SHARING PATHOLOGY DATA SETS

Tentative outline for ASCP teleconference, fall, 2004

Jules J. Berman, Ph.D., M.D.
Program Director, Pathology Informatics
Cancer Diagnosis Program, DCTD, NCI, NIH
EPN - Room 6028
6130 Executive Blvd.
Rockville, MD  20892

• Value of data sharing [to pathologists]
  • Discovery tool
  • New hypotheses about disease
  • Instant linkage to archived tissue blocks for research
  • Epidemiology device
  • Disease surveillance
  • BioTerror surveillance
  • Test Development
  • Merging and analyzing large pathology datasets may prove to be faster and less expensive than traditional clinical trials
  • Internal QA and enhanced institutional performance
    • External Inducements to sharing data
    • NIH data sharing policy
    • Institute of Medicine data sharing policy
    • The only path to evidence-based medical practice is by making the evidence available to the public
    • Source of institutional revenue
    • Corporate partnerships
    • NIH funding
    • Cooperating with progressive service payers/users (e.g. Leapfroggroup, patient advocacy groups)
  • Impediments to sharing data
    • Medicolegal
    • HIPAA
    • Common Rule
    • Tort/Patient animosity (absolutely the most importantconsideration)
    • Proper perspective
      • Problem not as big as it seems
      • There are a variety of freely available technicalsolutions (provided as supplementary material)
  • Data issues
    • What is pathology data, and how big is it?
    • Problems with free-text data
    • Problems with structured data
    • Solution?
      • XML can provide a standard structure to free-text data
      • Example of structured free-text in XML
      • Standards and specification permit data exchange
      • Movement toward self-describing documents
  • Case Study: Shared Pathology Informatics Network
    • Peer-to-Peer Network funded by National Cancer Institute to query multiple pathology departments (including the Harvard-affiliated hospitals, the UCLA-affiliates, U of Pittsburgh and the U of Indiana affiliates)
    • Network must deal with technical issues as well as social and legal challenges
    • Technical and legal problems pale in comparison to social issues
    • Current status of the Shared Pathology Informatics Network
      • Autocoding
      • De-identification
      • P2P
      • Standards for data representation (XML specifications)
      • IRB hurdles