Melillo, Paolo (2011) Design and assessment of disease management program for cardiac patients via enhanced telemedicine with data-mining and pattern recognition. [Tesi di dottorato] (Unpublished)
Visibile a [TBR] Repository staff only fino a 23 January 2015.
|Item Type:||Tesi di dottorato|
|Uncontrolled Keywords:||meta-analysis, evaluation, disease management program, telemedicine, heart failure, data-mining|
|Date Deposited:||12 Dec 2011 10:26|
|Last Modified:||30 Apr 2014 19:47|
Cardiovascular diseases and in particular Chronic Heart Failure (CHF) represent one of the challenge to be faced by National Health System, because of their mortality and morbidity, related expenditure and huge impact on health-related quality of life. Limited health funding and rapidly expanding population of older patients with CHF are leading many National Health Services (NHSs) to search for new programs of care, which allows providing high-quality care in settings alternative to hospitals ones. Home-monitoring (HM) and Disease Management Programs (DMP) were widely explored in the last years because, compared to usual care, they can provide specialized care to a larger number of patients with a limited access to healthcare services. Moreover, both HM and DMP are effective alternatives to UC for management of CHF, reducing mortality and readmission rate. A wide literature investigates their effects on clinical outcomes of patients suffering from CHF. In almost the totality of study, those models have been compared to UC, and there is not sufficient literature comparing directly HM and DMP. This comparison is needed since some National Health Services (NHSs), as the Italian one, are promoting the adoption of HM as an alternatives to UC, while preliminary studies proved that, although HM could be slightly more effective than DMP, HM is up to five times more costly than DMP, and therefore less cost-effective. Therefore, the first aim of the current thesis is to provide a systematic comparison of efficacy of HM benchmarked with DMP, which is reported in the chapter 1. Moreover, I contribute to the project and design of a telemedicine service enhanced with data-mining and pattern recognition. In the chapter 2, the design of the telemedicine platform is described and the details about the data-mining application for Heart Failure detection and assessment are reported. In the chapter 3, the results relative to a data-warehouse for hypertensive patients is reported. Finally, the chapter 4 presents the results of the research for prediction of long-term survival in the elderly.
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