Alternatively, a healthcare model that utilizes medical intervention based on personalized predictions of the patient's clinical status and possible deterioration could potentially decrease costs, unplanned rehospitalizations and mortality rates. This model also has the potential to improve the overall quality of care. We refer to this model as the predictive healthcare model.
In this dissertation, we examine three outstanding challenges towards fully realizing the predictive healthcare model as the prevalent care model. Namely, i) we investigate means to streamline the costly longitudinal epidemiological studies using remote mobile monitoring and introduce the Berkeley Telemonitoring project; ii) we investigate the privacy challenge that is particular to the remote monitoring model and introduce the Private Disclosure of Information (PDI) semantic privacy model; and iii) we investigate the problem of publication bias in empirical sciences (including biomedicine) that hinders the credibility of empirical scientific findings and introduce a statistical test that detects bias in a sample of scientific publications which utilize the Student t-test.