Health Information Technology (Health IT) is designed to store patients' records safely and clearly, to reduce input errors and missing records, and to make communications more efficiently. Concerned with the relatively lower adoption rate among the US hospitals compared to most developed countries, the Bush Administration set up the Office of National Coordinator for Health Information Technology in 2006 to mandate the Health IT implementation with $25.9 billion subsidies to eligible hospitals starting 2009. Underlying the huge subsidy is the belief that Health IT can reduce hospital cost by improving efficiency and quality, and can help reduce total health expenditure. Yet researchers from both medical field and economics field have been struggling to find evidence on such implications for years. In my dissertation I conduct empirical analysis to investigate the causal effect of Health IT on US hospitals efficiency and quality, and to examine whether such effects vary across different patient groups or hospital types. The rich study sample consists of two datasets: the 2002-2008 National Inpatient Sample (NIS) and State Inpatient Database (SID) generated by Agency for Healthcare Research and Quality (AHRQ), and the HIMSS Analytics data. The NIS is a nationally representative patient level discharge data set, which includes about 20% of the total US inpatient discharges annually. The HIMSS Analytics is a survey data set that reports about 5000 US hospitals' Health IT adoption status and history, together with hospital characteristics. The SID comes from four states: CA, FL, NY, and WA, with almost 100% discharge information available. It also gives the benefit of linking multiple discharge records to the same initial visit record, and therefore can be used to identify readmissions to hospitals. The first chapter is a in depth literature review on the studies of Health IT and its impact on hospital outcomes. The second chapter is an empirical paper that uses nationally representative data to study how IT system affect patients' Length of Stay, by adopting Finite Mixture Model method. The third chapter empirically analyzes IT's impact on readmissions, and particularly for five conditions that are also of Center for Medicare and Medicaid Services (CMS) focuses in determining hospital quality. This dissertation finds empirical evidence that supports Health IT's contribution. It also establishes how Finite Mixture Model method can be used in healthcare research. Results show that not all patients or hospitals are expected to receive the same degree of benefit from Health IT adoption. In fact, majority of the populations are not shown to be affected. Still, studies from this dissertation illustrate the potential of Health IT systems, as the analyses so far are only conducted in the hospital setting. On a broader sense, Health IT is supposed to support all aspects of healthcare deliveries and coordination. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]