AI News, This Software Uses AI to Identify Cardiac Arrests Faster Than Humans artificial intelligence
This AI Software Is Helping Emergency Dispatchers Save Lives
Although ambulance crews are undoubtedly essential players in the process of saving the lives of people in medical distress, dispatchers are the ones who initially assess the situation and make crucial decisions about the urgency of the situation and what kind of help to send.
By displaying information on dispatchers’ computer screens, Corti gives them real-time coaching to talk calls through how to check a person’s breathing and pulse and perform CPR if needed.
In Copenhagen, the emergency services department there used Corti and found it could pick up on possible cardiac arrest cases faster and with greater accuracy than humans alone without the tool.
Data shows for every minute in a cardiac arrest case that passes without intervention via CPR or an automated external defibrillator, the chances of survival decrease by 7 to 10 percent.
The successful uses of Corti so far understandably make people feel hopeful and wonder if AI could be a key component in helping the hundreds of thousands of people who suffer cardiac arrests each year survive them.
Besides taking dispatchers through each step of coaching a person to check for a pulse and breathing, and then perform CPR if necessary, Corti notices things dispatchers may miss and relies on the power of machine learning-driven knowledge.
AI software helps emergency dispatchers spot cardiac arrests | MobiHealthNews
A Danish AI system that can identify cardiac arrest from a 911 call 95 percent of the time might soon be coming to the US, according to a report in Fast Company. Corti, which launched from the company of the same name in 2016, is an example of how AI can augment, not supplant, human healthcare workers.
Once they have dispatched an ambulance, they must try to determine the nature of the injury — and do everything possible to stabilize the patient — by talking to family members or bystanders who are often panicked and generally lack medical training.
Artificial Intelligence and EMS
SSM was revolutionary because Jack showed us all how to correlate computer aided dispatch data, incident dates, times and locations to predict peak times in order to schedule the right number of ambulances in the proper geographic post locations to manage call volume in an efficient and cost-effective manner.
We’ll be introducing AI in a comprehensive, full-day preconference workshop on Tuesday, Feb. 20, and we’ll also present an amazing, fast-paced session on the same technologies on Wednesday, Feb. 21, from 10:00–11:30 a.m. For those thinking this is like Star Wars and not applicable to their EMS system, hold on because I’m going to take you on a fast, explanatory ride into this new technology galaxy.
Engineers and programmers have now learned how to mimic the brain and turn computers into multicenter processing systems that can store incredible amounts of data, analyze it, perform multiple simultaneous processes and calculations from different sensory and stimulus areas, blend/merge them together into an incredible “data milkshake,” and conduct incredibly complex actions that we take for granted—all in a nanosecond.
In a nanosecond, the infant’s brain senses the danger, processes the location and extent of the hazard, charts a path for escape, and sends signals to multiple programmable action pathways that allow independent biological systems to take immediate actions: the infant rapidly pulls back his hand, cries out to alert his parents of his plight, turns his body, flees the hazard area and runs to his parents for care.
Engineers are now able to recognize the limits of computers and build AI machines that think and act like the human brain, where a central brain stem oversees the nervous system and offloads tasks—like hearing and seeing—to the surrounding cortex.3 What’s amazing is that AI machines work on systems that allow them to navigate the physical world by themselves.
The next generation systems, capable of AI, are now are dividing work into tiny pieces and spreading them among vast “farms” of simpler, specialized chips that consume less power.3 For example, Google’s servers now have enormous banks of custom-built chips that work alongside the CPU, running the computer algorithms that drive speech recognition and other forms of AI.3 In 2011, Google engineers led a team that explored the idea of neural networks and computer algorithms that can learn tasks on their own, like recognizing words spoken into phones or faces in a photograph.
In the fall of 2016, a team of Microsoft researchers built a neural network that could recognize spoken words more accurately than the average human could.3 The leading internet companies are now training their neural networks with help from another type of low-powered chip called a graphics processing unit (GPU) that can process the math required by neural networks far more efficiently than CPUs.3 If you watch Jeopardy, you may know that IBM programmed Watson, its AI machine, to play against the game show’s top winners of all time.
Corti’s platform has a built-in recording solution that’s embedded with AI models that can analyze every call and, as the call volume increases, predict which calls should be the focus of additional training for dispatchers, which calls should be checked for quality assurance and which calls potentially hold new models about patterns formerly unknown to Corti.
Google’s recent announcement that the company will release a headset that uses AI to instantly translate different languages and an image recognition app that will allow us to point at objects and instantly retrieve information also may be new tools that prove invaluable to EMS crews in the field.5 Nashville-based Intermedix, also exhibiting at EMS Today, has a data science arm (formerly WPC Healthcare) that’s developed an amazing ML system.
The system distilled 90,000 articles related to sepsis and correlated 2,200 risk attributes (i.e., variables).Using this knowledge, it can rapidly analyze each patient encounter in a single facility over the last 24 months to score whether or not a patient has a high likelihood for sepsis—based solely on a few data parameters entered at registration—and without the clinical team providing vital signs, lab results, or having to study a patient through observation.auactqtdatzdusreyfzrfsawtrfayf One Intermedix hospital subscriber reported that, out of 1,535 patients entering their ED, the system identified 26 patients who had sepsis.
- On 19. juni 2021
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