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PharmaVOICE Editors' Blog

Thursday, October 9, 2014

Key Takeaways from BigDiP (Big Data in Pharma)


Now that BigDip is over, it’s time to share some of the key insights shared during the conference. Some of the top thought leaders in the industry shared their insights with the attendees. If you attended, please add your takeaways in the comments below. If you were not able to attend, enjoy this recap and feel free to participate in the discussion as well.

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  • Review current publications concerning your products or drugs in development to understand how comparative groups are being assigned today.
  • List any gaps that may exist in current research that could be addressed via observational data research provided the right data elements are collected.
  • Consider the value of closing these gaps via a registry environment only if the right data are not collected at the right times in the right patients for the right products.
 -          Billy Franks - Astellas

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Complex Business Problems Drive Analytics Innovation.  Enable Disruptive Information by following these three principles:

  • Shared: The biggest difference in practical analytics may not be in how we analyze or interpret results but rather how that information is shared and consumed by all of us. 
  • Enabled:  Data/analytic portals and interactive analytic tools available to business leaders, analysts and scientists alike. 
  • Transparent: Information isn’t sequestered in static reports/memos and data isn’t hidden in disjointed data warehouses but rather is open and accessible to everyone.
 -          Christian Nimsch - SAS

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  • Get Parallel - The difference between a lot of data and big data is parallel processing. Via Netezza, sharded SQL, Hadoop, Apache Spark, etc. If you are not yet in an environment where you can do parallel processing, you will soon run out of capacity.
  • Get to the Decision - If you can apply your analytics where you make the most decisions, you will generate the most value.
 - Greg Hayworth, Humana

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  • In healthcare analytics a focus on causal conclusion is key.
  • Maximize the use of the longitudinal data structure healthcare records provide.
  • Three keys that will unlock the potential of healthcare database networks: size, depth, and speed.

 - Sebastian Schneeweiss – Harvard Medical School
  
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  • Evaluate how quickly you are getting answers to critical questions.
  • Look at the bottlenecks in data processing and ask how well your system can scale if data volumes go up by a factor of ten or 100.
  • Honestly assess what the total cost of your system and its upkeep are, and ask if it has become unwieldy.
 -          Brian Bradbury - Amgen

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  • Think of patient data as the new resource that will power drug discovery in the next decade. Are your systems ready?
  • Everyone should at least have pilots on how to manage large patient data. Genomic datasets are expected to grow beyond 100,000 patients, but current systems are not ready to handle even a few thousand patients.
  • Push the edge on data analytics and facilitate the combination of knowledge across disciplines (chemistry, biology, genetics, statistics etc.).
 -          Francesca Milletti - Roche

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  • No development of any RWE BIG DATA platform is complete without the ability to provide an end- to -end view of the patient journey.
  • Think about, conceptualize and address “confounding by indication” & “ selection bias” every time real world BIG Data platforms are being developed and analyzed.
  • BIG Data stacking in R&D spans different methodologies that need to be coordinated simultaneously for any reliable analytic exercise.
 -          Usman Iqbal - AstraZeneca

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  • Decide beforehand what are the questions that you plan to ask of the data. This will determine how you should store your data as the performance of the storage schema is impacted by the users queries.
  • Context is crucial - in many cases a single sensor may not suffice. Use multiple sensors to complete the picture
  • Data is dirty and outliers may skew your results. Ensure that you have a set of rules in place that will enable you to differentiate between "good" and "bad" data.
 -          Yadid Ayzenberg - MIT

* * * * * * * * * * * * * * *
  • To assure meaningful patient centered outcomes, plan for patient input through all stages of measures development, including concept elicitation, determination of item language and relevance, and testing.
  • Consider differences in patient preferences in the design and logistics of clinical trials based on condition, severity, demographics, and other patient level variables.
  • The potential utility of wearables is promising but requires a carefully considered engagement experience to enhance collection of meaningful data.
 -          Emil Chiauzzi – Patients Like Me

3 comments:

Anonymous said...

When info down to the cell level is available patient by patient, it will be possible to know why a drug failed ME. Do I have outside-normal-range genes, expression of those genes, anti-bodies, toxins, parasites, hormones, gut-bacteria ratios????

Once reasons are known why people react differently to the same drug.....solutions will appear if sufficient cash is available.

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