Search

Over the past 4 years, the authors have participated as members of the Mobilizing Computable Biomedical Knowledge Technical Infrastructure working group and focused on conceptualizing the infrastructure required to use computable biomedical knowledge. Here, we summarize our thoughts and lay the foundation for future work in the development of CBK infrastructure, including: explaining the difference between computable knowledge and data, and contextualizing the conversation with the Learning Health Systems and the FAIR principles. Specifically, we provide three guiding principles to advance the development of CBK infrastructure: (a) Promote interoperable systems for data and knowledge to be findable, accessible, interoperable, and reusable. (b) Enable stable, trustworthy knowledge representations that are human and machine readable. (c) Computable knowledge resources should, when possible, be open. Standards supporting computable knowledge infrastructures must be open. [ABSTRACT FROM AUTHOR]

Clinical trials generate key evidence to inform decision making, and also benefit participants directly. However, clinical trials frequently fail, often struggle to enroll participants, and are expensive. Part of the problem with trial conduct may be the disconnected nature of clinical trials, preventing rapid data sharing, generation of insights and targeted improvement interventions, and identification of knowledge gaps. In other areas of healthcare, a learning health system (LHS) has been proposed as a model to facilitate continuous learning and improvement. We propose that an LHS approach could greatly benefit clinical trials, allowing for continuous improvements to trial conduct and efficiency. A robust trial data sharing system, continuous analysis of trial enrollment and other success metrics, and development of targeted trial improvement interventions are potentially key components of a Trials LHS reflecting the learning cycle and allowing for continuous trial improvement. Through the development and use of a Trials LHS, clinical trials could be treated as a system, producing benefits to patients, advancing care, and decreasing costs for stakeholders. [ABSTRACT FROM AUTHOR]

The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways1. There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms, and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community was formed in 2016 to address these needs. This report summarizes the main outputs of the third annual MCBK public meeting, which was held virtually from June 30 to July 1, 2020 and brought together over 200 participants from various domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18‐19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]

Preference cards, the nexus of an efficient OR, help ensure that OR personnel have what they need to safely and efficiently care for surgical patients. In June 2013, a new electronic health record (EHR) was implemented in the ORs of the Duke University Health System, Durham, North Carolina. Although it was an integrated system, the EHR disrupted supply management in the OR, which affected the availability of correct resources. The project team performed a process-improvement project using root cause analysis (RCA) to identify the factors that led to the disruption in the preference card management process for orthopedic surgical procedures. Team members identified that a chief cause of resource disruption was the mistiming of interface messages that created the pick lists and requisition forms. Based on the results of the RCA, the project team developed and implemented recommendations for process improvement. [ABSTRACT FROM AUTHOR]