Doctor-patient partnership has been advocated for achieving effective diabetes management. Such a partnership requires empowering patients with proper knowledge and skills so they are capable of participating in decision-making, effective communication with health professionals and successful diabetes self-management. Although it is well known that patient education should be tailored and prioritised, little research on Computer-based Patient Education Systems (CPESs) has aimed at customising information that is flexible enough to adapt to firstly, the dynamic nature of patients' ongoing information needs and secondly, changes in their personal health and social circumstances. Moreover, except for indirect support for doctor-patient communication through tailored information, current CPESs do not aid patients in formulating their questions. / This thesis targets limitations of current CPESs and explores approaches for using information technology (IT) to support the doctor-patient partnership. Two approaches used to achieve the research goal are 1) providing essential information to individual patients - information that is not only relevant, but also prioritised; and 2) providing direct support for patients to generate personalised agendas prior to scheduled health visits. The innovative technologies that have been developed for implementing these two approaches include a comprehensive Diabetes Information Profile (DIP) for each patient, information tailoring and prioritisation algorithms (information algorithms), quiz tailoring and prioritisation algorithms (quiz algorithms), and agenda personalisation algorithms (which serve to populate an agenda question pool). The DIP includes data elements on a patient's lifestyle, diet profile, psychosocial profile, risk factors of diabetic complications, behaviour change profile, self-management profile, and clinical status. The information algorithms take into account these DIP elements, as well as patients' diabetes knowledge level (based on educational exposure) and individual information preferences. Collectively, the implementation of these approaches, using an extensible architecture based on Extensible Mark-up Language (XML) and Java technologies, is called “Violet Technology” (VT). A VT-based web portal has been developed and evaluated. / A two-phase evaluation was conducted through the Diabetes Centre of a metropolitan hospital. The first study evaluates the validity of the information algorithms through patient and healthcare provider assessment of prioritised information topics. The participants of the first study include 11 patients with diabetes, one General Practician (GP), one endocrinologist, two diabetes nurse educators and one dietician. The second trial evaluates the VT-based portal overall - including information, quiz and agenda personalisation algorithms - through a field trial of the portal with random selection of patients to treatment and control groups. In total, 27 patients, one GP, one endocrinologist, two nurse educators, and one dietician were involved in the second trial. The evaluations provide qualitative support for the relevance of information prioritisation by VT, and show acceptable consumer usability, as well as healthcare provider support, for the portal. The evaluations also revealed further incremental refinements to the information algorithms. / This thesis contributes a specific framework for the use of IT to support the doctor-patient partnership through prioritised information and integrated agenda formulation services. While a larger scale of evaluation is needed to establish patient health benefits, the results of the two initial studies are encouraging. This framework could be adapted for other chronic diseases, such as depression or asthma. It could also be used for other purposes, such as an intelligent information searching facility. A future VT framework should provide more explicit representation of patients' emotional supports and have further mechanisms for promoting patient behaviour changes. / Thesis (PhDInformationTechnology)--University of South Australia, 2005.
| Identifer | oai:union.ndltd.org:ADTP/267387 |
| Creators | Ma, Chunlan. |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | copyright under review |
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