Supporting clinical decision making: Clinicians rely ever more on support in diagnosis
With over 7,000 rare diseases, of which approximately 500 are treatable, it is impossible for an individual clinician to know how to diagnose every rare disease. Disease progression in chronic conditions can generate a plethora of potential treatments as symptoms evolve, and clinicians may find it difficult to choose the appropriate treatment at a given time.
New biomarkers that help clinicians better understand the phenotypic presentation of a patient and that can help with diagnosis are very important to clinicians.
inTrigue provides not only highly predictive models for disease diagnosis at scale in electronic health record systems, but also interpretable models that make clinical sense, which help the clinician to understand better how the diagnosis model is working and therefore help with diagnosis.
inVerse provides phenotype-based tools to help clinicians choose the best treatments for their patients.
inCognita can allow remote monitoring of consenting patients, providing real time insights into patient health.
Building artificial intelligence systems is useful in its own right, but significant value is created only when they become a natural part of supporting the clinical decision making process.
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