Data  data  everywhere  Joe  Donahue  Senior  Vice  President  BioReference  Laboratories  

October  13,  2015    

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Preparing  for  this  panel…  

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Done!  

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Let  me  be  a  bit  more  thoughHul…  

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Let  me  be  a  bit  more  thoughHul…  

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I  learned  a  lot  about  Pharma…    

•  We’re  inefficient  (on  pre.y  much  everything)  

•  We’re  expensive  

•  Not  very  good  at  integra;ng  data  

•  Slow  to  adopt  new  technologies  

•  Stagnant  pipelines  

•  Etc…!  

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But  no-­‐one  said  we  don’t  have  data…!    

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But  no-­‐one  said  we  don’t  have  data…!    

We’ve  had  lots  of  data  for  a  long  Mme  

•  Combinatorial  Libraries  

•  HTS  

•  Genome  

•  Laboratory  notebooks  /  e-­‐notebooks  

•  Images  

•  Health  Records  (electronic  and  otherwise)  

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Why  all  the  commoMon  all  of  a  sudden?    

PaMent  Data  

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PaMent  Data  

•  It’s  helping  us  interpret  what  we  have  

•  Some  of  it  is  ‘dirty’  

•  There’s  a  lot  more  of  it  from  daily  life  –  sensors  

•  Lab  Data  (not  as  ‘dirty’)  

•  EHR  data  (‘standards’  challenging)  

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PaMent  Data  

•  It’s  helping  us  interpret  what  we  have  

•  Some  of  it  is  ‘dirty’  

•  There’s  a  lot  more  of  it  from  daily  life  –  sensors  

•  Lab  Data  (not  as  ‘dirty’)  

•  EHR  data  (‘standards’  challenging)  

•  What  are  we  allowed  to  REALLY  do  with  it  (‘Consents’)?  

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Decision  Support  

Knowledge  

Informa;on  

Data  

Pre-­‐Omics  TesMng  

Results\Data  

Informa;on  

Knowledge  

Wisdom\Management  Decisions  

Post-­‐Omics  TesMng  

Lab  

Provider  

Lab  

Provider,  Research  Hospital,  Pharma,  PaMents  

Wisdom\Management    and  Research  Decisions  

Laboratories  /  Lab  Data  taking  on  a  new  role  

New  Data  

•  Genomics  technologies  

•  Laboratory  Tes;ng  Data  

•  Imaging  

•  Healthcare  data  mining  

•  Safety  monitoring  from  social  media  

•  Anything  said  on  social  media  

•  Data  from  Pa;ent  Groups  /  Disease  Founda;ons  

•  Digital  medicine  

•  Wearable  devices  (daily  pa;ent  data)  

•  Microbiome  

•  No  longer  n-­‐of-­‐1  •  It’s  n-­‐of-­‐many  

•  DNA/RNA  technologies  and  omics  (in  general)  are  going  to  be  the  bulk  of  data  for  a  while  

•  Even  with  all  the  sensors,  those  ‘phenotypes’  s;ll  need  ‘genotypes’  

•  Machine  learning  and  experts  will  help  with  the  data  challenges  and  contribute  knowledge  that  helps  us  ‘connect  the  dots’  

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Discovery   I   II   III   Launch   MarkeMng  &  Sales    Life  Cycle  Management  

Phase  IV  

• 10  

• 100  

• 1000  

PaMents  (thousands)  

Pharma  currently  uses  very  li.le  RWPD  in  the  R&D  process.  What  they  do  use  is  

highly  controlled  

T  =  ~20  years  $1.4  –  2.6B  

Pharma-­‐owned  highly  controlled  clinical  trials  data  

 Real  World  PaMent  Data  (RWPD  /  RWE)  

Discovery   I   II   III   Launch   MarkeMng  &  Sales    Life  Cycle  Management  

Phase  IV  

• 10  

• 100  

• 1000  

PaMents  (thousands)  

Access  to  Real  World  Pa;ent  Data  can  help  make  be.er  decisions  throughout  the  Discovery,  Development  and  Launch  phases  of  bringing  a  medicine  to  market  

T  =  ~20  years  $1.4  –  2.6B  

Pharma-­‐owned  highly  controlled  clinical  trials  data  

Clinical  prac;ce,  pa;ents,  payers  and  providers  own  rights  to  the  data  

 Real  World  PaMent  Data  (RWPD  /  RWE)  

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Joe Donahue Senior Vice President jdonahue@bioreference.com +1-551-223-9716 (mobile)