How might BMJ Clinical Intelligence be used? BMJ Clinical Intelligence can be used in multiple contexts. It can be used in clinical decision support at the point of care via web services or as a SMART on FHIR application for individual patients. It can also be used as a knowledge resource in population health to address the care of cohorts of patients. At the point of care, the graph can be queried to look for deviations from a patient’s expected course – for a care gap or untoward trend. And in turn, once it detects a deviation, it can suggest an early intervention to rectify the patient’s care. It can help in cases of diagnostic error or diagnostic delay. Patients might have a delayed diagnosis which can be impactful on their clinical course. The knowledge graph can be used to help accelerate the differential diagnosis reasoning process to augment the clinical reasoning of the physician and so avoid error or delay. BMJ Clinical Intelligence can be used in population health analyses as well. For example, it could be used on a whole population and then a cohort of that population who have a given condition such as diabetes and finally a section of that cohort with diabetes who are not proceeding as expected in their care journey – they might exhibit care gaps or untoward trends. For example, the knowledge graph might provide the clinical context and prioritization of patients identified as frequent flyers or high utilizers of emergency care services. Healthcare providers may then call in such patients preemptively to keep them on track and to prevent them from becoming acutely unwell. Thus, limited care resources are directed to where they are most needed – resulting in accurate patient risk stratification and more cost-effective resource allocation. Today knowledge representation at scale is best done as a knowledge graph. This is true of other industries, not just healthcare. Knowledge graphs are supporting drug discovery in the pharmaceutical industry, fraud detection in the banking industry, and even space exploration at NASA. In this case, our knowledge graph is a set of diseases and findings that are all interrelated to each other to give a complete picture of a domain of practice. Knowledge graphs allow us to do decision support in novel ways. They allow us not only to do individual condition-specific decision support for a particular disease, they also allow us to look at a disease and find all of its neighbors and comorbidities to which the disease might be connected, and to find out how all of those relate to the patient’s overall state. In this way, we can augment clinical reasoning in a fashion that is not possible with rule-based approaches for single conditions.
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Why the BMJ Knowledge Graph
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