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    Trial io pcori doc v1 Trial io pcori doc v1 Document Transcript

    • 4/15/13  TrialIO:  A  Empowering  Investigators  and  Patients  with  Better  Information  Executive  Summary  TrialIO  addresses  the  patient  researcher-­‐matching  problem  by  addressing  the  needs  of  the  researchers,  patient  advocates,  and  caregivers  during  the  trial  planning  process.  Trials  that  are  conducted  with  the  “right  investigator,  at  the  right  location,  at  the  right  time”  have  a  better  chance  of  getting  funded,  fulfilling  recruitment  goals  and  improving  confidence  in  the  study  outcome.    Patients  and  researchers  seeking  to  find  each  other  would  be  empowered  with  better  information  to  start  their  process.  The  ClinicalTrials.gov  web  site  and  derivative  search  engines  excel  at  finding  individual  trial  records,  but  provide  little  support  for  a  time-­‐based  or  “trended”  views  of  clinical  trial  activity  for  a  given  disease,  investigator,  sponsor,  or  geographic  location.    TrialIO  re-­‐imagines  the  ClinicalTrials.gov  data  as  a  vast  spreadsheet  in  the  cloud.  Using  a  web  browser  or  mobile  device:     n Patient  advocates  can  quickly  identify  geographies  that  are  under-­‐ represented  by  clinical  trial  activity  for  a  condition.     n Patients  seeking  investigators  can  build  lists  of  candidate  investigators  for   pitching  their  trial  idea.   n Investigators  seeking  funding  can  see  the  entire  portfolio  of  activity  for  a   sponsor  or  possible  collaborator  trending  over  time.     n Trial  planners  can  see  the  recruitment  history  for  a  condition  over  all   locations.  And,  quickly  see  the  likelihood  that  a  planned  trial  will  face   competition  for  patients  at  a  given  location.     n Sponsors  can  identify  the  best  investigators  based  on  prior  trial  activity.   n The  benefits  of  easy  access  to  aggregate  trial  activity  extend  to  world  health   organizations,  governments,  medical  societies,  disease  foundations,   academia  and  industry.    TrialIO  is  envisioned  as  both  a  web  application  and  a  syndicated  web  service  for  developers.  For  end  users,  anyone  with  access  to  an  Internet  connection  can  access  the  site,  generate  reports  and  share  insights  with  colleagues.  Clinical  trial  matching  is  networking  and  better  information  shared  will  promote  communication  and  dissemination  of  information.      Developers  can  syndicate  the  TrialIO  web  service  to  create  new  applications  using  clinical  trial  data.  By  providing  these  data  services  the  cost  of  application  1   Copyright  -­‐  Incite  Advisors,  Inc.  2013    
    • 4/15/13  development  is  lowered  increasing  availability  of  information  services  for  caregivers  operating  in  lower  income  areas.    Background  The  idea  for  TrialIO  grew  out  of  a  consulting  project  with  a  hospital  organization  in  the  Boston  area.  The  client  was  interested  in  expanding  its  collaborative  activity  in  the  field  of  genomics.  This  led  me  to  two  questions:  1)  who  are  the  potential  collaborators  who  would  be  most  interested  in  collaborations  in  genomics?  And,  2)  how  active  are  the  peer  hospitals  in  the  field?  The  ClinicalTrials.gov  web  site  was  a  natural  place  to  look.  I  found  the  data  there  structured  nicely  for  a  computer  programmer  but  too  voluminous  and  not  easily  fitting  into  the  form  I  wanted  it:  a  spreadsheet.      The  project  was  also  inspired  by  the  Clinical  Trials  Transformation  Initiative  Aggregate  Analysis  of  Clinical  Trials  project  sponsored  by  the  Duke  School  of  Medicine.  Notably  Duke  makes  the  data  available  on  the  ClinicalTrials.gov  web  site.  However,  the  IT  required  downloading,  hosting,  and  maintaining  that  data  is  significant.    A  number  of  commercial  firms  exist,  mainly  to  supply  clinical  trial  business  intelligence  and  analytics  to  pharmaceutical  and  biotech  executives.  IMS  Health  provides  Site  Optimizer.  Citeline  provides  TrialTrove  and  SiteTrove  products.  A  number  of  market  research  providers  offer  reports  on  clinical  trial  pipeline  activity  for  upwards  of  $2,500  per  condition.  The  presence  of  these  commercial  offerings  validates  the  value  proposition  of  TrialIO.  However,  their  business  models  are  prohibitive  for  many  academic  and  non-­‐profit  entities.  Thus,  TrialIO  has  the  potential  to  serve  a  real  market  need  and  is  potentially  disruptive  to  these  businesses.  PCORI  Considerations    Technical  Feasibility,  Usability,  and  Scalability  The  TrialIO  architecture  is  a  proof-­‐point  for  the  application  of  “big  data”  programming  and  database  technologies  in  healthcare.  The  system  uses  the  Apache  open  source  database  CouchDB  and  the  data  is  indexed  using  the  “map-­‐reduce”  paradigm.  The  presentation  of  this  proof-­‐of-­‐concept  implementation  validates  these  technical  choices.  Cloudant,  a  data-­‐as-­‐a-­‐service  company  provides  the  servers  and  storage  hosting  the  project.  Without  these  tools  the  functionality  would  have  been  challenging  to  achieve  and  the  programming  cost  and  IT  infrastructure  needed  would  have  made  the  project  prohibitive.    A  majority  of  the  effort  focused  on  the  development  of  the  indices  and  algorithms  for  managing  complex  queries  and  the  “pivot”  function.  The  map-­‐reduce  computing  paradigm  assures  that  most  of  the  heavy  computation  of  indices  occurs  on  the  2   Copyright  -­‐  Incite  Advisors,  Inc.  2013    
    • 4/15/13  server  during  off-­‐peak  times,  so  there  are  no  scalability  issues  there.  Currently  the  “pivot”  algorithm  runs  in  the  client.  This  method  can  be  computational  so  we  plan  to  move  this  processing  to  the  server  on  the  next  revision  of  the  software.  The  client  maintains  a  cache  of  trial  records  when  bulk  loading  data  from  the  server  to  keep  the  screen  active  without  having  data  from  the  server  over  run  the  client.  Scalability  is  further  insured  by  enforcing  a  ‘date-­‐range’  on  all  queries.  By  placing  limits  on  the  time  range,  we  limit  the  number  of  trial  records  the  system  has  to  process  at  once.  Currently  these  limits  are  1,  2,  and  5-­‐year  windows.      We  anticipate  the  need  for  mobile  access  the  web  user  interface  is  created  using  the  responsive  web  design  techniques.  We  are  not  skilled  designers;  we  are  data  architects  so  the  application  will  need  a  user  interface  design  makeover  before  going  into  production.  We  tried  to  minimize  options  and  extra  features  to  keep  users  focused  on  the  spirit  of  the  application.    To  get  the  trial  documents  into  the  system  require  significant  data  cleansing  operations.  One  example  is  a  system  of  classifying  trial  conditions  into  one  of  24  NLM  Mesh  Terms  was  devised  so  that  trial  activities  can  be  grouped  into  “categories”.    Differences  in  the  ways  patients,  caregivers,  and  researchers  interact  The  research  community  will  find  the  spreadsheet  paradigm  the  most  relevant  and  comfortable.  Though,  the  application  requires  no  knowledge  of  Excel,  pivot  tables,  and  the  like.  The  application  can  be  made  more  approachable  to  patients  by  for  example  changing  references  to  conditions  from  “neoplasms”  to  “cancer”  wherever  possible.      A  key  future  requirement  of  TrialIO  is  to  help  caregivers  directly  match  patients  to  trials.  Physicians  treating  patients  who  are  candidates  for  clinical  trials  are  unable  to  spend  time  parsing  updates  to  clinical  trials  to  recommend  to  their  patients.  As  a  result,  many  physicians  don’t  refer  their  patients  to  trials  because  they  don’t  know  about  them1.  With  an  interface  to  the  EHR,  this  process  can  be  automated  and  recommendation  alerts  forwarded  to  physicians  in  a  convenient  manner.      Maximizing  Patient-­‐Centeredness  and  Scientific  Rigor  The  application  anticipates  that  users  will  make  interesting  discoveries  in  the  data  and  want  to  share  their  findings.  To  support  this,  users  will  be  able  to  cut-­‐paste  simple  URL  into  their  email  or  social  media  (Facebook,  Twitter)  accounts.  The  volume  of  discussion  about  clinical  trial  activities  should  increase.    Our  scientific  rigor  is  computer  science.  Before  going  into  production,  the  system  will  need  extensive  testing  and  validation  of  results  against  some  hand  calculations                                                                                                                  1  The  Project  IMPACT  Experience  To  Date:  Increasing  Minority  Participation  and  Awareness  of  Clinical  Trials  3   Copyright  -­‐  Incite  Advisors,  Inc.  2013    
    • 4/15/13  to  verify  the  results.  We  version  our  software  and  develop  tests  to  confirm  that  software  quality  is  maintained  as  new  features  are  added  to  the  system.        Serving  “hard  to  reach”  audiences  Our  model  for  extending  our  reach  is  to  syndicate  our  feeds.  The  proliferation  of  clinical  trial  searching  sites  on  the  Web  is  evidence  of  the  demand  for  this  type  of  information.  By  syndicating  our  data  feeds  we  can  lower  the  cost  of  software  development  so  that  the  barriers  to  better  information  are  lowered  for  organizations  serving  hard  to  reach  audiences.    Usage    The  prototype  version  of  TrialIO  launches  to  a  dashboard  of  aggregated  clinical  trial  counts  grouped  by  Disease.    Dashboard    Figure  1  -­‐  Sample  Dashboard  The  three  main  navigation  options  are  Dashboard,  Explore,  and  Share.  Users  can  navigate  from  the  Dashboard  to  begin  their  exploration  of  the  data,  or  move  to  the  Explore  menu.  Explore  Most  users  will  move  straight  to  EXPLORE  where  they  can  select  from  a  menu  of  pre-­‐computed  indexes  such  as  Disease,  Sponsor,  or  Location.  It  will  be  possible  to  expand  this  list  to  include  combinations  of  indexes  such  as  Sponsors-­‐Collaborators  to  allow  comparative  analysis.  Also,  it  will  be  possible  to  index  complex  data  types  in  the  clinical  trials  archive  such  as  Study  Design.  The  functionality  will  benefit  from  a  deeper  understanding  of  the  research  investigators  use  case.  See  figure  2  below.    4   Copyright  -­‐  Incite  Advisors,  Inc.  2013    
    • 4/15/13  Share  (not  yet  implemented)  The  sharing  paradigm  returns  a  URL  for  each  report  or  graph  generated.  Users  can  book  mark  and  share  these.        Figure  2  -­‐  EXPLORE  output  for  "Conditions".  Users  can  choose  a n  interesting  condition  and  see  an    aggregation  of  "locations"  for  that  disease.    About  Incite  Advisors,  Inc.  Incite  Advisors,  Inc.  is  a  consulting  business  focused  on  data  driven  web  applications  for  healthcare  and  life  sciences.  We  offer  strategy  consulting  and  web  data  services.  We  serve  life  science  vendors,  pharmaceutical  and  biotech  enterprises,  and  healthcare  provider  institutions  worldwide.  Our  offices  are  in  Worcester,  Massachusetts.    Contact  Information:  Incite  Advisors,  Inc.  19  Goddard  Drive  Auburn,  MA  01501  www.inciteadvisors.com  Ph.:  (508)  254-­‐8349    5   Copyright  -­‐  Incite  Advisors,  Inc.  2013