and CKD-EPI estimated glomerular filtration rate (eGFR) equations in Black adults.

Use of race adjustment in estimating glomerular filtration rate (eGFR) has been challenged given concerns that it may negatively impact the clinical care of Black patients, as it results in Black patients being systematically assigned higher eGFR values than non-Black patients. We conducted a systematic review to assess how well eGFR, with and without race adjustment, estimates measured GFR (mGFR) in Black adults globally. A search across multiple databases for articles published from 1999 to May 2021 that compared eGFR to mGFR and reported outcomes by Black race was performed. We included studies that assessed eGFR using the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPICr) creatinine equations. Risk of study bias and applicability were assessed with the QUality Assessment of Diagnostic Accuracy Studies-2. Of 13,167 citations identified, 12 met the data synthesis criteria (unique patient cohorts in which eGFR was compared to mGFR with and without race adjustment). The studies included patients with and without kidney disease from Africa (n = 6), the United States (n = 3), Europe (n = 2), and Brazil (n = 1). Of 11 CKD-EPI equation studies, all assessed bias, 8 assessed accuracy, 6 assessed precision, and 5 assessed correlation/concordance. Of 7 MDRD equation studies, all assessed bias, 6 assessed accuracy, 5 assessed precision, and 3 assessed correlation/concordance. The majority of studies found that removal of race adjustment improved bias, accuracy, and precision of eGFR equations for Black adults. Risk of study bias was often unclear, but applicability concerns were low. Our systematic review supports the need for future studies to be conducted in diverse populations to assess the possibility of alternative approaches for estimating GFR. This study additionally provides systematic-level evidence for the American Society of Nephrology-National Kidney Foundation Task Force efforts to pursue other options for GFR estimation.

A narrative review of the impact of the transition to ICD-10 and ICD-10-CM/PCS.

OBJECTIVES: The United States transitioned to the tenth version of the International Classification of Diseases (ICD) system (ICD-10) for mortality coding in 1999 and to the International Classification of Diseases, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS) on October 1, 2015. The purpose of this study was to conduct a narrative literature review to better understand the impact of the implementation of ICD-10/ICD-10-CM/PCS. MATERIALS AND METHODS: We searched English-language articles in PubMed, Web of Science, and Business Source Complete and reviewed websites of relevant professional associations, government agencies, research groups, and ICD-10 news aggregators to identify literature on the impact of the ICD-10/ICD-10-CM/PCS transition. We used Google to search for additional gray literature and used handsearching of the references of the most on-target articles to help ensure comprehensiveness. RESULTS: Impact areas reported in the literature include: productivity and staffing, costs, reimbursement, coding accuracy, mapping between ICD versions, morbidity and mortality surveillance, and patient care. With the exception of morbidity and mortality surveillance, quantitative studies describing the actual impact of the ICD-10/ICD-10-CM/PCS implementation were limited and much of the literature was based on the ICD-10-CM/PCS transition rather than the earlier conversion to ICD-10 for mortality coding. DISCUSSION: This study revealed several gaps in the literature that limit the ability to draw reliable conclusions about the overall impact, positive or negative, of moving to ICD-10/ICD-10-CM/PCS in the United States. CONCLUSION: These knowledge gaps present an opportunity for future research and knowledge sharing and will be important to consider when planning for ICD-11.