Multi-Institutional Multidisciplinary Injury Mortality Investigation in the Civilian Pre-Hospital Environment (MIMIC): a methodology for reliably measuring prehospital time and distance to definitive care.
Nicolas W MedranoCynthia Lizette VillarrealMichelle A PriceEllen MacKenzieKurt B NolteMonica J PhillipsRonald M StewartBrian J EastridgePublished in: Trauma surgery & acute care open (2019)
The detailed study of prehospital injury death is critical to advancing trauma and emergency care, as circumstance and causality have significant implications for the development of mitigation strategies. Though there is no true 'Golden Hour,' the time from injury to care is a critical element in the analysis matrix, particularly in patients with severe injury. Currently, there is no standard method for the assessment of time to definitive care after injury among prehospital deaths. This article describes a methodology to estimate total prehospital time and distance for trauma patients transported via ground emergency medical services and helicopter emergency medical services using a geographic information system. Data generated using this method, along with medical examiner and field investigation reports, will be used to estimate the potential survivability of prehospital trauma deaths occurring in five US states and the District of Columbia as part of the Multi-Institutional Multidisciplinary Injury Mortality Investigation in the Civilian Pre-Hospital Environment study. One goal of this work is to develop standard metrics for the assessment of total prehospital time and distance, which can be used in the future for more complex spatial analyses to gain a deeper understanding of trauma center access. Results will be used to identify high priority areas for research and development in injury prevention, trauma system performance improvement, and public health.
Keyphrases
- radiation therapy
- emergency medical
- trauma patients
- healthcare
- locally advanced
- public health
- cardiac arrest
- quality improvement
- palliative care
- affordable care act
- blood pressure
- pain management
- climate change
- emergency department
- machine learning
- cardiovascular disease
- risk factors
- risk assessment
- mental health
- current status
- deep learning
- coronary artery disease
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