Real world evidence for real life patients in a changing healthcare world

The randomizedclinical trial has long been the only source for gathering evidence about theeffectivity and safety of a new medicinal product to gain market access. In thechanging world of healthcare, where all new information about patients isflowing to the healthcare provider, the patient and Internet, patient orproduct-related information can now be integrated and used for obtaining proofof clinical effectiveness and patient benefit. New technologies for analysis,machine and automated learning are being integrated as well in the healthcareworld. Both structured and unstructured information and data coming fromscientific literature, patient social media, smart devices or electronic healthrecords, among others, are all relevant and knowledge about the patient outcomecan be extracted with advanced analysis methods.  Automated learning techniques allow forbuilding decision-support machines that can determine whether a patient shouldbe dismissed from hospital, whether they are taking their drug on time when athome or if they should be contacted by their physician as their disease isworsening.  This shift from productdelivery to patient outcomes in healthcare comes at a time when the analyticaltechnology, hardware and data standardization (e.g. OMOP, IDMP, CDISC) isallowing for massively parallel analysis of data. Understanding the patienthealth situation by analyzing their data fingerprint requires however anunderstanding of personalized medicine, respect for data privacy and anunderstanding of how the algorithms work. The gap between research and clinicalpractice remains wide but is narrowing – analytics, and the ability to make itusable by physicians, can further close that gap without breaking the systemeconomically or practically.