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Title: A process driven quality assessment model for electronic healthcare records
Author: Addico, Henry
ISNI:       0000 0004 2743 3084
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2011
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Electronic Healthcare Records (EHRs) are valuable resources, shared across all three subdomains of healthcare: research, policy and practice. The content of these EHRs need to be fit for making critical decisions required for effective and high quality clinical care. The management of the fitness for the purposes of EHR is a well known problem referred to as 'the data quality problem'. within the health care domain, Information Quality (IQ) forms an indirect relation with the quality of service or has a direct impact on decision making. One of the approaches to the management of this problem and its adverse effect on clinical care is through continuous assessment, monitoring and review of its fitness for required purposes. A key challenge to the management of the IQ problem in the health care setting, is having to deal with both objective and subjective determinants of quality in a uniform way on large amount of heterogeneous data and information with complex interdependencies. Whilst the objective determinants like accuracy, completeness, etc. has been well formulated, the subjective determinants like accessibility, confidentiality, privacy, etc. have not been logically formulated. The work presented in this thesis form a step towards a unified logical way to the assessment of the fitness for purpose of data and information from the healthcare context for the activities performed as part of the work flow followed by clinicians during patient care. This thesis makes two main contribution to the assessment of the data and information quality problem for EHRS. First, a model named LOgical Quality (LOQ), which models IQ assessment using fuzzy set and fuzzy logic and thus enables a logical formulation and quantification of both objective and subjective quality. The other contribution, a framework called Process-centric framework for IQ (PROF), builds on the logical model to create a process centric framework using clinical pathways as the source for deriving and generating disease specific IQ rules for the assessment. The clinical pathways, which are disease centric, are also used to determine the evaluation order of the derived quality rules. Given an appropriate domain knowledge representation of the care context and workflow, the two contributions form a road map towards the development of automated online IQ assessment techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available