Journal Article

Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records

Broadbent, Matthew TM, Fernandes, Andrea C, Chang, Chin-Kuo, Hayes, Richard D, Cloete, Danielle, Tsang, Jason, Soncul, Murat, Liebscher, Jennifer, Jackson, Richard G, Callard, Felicity, Roberts, Angus, Stewart, Robert

2013

Overview

Background Electronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research. Methods We describe development, implementation and evaluation of a bespoke de-identification algorithm used to create the register. It is designed to create dictionaries using patient identifiers (PIs) entered into dedicated source fields and then identify, match and mask them (with ZZZZZ) when they appear in medical texts. We deemed this approach would be effective, given high coverage of PI in the dedicated fields and the effectiveness of the masking combined with elements of a security model. We conducted two separate performance tests i) to test performance of the algorithm in masking individual true PIs entered in dedicated fields and then found in text (using 500 patient notes) and ii) to compare the performance of the CRIS pattern matching algorithm with a machine learning algorithm, called the MITRE Identification Scrubber Toolkit – MIST (using 70 patient notes – 50 notes to train, 20 notes to test on). We also report any incidences of potential breaches , defined by occurrences of 3 or more true or apparent PIs in the same patient’s notes (and in an additional set of longitudinal notes for 50 patients); and we consider the possibility of inferring information despite de-identification. Results True PIs were masked with 98.8% precision and 97.6% recall. As anticipated, potential PIs did appear, owing to misspellings entered within the EHRs. We found one potential breach. In a separate performance test, with a different set of notes, CRIS yielded 100% precision and 88.5% recall, while MIST yielded a 95.1% and 78.1%, respectively. We discuss how we overcome the realistic possibility – albeit of low probability – of potential breaches through implementation of the security model. Conclusion CRIS is a de-identified psychiatric database sourced from EHRs, which protects patient anonymity and maximises data available for research. CRIS demonstrates the advantage of combining an effective de-identification algorithm with a carefully designed security model. The paper advances much needed discussion of EHR de-identification – particularly in relation to criteria to assess de-identification, and considering the contexts of de-identified research databases when assessing the risk of breaches of confidential patient information.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V
• Subject: Air pollution control equipment / Algorithms / Alzheimer's disease / Computer Security / Confidentiality / Consent / Consulting services / Data mining / Electronic Data Processing - standards / Electronic Health Records / Electronic records / Health care industry / Health Informatics / Health services / Health Services Research / Humans / Information Systems and Communication Service / London / Machine learning / Management of Computing and Information Systems / Medical records / Medical research / Medicine / Medicine & Public Health / Mental disorders / Mental health care / Mental Health Services - organization & administration / Mental Health Services - standards / Patient education / Patients / Program Development / Registries / Reproducibility of Results / Research Article / Safety and security measures / Systems Integration