Scientific objectives This study explores ways in which the requirements and interrelationships between Personalized Medicine (PM), clinical medical practice, and basic medical research could be best served by information and communication technologies (ICT). To avoid the problems inherent in formulating ICT solutions in isolation, a use-case was developed employing hepatocellular carcinoma (HCC). The subject matter was approached from four separate, but interrelated, tasks: (1) review of current understanding and clinical practices relating to HCC; (2) propose an ICT system for dealing with the vast amount of information relating to HCC, including clinical decision support and research needs; (3) determine the ways in which a clinical liver cancer center can contribute to this ICT approach; and, (4) examine the enhancements and impact that the first three tasks, and therefore Personalized Medicine, will have on the management of HCC. The development of an IT System for Personalized Healthcare (ITS-PHC) for HCC will provide a comprehensive system to identify and then determine the relative value of the wide number of variables or Information Entities (IEs): (1) factors reflecting clinical assessment of the patient including functional status, liver function, degree of cirrhosis, and comorbidities; (2) factors reflecting tumor biology, at a molecular, genetic and anatomic level; (3) factors reflecting tumor burden and individual patient response; and (4) factors reflecting medical and operative treatments and their outcomes. Technological approaches It is our hypothesis, that if we can utilize patient-specific modeling techniques to generate valid Digital Patient Models (DPM) (utilizing these IEs) we may be able to develop a statistically valid methodology for predicting diseases, predicting treatment outcomes, preventing diseases or complications, and developing personalized treatment regimens. We are calling this proposed system Model-Based Medical Evidence (MBME), and as yet remains undeveloped. It is further postulated that MultiEntity Bayesian Networks (MEBN) used in the construction of the DPM will be utilized in the development of a practical decision support system. Literature regarding HCC was analyzed, combining epidemiology; risk factors; infectious etiologies; pathology, microenvironment and biomarkers; screening and diagnostic technologies; treatment modalities. IEs, that will be used to populate the patient databases and MEBNs required for data mining and decision support, were identifed. This information was also used to reinforce a well-established treatment protocol, i.e. the Barcelona treatment algorithm, and, to add extensions that include enhanced screening and greater specifics regarding treatment selections.