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Andrew T. Reisner M.D. is an attending physician at the Massachusetts General Hospital Department of Emergency Medicine (MGH ED). He is also associate professor of Emergency Medicine at Harvard Medical School.
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Dr. Andrew T. Reisner, attending physician in the MGH Emergency Department (ED), received his undergraduate engineering degree at Stanford University prior to receiving his M.D. from Harvard Medical School. He completed his residency with the Harvard Affiliated Emergency Medicine Residency Program. He completed a research fellowship with Prof. Roger Mark at the Massachusetts Institute of Technology (MIT) Laboratory for Computational Physiology (LCP) and was a co-investigator during the initial development and public release of MIMIC-2, which is one of the world’s largest publically-available ICU research databases.
Dr. Reisner first joined the MGH ED as a staff attending in 2001. Between 2002 and 2012, Dr. Reisner also held an appointment as a research scientist with MIT. In addition to working with the LCP, Dr. Reisner worked with Prof. Harry Asada at the d’Arbeloff Lab, developing and investigating wearable medical sensor technology. Many of the principles identified by that MIT research team have contributed to today’s widely-available wearable sensor technology.
Starting in 2003, Dr. Reisner has held civilian appointments with the United States Army Medical Research and Materiel Command, working for Dr. Jaques Reifman and the Biotechnology High-Performance Computing Software Applications Institute. This collaboration led to the invention of the APPRAISE system which applies real-time pattern recognition algorithms to provide trauma patient decision-support. Currently, Dr. Reisner is an associate professor of at Harvard Medical School and the director of the MGH ED's Clinical Decision Technology Laboratory.
View my most recent publications at PubMed
Automated analysis of the vital signs commonly monitored in patients transported to trauma centers could significantly improve the ability to diagnose life-threatening bleeding before patients arrive at the hospital
A team led by investigators from MGH and the U.S. Army successfully field tested a system that analyzed patient vital signs during emergency transport in a fully automated fashion, finding that such a system could diagnose those with life-threatening bleeding before they arrive at the hospital, potentially saving lives.
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