Ebook: Top 20 Big Data projects in Pharma & Biotech – Part 2
There are 5 main areas of Big Data investigation in real world outcomes by pharma today...
Remote, mobile health – The use of mobile devices such as smart phones to gather data on treatment adherence, on-going patient care, predictive symptoms or adverse events and health outcomesHealthcare provider data – Collecting data from electronic medical records that includes patient diagnosis, treatment, adherence and outcome.Connecting genomic analysis with patient outcomes and subpopulations in order to progress personalized medicineUtilizing data both from health records and pre-clinical studies to find new indications for therapeutic pipelinesBetter clinical decision making in order to improve patient outcomes with prescribed therapeutic
I often see articles that describe how big data won’t save pharma but smart analytics on data might. In the end Big Data is only as good as the questions that are being asked of it and essentially at present Big Data in healthcare is one big Petri dish. Certainly we will see a lot more business intelligence and analytics roles – both in pharma and providers – moving into big data applications and there will be huge demand for this talent in the years to come. This is the time to debate and discuss where the whole strategy of Big Data is going to take us and I hope this ebook inspires your team to invest further in the area.
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See how health care is a "late runner up":
Interesting viewpoints and promising initiatives..;-)
We can see how health care might have good examples in benchmarks from other businesses!
There are 5 main areas of Big Data investigation in real world outcomes by pharma today…
Remote, mobile health – The use of mobile devices such as smart phones to gather data on treatment adherence, on-going patient care, predictive symptoms or adverse events and health outcomesHealthcare provider data – Collecting data from electronic medical records that includes patient diagnosis, treatment, adherence and outcome.Connecting genomic analysis with patient outcomes and subpopulations in order to progress personalized medicineUtilizing data both from health records and pre-clinical studies to find new indications for therapeutic pipelinesBetter clinical decision making in order to improve patient outcomes with prescribed therapeutic