title: Identifying and collecting pertinent medical records for centralized abstraction in a multi-center randomized clinical trial: the model used by the American College of Radiology arm of the National Lung Screening Trial.
abstract: BACKGROUND
  In clinical trials and epidemiologic studies, information on medical care utilization and health outcomes is often obtained from medical records. For multi-center studies, this information may be gathered by personnel at individual sites or by staff at a central coordinating center. We describe the process used to develop a HIPAA-compliant centralized process to collect medical record information for a large multi-center cancer screening trial.
  
  
  METHODS
  The framework used to select, request, and track medical records incorporated a participant questionnaire with unique identifiers for each medical provider. De-identified information from the questionnaires was sent to the coordinating center indexed by these identifiers. The central coordinating center selected specific medical providers for abstraction and notified sites using these identifiers. The site personnel then linked the identifiers with medical provider information. Staff at the sites collected medical records and provided them for central abstraction.
  
  
  RESULTS
  Medical records were successfully obtained and abstracted to ascertain information on outcomes and health care utilization in a study with over 18,000 study participants. Collection of records required for outcomes related to positive screening examinations and lung cancer diagnosis exceeded 90%. Collection of records for all aims was 87.32%.
  
  
  CONCLUSIONS
  We designed a successful centralized medical record abstraction process that may be generalized to other research settings, including observational studies. The coordinating center received no identifying data. The process satisfied requirements imposed by the Health Insurance Portability and Accountability Act and concerns of site institutional review boards with respect to protected health information.
authors.affiliation: Center for Statistical Sciences, Brown University School of Medicine, Providence, RI 02912, USA. igareen@stat.brown.edu
authors.forename: Ilana F
authors.initials: IF
authors.lastname: Gareen
authors.forename: JoRean D
authors.initials: JD
authors.lastname: Sicks
authors.forename: Amanda Adams
authors.initials: AA
authors.lastname: Jain
authors.forename: Denise
authors.initials: D
authors.lastname: Moline
authors.forename: Nancy
authors.initials: N
authors.lastname: Coffman-Kadish
mesh_terms: D016494:Computer Security; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis* / Q000453:epidemiology; D008403:Mass Screening / Q000379:methods*; D008499:Medical Records; D015337:Multicenter Studies as Topic / Q000379:methods*; D011871:Radiology; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D014481:United States