A certified de-identification system for all clinical text documents for information extraction at scale

• DOI: 10.1093/jamiaopen/ooad045
• Publication Date: 2023-07-05T13:15:50Z
• Authors: Lakshmi Radhakris...; Gundolf Schenk; Kathleen Muenzen; Boris Oskotsky; Habibeh Ashouri C...; Thomas Plunkett; Sharat Israni; Atul J Butte
• Keywords: clinical note text; de-identification; 610; Patient Safety; Philter; Research and Applications; unstructured data; 3. Good health

Abstract

Objectives Clinical notes are a veritable treasure trove of information on a patient’s disease progression, medical history, and treatment plans, yet are locked in secured databases accessible for research only after extensive ethics review. Removing personally identifying and protected health information (PII/PHI) from the records can reduce the need for additional Institutional Review Boards (IRB) reviews. In this project, our goals were to: (1) develop a robust and scalable clinical text de-identification pipeline that is compliant with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule for de-identification standards and (2) share routinely updated de-identified clinical notes with researchers.

Materials and Methods Building on our open-source de-identification software called Philter, we added features to: (1) make the algorithm and the de-identified data HIPAA compliant, which also implies type 2 error-free redaction, as certified via external audit; (2) reduce over-redaction errors; and (3) normalize and shift date PHI. We also established a streamlined de-identification pipeline using MongoDB to automatically extract clinical notes and provide truly de-identified notes to researchers with periodic monthly refreshes at our institution.

Results To the best of our knowledge, the Philter V1.0 pipeline is currently the first and only certified, de-identified redaction pipeline that makes clinical notes available to researchers for nonhuman subjects’ research, without further IRB approval needed. To date, we have made over 130 million certified de-identified clinical notes available to over 600 UCSF researchers. These notes were collected over the past 40 years, and represent data from 2757016 UCSF patients.