Medication errors in the operating room are much more prevalent than traditional incident reports have indicated. In fact, our recent study demonstrated that one in twenty perioperative medication administrations, and every second operation, involves a medication error (ME) and/or adverse drug event (ADE). With more than 50,000 operating rooms conducting 27 million operations annually, this suggests that approximately 15.75 million perioperative MEs occur annually in the U.S. alone. Almost half of these lead to observed patient harm and the remainder have the potential for patient harm. More than two thirds of the harm caused by perioperative medication errors is serious or life-threatening. While not yet widely used in operating rooms, clinical decision support systems have been shown to prevent medication errors and associated patient harm in other patient care areas, and alerts and specific drug decision support have the potential to prevent more than 50% of MEs and 95% of ADEs in the operating room. The primary goals of the proposed research is therefore to design, build and implement platform-independent clinical decision support in the perioperative setting and to evaluate whether the decision support improves patient safety by preventing MEs and/or ADEs. Evidence-based medication-related clinical decision support rules for the operating room will be designed, prioritized and tiered, and finally validated by expert panel using a modified Delphi Approach. The decision rules will be used to develop and implement a perioperative clinical decision support tool that interfaces with existing electronic health records, using user feedback for iterative redesign and optimization. We will study the unintended consequences of the technology implementation with an interrupted time series analysis complemented by qualitative analysis of semi-structured user interviews. Finally, we will conduct a randomized controlled trail to assess the incidence of MEs and ADEs with and without the clinical decision support system, using our previously-described observational technique for ME detection. I will perform the proposed research within the Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital, working closely with my mentor Dr. David Bates, a world renowned expert in patient safety, informatics and medication errors, and Chief of the Division of Internal Medicine at Brigham and Women's Hospital, our partner hospital. We have assembled an interdisciplinary team of collaborators and consultants from across Harvard University that have deep expertise and international reputations in patient safety, medical informatics, device interoperability, and qualitative and quantitative statistical methods. This research will be complemented by formal coursework at Harvard University as well as career development workshops at Brigham and Women's Hospital and Massachusetts General Hospital. This combination of intensive research, ment...