Next Article in Journal
Feasibility Pump Algorithm for Sparse Representation under Gaussian Noise
Previous Article in Journal
Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems
Previous Article in Special Issue
Multidimensional Group Recommendations in the Health Domain
Article

The Need for Machine-Processable Agreements in Health Data Management

1
School of Electronics and Computer Science, University of Southampton, Southampton SO14 0AA, UK
2
Clinical Ethics and Law at Southampton, Centre for Cancer Immunology, University of Southampton, Southampton SO14 0AA, UK
*
Author to whom correspondence should be addressed.
This paper is an extended version of G.K.’s invited talk abstract published in the Proceedings of the Second International Workshop on Semantic Web Meets Health Data Management (SWH 2019), Auckland, New Zealand, 26 October 2019.
Algorithms 2020, 13(4), 87; https://doi.org/10.3390/a13040087
Received: 29 February 2020 / Revised: 1 April 2020 / Accepted: 4 April 2020 / Published: 7 April 2020
Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources. View Full-Text
Keywords: data sharing; consent; privacy policies; privacy languages; genomic data; genomic medicine; health data management data sharing; consent; privacy policies; privacy languages; genomic data; genomic medicine; health data management
Show Figures

Figure 1

MDPI and ACS Style

Konstantinidis, G.; Chapman, A.; Weal, M.J.; Alzubaidi, A.; Ballard, L.M.; Lucassen, A.M. The Need for Machine-Processable Agreements in Health Data Management. Algorithms 2020, 13, 87. https://doi.org/10.3390/a13040087

AMA Style

Konstantinidis G, Chapman A, Weal MJ, Alzubaidi A, Ballard LM, Lucassen AM. The Need for Machine-Processable Agreements in Health Data Management. Algorithms. 2020; 13(4):87. https://doi.org/10.3390/a13040087

Chicago/Turabian Style

Konstantinidis, George; Chapman, Adriane; Weal, Mark J.; Alzubaidi, Ahmed; Ballard, Lisa M.; Lucassen, Anneke M. 2020. "The Need for Machine-Processable Agreements in Health Data Management" Algorithms 13, no. 4: 87. https://doi.org/10.3390/a13040087

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop