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Computable biomedical knowledge (CBK) is: "the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit, and therefore can be represented and reasned upon using logic, formal standards, and mathematical approaches." Representing biomedical knowledge in a machine-interpretable, computable form increases its ability to be discovered, accessed, understood, and deployed. Computable knowledge artifacts can greatly advance the potential for implementation, reproducibility, or extension of the knowledge by users, who may include practitioners, researchers, and learners. Enriching computable knowledge artifacts may help facilitate reuse and translation into practice. Following the examples of 10 Simple Rules papers for scientific code, software, and applications, we present 10 Simple Rules intended to make shared computable knowledge artifacts more useful and reusable. These rules are mainly for researchers and their teams who have decided that sharing their computable knowledge is important, who wish to go beyond simply describing results, algorithms, or models via traditional publication pathways, and who want to both make their research findings more accessible, and to help others use their computable knowledge. These rules are roughly organized into 3 categories: planning, engineering, and documentation. Finally, while many of the following examples are of computable knowledge in biomedical domains, these rules are generalizable to computable knowledge in any research domain. Author summary: Computable biomedical knowledge, including algorithms, models, and evidence, is already impacting—and will likely transform—health research and healthcare delivery. Representing this knowledge in formats that can be discovered and processed by computers increases its potential for use at scale. Similarly, making these algorithms, models, etc. (aka computable knowledge artifacts) understandable and reusable by others can help facilitate reuse and translation into practice. However, as with other types of scientific software or research data, creating artifacts that are usable by and useful to others requires intentionality, and often additional time and effort. We've developed over 100 computable knowledge artifacts in diverse areas of health research and healthcare and we've learned a few things along the way. We've distilled these lessons learned into 10 Simple Rules to help researchers who would like to make their computational research products available and useful to others. Our work centers on knowledge in the biomedical domain, but we believe these 10 Simple Rules are generalizable and valuable as design principles for any researcher wishing to share computational research products. [ABSTRACT FROM AUTHOR]

The four major NIH-supported career development K awards are the K01, K08, K23, and K99, which comprise 70% of the investment allocated to the 15 K-type career awards. One measure [...]

We present our work to develop digital objects to represent and convey a specific category of scientific knowledge: computable biomedical knowledge (CBK). Properly developed, validated, implemented and stewarded, CBK has the potential to accelerate the translation of actionable knowledge from scientific discovery to clinical application. Our research takes an infrastructural approach to CBK, initially by focusing on the creation of a conceptual model for packaging computable biomedical knowledge - the Knowledge Object (KO) - and on corresponding efforts to create an architecture for KO management and implementation. Additionally, our work is grounded in the FAIR principles, such that KO artefacts should be findable, accessible, interoperable and reusable and we are exploring aligning KOs with emerging best practices for FAIR Digital Objects (FDO). The outcomes of this work resonate in clinical contexts, health professions education, healthcare quality improvement, biomedical and translational research and population care. Our KO model is also of interest to researchers and practitioners interested in knowledge science, including those working with semantic technologies and other forms of digital objects. [ABSTRACT FROM AUTHOR]

"Research Data Sharing: A Maturity Model for Organizational Capacity" can also serve as a tool to guide a gap analysis, helping organizations identify specific resource investments and interventions to increase their data sharing performance. Rule 1: Use a maturity model to understand organizational qualities that impact data sharing Organizations need to understand their baseline capabilities for data sharing, and a maturity model can facilitate this assessment. We consider an institution's organizational culture about data sharing to encompass how leaders and researchers generally interpret data sharing, how its reward systems express these attitudes, and how it treats decisions for engaging with new data sharing opportunities and best practices. [Extracted from the article]

Introduction: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. Methods: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories. Results: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. Conclusion: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders. [ABSTRACT FROM AUTHOR]

Objective: To offer practical, evidence-informed knowledge on clinical decision support systems (CDSSs) and their utility in improving care and reducing costs in otolaryngology–head and neck surgery. This primer on CDSSs introduces clinicians to both the capabilities and the limitations of this technology, reviews the literature on current state, and seeks to spur further progress in this area. Data Sources: PubMed/MEDLINE, Embase, and Web of Science. Review Methods: Scoping review of CDSS literature applicable to otolaryngology clinical practice. Investigators identified articles that incorporated knowledge-based computerized CDSSs to aid clinicians in decision making and workflow. Data extraction included level of evidence, Osheroff classification of CDSS intervention type, otolaryngology subspecialty or domain, and impact on provider performance or patient outcomes. Conclusions: Of 3191 studies retrieved, 11 articles met formal inclusion criteria. CDSS interventions included guideline or protocols support (n = 8), forms and templates (n = 5), data presentation aids (n = 2), and reactive alerts, reference information, or order sets (all n = 1); 4 studies had multiple interventions. CDSS studies demonstrated effectiveness across diverse domains, including antibiotic stewardship, cancer survivorship, guideline adherence, data capture, cost reduction, and workflow. Implementing CDSSs often involved collaboration with health information technologists. Implications for Practice: While the published literature on CDSSs in otolaryngology is finite, CDSS interventions are proliferating in clinical practice, with roles in preventing medical errors, streamlining workflows, and improving adherence to best practices for head and neck disorders. Clinicians may collaborate with information technologists and health systems scientists to develop, implement, and investigate the impact of CDSSs in otolaryngology. [ABSTRACT FROM AUTHOR]

It is not known how often lean tools and implementation determinants frameworks or checklists are used concurrently in health care quality improvement activities. The authors systematically reviewed the literature for studies that used a lean tool along with an implementation science determinants framework (January 1999 through August 2018). Seven studies (8 publications) were identified, inclusive of 2 protocols and 6 research articles across multiple continents. All included studies used the consolidated framework for implementation research as their implementation science determinants framework. Lean tools included in more than 1 publication were process mapping (4 publications), process redesign (3 publications), and 5S standardization (2 publications). Only 1 study proposed using a lean tool concurrently with an implementation science determinants framework in the design and execution of the QI project. Few published studies utilize both an implementation science determinants framework or checklist and 1 or more lean tool in their study design. [ABSTRACT FROM AUTHOR]