Knowledge graphs – the art of the possible 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 built from BMJ’s proprietary clinical decision support content, and it has been designed to overcome the limitations of traditional rule-based systems. BMJ Clinical Intelligence is an evidence-based and continuously updated content resource covering a range of medical specialties. Its structured representation of content 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. BMJ Group has a team of clinical experts and informatics specialists, who have been creating and updating clinical decision support content for over 15 years. This trusted, high quality content, now in knowledge graph form, provides a solution that spans clinical medicine and population health. The knowledge graph is updated daily, offering a dynamic, comprehensive, and evidence- based tool for healthcare professionals. BMJ Clinical Intelligence can be used by partners in their applications to support clinical decision making at the point of care. It can be used by clients in applications for population health analytics to identify care gaps, suggest interventions and facilitate early diagnosis. In population health, the graph can be used to support comprehensive monitoring, analysis, and early intervention, ensuring efficient use of resources. The knowledge graph’s advantages over traditional guidelines lie in its ability to represent an entire domain, providing a holistic view of patient care. Clients can use it to complement 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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