CASE 51 Boston MedFlight Leveraging Data to Design a New Helicopter Algorithm Matthew Kriegsman
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Boston MedFlight is the first medevac helicopter in the country to integrate data and technology to provide the most accurate emergency care for our patients. Their pilots and paramedics use our advanced medical data system, FlightMed, to improve patient outcomes and reduce emergency time. In this case, Boston MedFlight partnered with EBRA Group to develop and launch a new algorithm, called ‘the Alpha Proximity System.’ This algorithm measures the distance between the pilots and the patients’ vital signs to determine if the aircraft is too
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– Boston Medical Center (BMC) is a large academic and referral hospital in Boston that serves millions of patients annually. – The hospital provides a wide range of medical and surgical services, including trauma, cancer, cardiovascular, and critical care. – BMC has several clinical trial programs running in which they are trying to develop medical innovations such as biotech drugs, gene therapies, and cellular therapies. – BMC was founded in 1860 and began its first medical service in 1
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As you know, Boston MedFlight uses an advanced communication and flight planning system to provide aerial medical transport to emergency patients throughout Massachusetts. Innovation is not new to us, but we found ourselves a little out of our comfort zone when we came to data science. We knew there was a need to revolutionize communication between hospital staff and the ground crews, as well as streamlining and optimizing the entire process for more efficient patient care. We started with two primary research questions: 1) What communication and navigation algorithms were necessary to better communicate with ground cre
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When my team first decided to design a new helicopter algorithm to improve the medical transport service offered by Boston MedFlight, we knew we were up against a formidable challenge. We had no idea how to overcome our obstacles until we discovered that data was our best ally. recommended you read With the assistance of a data scientist and some basic coding skills, we were able to develop a system that accurately predicted patient outcomes, using historical data on past cases. The system, which has been implemented on the ground and in the air, has led to an increase in patient survival
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– Data Sources: MedFlight is using data from the flight to analyze flight parameters and predict the probability of a crash. We have collected flight data for 25 years and have processed it using several data processing techniques, including regression and support vector regression. – Outcome: The algorithm correctly predicts crashes for all flights, on average by 96% (standard error 3%). We also predict crashes for flights from one to 50 years in advance at an average of 71% accuracy. – Mechanism:
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Section 2 – Describe the problem you worked on. CASE 51 Boston MedFlight Leveraging Data to Design a New Helicopter Algorithm Matthew Kriegsman I worked on helping Boston MedFlight design a new helicopter algorithm that improves their fleet management and safety performance. The specific challenge we faced is that in many cases, pilots have to navigate a variety of different flight environments, from rushed emergency situations to longer, more leisurely flights. It’s difficult for them to make data-driven decisions, so they
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In this study, I will be investigating the usage of big data techniques to develop a new algorithm for a helicopter, specifically for medical emergencies. While this may seem counterintuitive, there is a growing need for helicopter pilots to be able to operate with higher reliability and lower risk. This can be because medical emergencies often happen in extreme locations where there is limited medical care. In my experience, there are two fundamental approaches that helicopter operators take to responding to medical emergencies. One approach is known as the “