Enhanced privacy governance in Health Information Systems through business process modelling and HL7

Rahmouni, Hanene Boussi; Essefi, Intidhar; Ladeb, Mohammed Fethi

Abstract

© 2019 The Authors. Published by Elsevier B.V. Medical data privacy is nowadays an alarming issue thanks to the technological revolution witnessed in the medical field and the ease of data access and exchange leveraged by newly implemented Hospital Information Systems (HIS). In order to help protect patient data while offering them the required medical procedures, many computerized techniques could be made available to be implemented in HIS since an early stage of their design. Those techniques should be applied throughout the rolling of clinical pathways to preserve medical data privacy and security in order to enhance privacy governance within Hospitals. When considered as processes, and because of their complexity and multidisciplinary nature, clinical pathways should be modelled in a simple way paying attention to medical tasks and the underlining shared clinical data. It is important to highlight the data with higher protection and sensitivity level. These data characteristics will influence many governance and security decisions of each process. This work aims to present a methodology to model clinical pathway specifications for data driven clinical processes, distinguishing sensitive data from other data and identifying personal data protection principles and the Protected Health Information (PHI). In this context, we precise for each clinical task potentially involving data processing and sharing, the level of protection the data requires through the use of privacy tags and labels added to data elements predefined using the HL7 standard. This method of tagging would help mapping extracted data, classified into categories, to a set of privacy requirements as needed by the HIPAA legislation. Hence data protection and privacy governance are leveraged in a seamless and highly transparent way. The use of HL7 allowed better data discovery and parsing which facilitates the definition of medical data protection measures at a later stage.

• Journal Article Type: Article
• Conference Name: Procedia Computer Science
• Acceptance Date: Jan 1, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Nov 10, 2020
• Publicly Available Date: Nov 11, 2020
• Journal: Procedia Computer Science
• Print ISSN: 1877-0509
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 164
• Pages: 706-713
• DOI: https://doi.org/10.1016/j.procs.2019.12.239
• Keywords: Business process; clinical care pathway; data protection; electronic exchanges; HL7; HIPAA legislation; patient privacy; privacy governance; privacy requirements; threats
• Public URL: https://uwe-repository.worktribe.com/output/6844276