Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Medical Data Trustworthiness Assessment Model

A Medical Data Trustworthiness Assessment Model
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Author(s): Bandar Alhaqbani (Queensland University of Technology, Australia) and Colin Fidge (Queensland University of Technology, Australia)
Copyright: 2013
Pages: 21
Source title: User-Driven Healthcare: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2770-3.ch071


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Electronic Health Record systems are being introduced to overcome the limitations associated with paper-based and isolated Electronic Medical Record systems. This is accomplished by aggregating medical data and consolidating them in one digital repository. Though an EHR system provides obvious functional benefits, there is a growing concern about reliability trust (trustworthiness) of Electronic Health Records. Security requirements such as confidentiality, integrity, and availability can be satisfied by traditional data security mechanisms. However, measuring data trustworthiness is an issue that cannot be solved with traditional mechanisms, especially since degrees of trust change over time. In this chapter, a Medical Data Trustworthiness Assessment model to assist an EHR system to validate the trustworthiness of received/stored medical data based on who entered the data and when is presented. The MDTA model uses a statistical approach that depends on the observed experiences available to the EHR system. In order to provide an accurate trustworthiness estimate for historical medical data, a time scope around the time when the data was entered was used. This scope enables the model to capture the dynamic behavior of the data entry agent’s trustworthiness. To conduct this assessment medical metadata is used to extract information about the medical data sources (e.g. timestamps, and the identities of healthcare agents and medical practitioners) and, thereafter, this information is used in a statistical process to derive a trustworthiness value for the medical data. The result can then be expressed in the displayed health record by manipulating the EHR’s metadata to alert the medical practitioner to possible trustworthiness problems.

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