Hedaquin: A Reputation-based Health Data Quality Indicator

Abstract

A number of applications based on personal health records (PHRs) are emerging in the field of health care and wellness. PHRs empower patients by giving them control over their health data. Health data for PHRs can be supplied by patients, wellness providers and health care providers. Health care providers may use the PHRs to provide medical care. Unfortunately, the quality of the health data cannot be guaranteed in all cases (e.g. consider cases where non-professionals such as patients and wellness providers supplied the data).To address this problem, we present in this paper Hedaquin, a system that provides health care professionals with an indication of the quality of health data in a PHR. This indication is based on the reputation of the supplier and on metadata provided by measurement devices. The proposed reputation system mimics the way trust in health data and their suppliers is built in the real world. Hedaquin uses the Beta reputation system as a starting point and extends it in several directions to cover specific PHR requirements. Firstly, Hedaquin supports the automatic calculation of a rating based on a repeated measurement. Secondly, certificates for the user such as diplomas are taken into account. Thirdly, Hedaquin calculates reputation for different scopes in order to discriminate among different tasks the suppliers of health data can perform. Finally, the time difference between the ratings and the calculation of the reputation influences the weight that is given to a rating.

TrustyFeer: A Subjective Logic Trust Model for Smart City Peer‐to‐Peer Federated Clouds

“…On the one hand, trust measures have values that are either discrete or continuous. Metrics (mathematical model), on the other hand, can be based on various approaches: simple summation, average of ratings [14], flow-based models [15,16], fuzzy logic [17][18][19][20][21], probabilistic models such as Bayesian systems [22][23][24] or beta probability density [25,26], or a subjective logic approach [27,28].…”

Impact of ICT on Home Healthcare

“…Next to ensuring proper patient/device authentication, data authenticity and integrity, it is important to capture the correctness of the authentication process too. An overall solution that can capture all these aspects is the application of reputation systems, where providers build a level of trust in the patient based on his ability to take measurements [18]. Reputation systems have been studied in the literature for different domains, such as auction websites and peer-to-peer sharing networks [19].…”

“…Conversely, very few studies address patients' trustworthiness from the perspective of healthcare providers and in particular the reliability of measurements taken by patients. Existing proposals [18,22] mainly focus on the reliability of the data maintained in the form of electronic and personal health records.…”

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“…Trust can be measured using discrete or continuous values. Metrics can be based on a simple summation or an average of ratings [22], fuzzy logic [1,7,24,25,37], flow-based models [2,6,13,14,29], probabilistic models such as Bayesian systems [31,32,34,36], or beta probability density [17,33] and subjective Logic [9,12,35].…”