A framework for de-identification of free-text data in electronic medical records enabling secondary use
Louis Mercorelli A B , Harrison Nguyen C D , Nicole Gartell E , Martyn Brookes C , Jonathan Morris F and Charmaine S Tam
C D *
A Sydney Informatics Hub, University of Sydney, NSW, Australia.
B Clinical Informatics Unit, Northern Sydney Local Health District, NSW, Australia.
C Performance and Analytics, Northern Sydney Local Health District, NSW, Australia.
D Faculty of Medicine and Health, University of Sydney, Office 543, Level 5, School of Computer Science (J12), NSW 2006, Australia.
E Health Information Services, Northern Sydney Local Health District, NSW, Australia.
F Clinical Excellence Commission, NSW, Australia.
Australian Health Review 46(3) 289-293 https://doi.org/10.1071/AH21361
Submitted: 23 November 2021 Accepted: 18 March 2022 Published: 12 May 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of AHHA.
Abstract
Clinical free-text data represent a vast, untapped source of rich information. If more accessible for research it would supplement information captured in structured fields. Data need to be de-identified prior to being reused for research. However, a lack of transparency with existing de-identification software tools makes it difficult for data custodians to assess potential risks associated with the release of de-identified clinical free-text data. This case study describes the development of a framework for releasing de-identified clinical free-text data in two local health districts in NSW, Australia. A sample of clinical documents (n = 14 768 965), including progress notes, nursing and medical assessments and discharge summaries, were used for development. An algorithm was designed to identify and mask patient names without damaging data utility. For each note, the algorithm output the (i) note length before and after de-identification, (ii) the number of patient names and (iii) the number of common words. These outputs were used to iteratively refine the algorithm performance. This was followed by manual review of a random subset of records by a health information manager. Notes that were not correctly de-identified were fixed, and performance was reassessed until resolution. All notes in this sample were suitably de-identified using this method. Developing a transparent method for de-identifying clinical free-text data enables informed-decision making by data custodians and the safe re-use of clinical free-text data for research and public benefit.
Keywords: algorithm, de‐identification, documentation, e-health, EMR, governance, health services management, information management.
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