Mark Ansermino

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  Augmented Clinical Decisions
  Mark Ansermino

  Addressing the pressing global health challenges will require new and innovative approaches to healthcare delivery. The widespread availability of low cost but powerful digital technology has provided opportunities to collect detailed information from front-line health workers everywhere. Clinical decision making can be supported and enhanced with the timely and evidence based treatment and referral recommendations. The complexity of these decisions that have traditionally required extensive training and experience can now be reliably undertaken by healthcare workers in the community with minimal training or experience but supported by powerful data driven prediction models. The goal of these prediction models is to significantly change the trajectory of the typical disease process. Early identification of the patient at risk can change healthcare behavior, allow for timely administration of treatment before significant deterioration in health and allow for early referral when a higher level of care is required. The development of these personalized prediction models is a rapidly evolving science that will leverage the era of big data to make a big impact on health outcomes globally.