Evidence-based medicine emphasizes the “conscientious, explicit, and judicious use of current best evidence”1 when making treatment decisions.
Randomized controlled trials (RCTs) are considered the highest-quality source of evidence about treatment efficacy and safety.
Evidence derived from RCTs, however, often does not generalize to the majority of patients, who tend to have multiple comorbidities, take many medications, and differ from individuals enrolled in RCTs on many characteristics, resulting in an inferential gap between the evidence that is available and that which is needed.
Therefore, it is necessary to transform the evidence-generation process and to incorporate the use of aggregate patient data at the point of care in order to create a successful learning health system.
Today — with the wide adoption of EMRs, the increasing ease of use of advanced statistical methods, and the ability to compute with large patient cohorts — a core tenet of the learning health system has been enabled: deriving on-demand evidence for diverse clinical scenarios from the EMR.
Stanford is leading the charge. The process is labour intensive and cluncky but I am sure some start up genius will apply AI to this process and bring the search time and accuracy down to a realistic number of seconds.
Stay tuned.
Raymond Rupert patient advocate and health system disruptor.