  Background: More than half (57%) of pharma clinical research spend is in
support of clinical trials. One reason is that Electronic Health Record (EHR)
systems and HIPAA privacy rules often limit how broadly patient information can
be shared, resulting in laborious human efforts to manually collect,
de-identify, and summarize patient information for use in clinical studies.
  Purpose: Conduct feasibility study for a Rheumatoid Arthritis (RA) clinical
trial in an Accountable Care Organization. Measure prevalence of RA and related
conditions matching study criteria. Evaluate automation of patient
de-identification and summarization to support patient cohort development for
clinical studies.
  Methods: Collect original clinical documentation directly from the provider
EHR system and extract clinical concepts necessary for matching study criteria.
Automatically de-identify Protected Health Information (PHI) protect patient
privacy and promote sharing. Leverage existing physician expert knowledge
sources to enable analysis of patient populations.
  Results: Prevalence of RA was four percent (4%) in the study population (mean
age 53 years, 52% female, 48% male). Clinical documentation for 3500 patient
were extracted from three (3) EHR systems. Grouped diagnosis codes revealed
high prevalence of diabetes and diseases of the circulatory system, as
expected. De-identification accurately removed 99% of PHI identifiers with 99%
sensitivity and 99% specificity.
  Conclusions: Results suggest the approach can improve automation and
accelerate planning and construction of new clinical studies in the ACO
setting. De-identification accuracy was better than previously approved
requirements defined by four (4) hospital Institutional Review Boards.
