The first Note posted was about risk predictive factors, ranging from traditional factors, such as family history or race and ethnicity (which are “phenotype”) patient factors to novel genome and biomarker (“genotype”) factors. Quture’s method of establishing a library of traditional and novel risk factors relies upon a systematic medical literature review, incidence and prevalence analysis of claims databases, and proof of concept analytics in de-identified patient databases.
To understand where Quture is today and how we got to our QualOptima experiential learning platform, think back to 2010 – 2012 and our formal clinical trial at the University of Miami, Miller School of Medicine and Jackson Memorial Hospital. With the leadership of Quture’s Associate Medical Director, Keith A. Candiotti, M.D., we first introduced our risk stratified analytics system to measure clinical performance and outcomes (see Success Stories: University of Miami Clinical Trial). One of the primary risk factors for post-operative nausea and vomiting (PONV) is gender-specific – being a female (a phenotype risk factor). Our data analytics can be used not only to measure performance and outcomes, but as an experiential learning platform to learn from clinical patient-specific experience. For example, the different reactions of men and women to the antiemetic drugs, from different pharmaceutical companies, administered in different combinations, sequencing, and dosages, can be studied to understand how to achieve optimal clinical performance and outcomes. And in our study, we even began with the female gender being more at risk than males.