| Medical literature and the actions of policymakers have emphasized the importance of evidence-based medicine in recent years, but basing clinical practice on an exploding base of evidence is challenging. Systematic reviews, which are very resource-intensive, are a crucial channel in the pathway from medical literature to clinical practice. This thesis begins by estimating the value of one systematic review, finding that synthesized evidence regarding treatments to prevent osteoporotic fractures generated a net benefit of approximately ;To further increase the speed at which articles can be screened and decrease the cost of preparing systematic reviews, machine learning techniques allow avoidance of up to 80% of articles. When updating an existing review, savings are increased by utilizing the information present in original screening decisions to train the machine learning model. Finally, implementation issues are addressed, paying attention to technical, organizational, and institutional challenges and opportunities. |
