The healthcare sector has been striving to streamline its operations to minimize loss of lives. One health center, Ochsner Medical hospital, seeks to hasten the process with their latest artificial intelligence project.
So what will be entailed in the Ochsner AI system? Well, the five-year-long, research work aims at utilizing patient’s data to prioritize high-threat cases. By doing so, medics will integrate a smart working technique that’s time conscious and precise.
The machine learning technique is expected to only give timely alerts for efficiency and quick response. For example, a cardiac arrest patient will be supervised under the system giving it prior warnings in case of an emergency.
Ochsner Life -Saving Technology
Normally, nurses and doctors use pagers for alerts or notification of emergencies. With the new Ochsner AI technique, hospital staff will be issued with alerts in good time (four hours in advance). This will be accomplished by a timely relay of information from the data points monitored by artificial intelligence.
“This technology definitely helps save lives. If we can make a difference for patients because of the alerts, we’ve done a tremendous service to them, their families and the community.”
So far Ochsner AI prides in having minimized the number of codes by 44 percent. This was accomplished after a 90-day pilot program was initiated to test the machine learning technique. From the result obtained, the researchers are optimistic that the system can be operational for 24/7.
What’s Unique About Ochsner Latest Project
To craft such a sophisticated and precise system is no mean task that’s for sure. Firstly, the technicians had to feed all medical records and records for synchronization by the medics. In doing this, Epic foundation had to involve their health center software to compiles the records from 11 hospitals.
“This is a whole different ball game that’s changing the culture. We’re creating specific training that doesn’t typically exist and building teams to do this well,” stated Richard Milani, chief clinical transformation officer at Ochsner Medical center.
Besides the technical complexity, the developing process entails synchronizing the output to make it meaningful to doctors. The sophisticated system shall analyze the patient’s stability and detect any sudden change in body functionality.
No Need to Outsource Artificial Intelligence Experts
Before the technology gets popular, medical facilities or hospitals need to have a clear picture of what is required to run on this. So this machine learning algorithm will be found on Microsoft’s software “Azure” and should act as an assistant for doctors.
All this tech talk shouldn’t scare users as technicians indicate there won’t be a need for data experts. The system should seamlessly interact with doctors since all patients are connected to it. Consequently, hospitals will be able to modify the AI systems and develop their own work models.
So far Epic foundation encourages users, on Epic’s software, to go and access the systems. This is regardless of whether hospitals have artificial intelligence experts or not. In spite of the program flexibility, the foundation insists that healthcare providers would still require induction to best handle the artificial intelligence system.
Information shared will include alert schedule, how to respond to the alerts and personnel to react. That implies that doctors won’t be replaced by the machine learning systems as they work simultaneously.
Should Doctors Be Worried About AI Systems?
The entry of machine learning treatment is slowly crawling its way to all fields. Medicine is no exemption. Recently, researchers integrated artificial intelligence in the removal of a tumor in the prostate gland. Apart from this, AI is used cancer treatment to replace heat induced therapy.
Did you know that a physician handles, on average, of 16-20 patients in a unit? Well, according to Michael Tuxillo, medical director Response and resuscitation team at Oschner Hospital, this is the scenario (which is overwhelming).
From the machine learning prototypes and projects been unveiled, it’s no doubt that the ‘train isn’t stopping’. The medical field is vast and capped with unending challenges providing an interesting playing ground for artificial intelligence to intervene.