Person:

Klann, Jeffrey

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

The Health Information Portability and Accountability Act (HIPAA) allows for the exchange of de-identified patient data, but its definition of de-identification is essentially open-ended, thus leaving the onus on dataset providers to ensure patient privacy. The Patient Centered Outcomes Research Network (PCORnet) builds a de-identification approach into queries, but we have noticed various subtle problems with this approach. We censor aggregate counts below a threshold (i.e. <11) to protect patient privacy. However, we have found that thresholded numbers can at times be inferred, and some key numbers are not thresholded at all. Furthermore, PCORnet’s approach of thresholding low counts introduces a selection bias which slants the data towards larger health care sites and their corresponding demographics. We propose a solution: instead of censoring low counts, introduce Gaussian noise to all aggregate counts. We describe this approach and the freely available tools we created for this purpose.