Summary Modern healthcare delivery faces multiple challenges, including delayed translation of new knowledge into clinical practice, unwarranted variability in healthcare delivery at the patient and population level, and lack of optimization of cost, quality, and value. Traditional clinical decision support has attempted to tackle some of these problems. However, to date, it has largely been developed on rule-based systems. These have been helpful, but the rule base becomes difficult to manage at scale – for example, when you have to manage hundreds of rules. And typically rules take a single disease perspective, which will not work for patients with multimorbidity. An alternative is a knowledge graph approach. BMJ clinical intelligence is our knowledge graph, and it has been designed to overcome the limitations of traditional rule-based systems. BMJ clinical intelligence is an evidence-based and continuously updated resource covering a range of medical specialties. Its structured representation of knowledge allows for a comprehensive understanding of relationships between medical entities, fostering better clinical decision-making.
The BMJ clinical intelligence approach involves knowledge engineering at scale, ensuring the translation of clinical guidelines into computable evidence.
With a team of clinical experts and informatics specialists, BMJ clinical intelligence provides a solution that spans clinical medicine and population health. The knowledge graph is updated daily, offering a dynamic, comprehensive, and evidence-based content set that can be integrated into tools or workflows that healthcare professionals use. BMJ clinical intelligence can be utilized in clinical decision support at the point of care and in population health analysis. Its knowledge graph can be integrated into third party systems to help identify deviations in patient care, suggest interventions, and facilitate early diagnosis. In population health, the graph can similarly be integrated into systems to help enable comprehensive monitoring, analysis, and early intervention, ensuring efficient resource utilization. It can also be used in a range of other contexts, including digital quality measures, prior authorization, and supporting AI scribes. The knowledge graph’s advantages over traditional guidelines lie in its ability to represent an entire domain, providing a holistic view of patient care. It complements large language models, enhancing clinical reasoning and decision support. The continuous updating and scalability of knowledge graphs offer a dynamic and comprehensive approach to healthcare knowledge management and clinical care. BMJ clinical intelligence provides knowledge that is relevant to every decision in healthcare, and that will improve each decision – from the point of care to population health.
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Why the BMJ knowledge graph
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