It may seem like disaster strikes out of nowhere, but in many instances, there are signs emergencies are about to take place. Hurricanes are usually heralded by atmospheric conditions, financial crisis will stem from irregular market activities, and emergency room visits are prompted by preexisting health concerns and other factors.
The problem isn't always a lack of information, but rather too much. It can be hard to tell which data indicates a problem is going to occur and which can be ignored as irrelevant. IBM detailed how Eric Holderman, a Disaster Preparedness Consultant, wants to overcome these information obstacles and use predictive analytics to simulate emergency situations to train response teams and inform the public. His strategies could be just what hospital emergency rooms need to prepare for the worst.
"The goal is to protect lives and decrease the amount of money lost to emergency situations."
It's impossible to actually look into the future, but an organization can learn from the past. Eric Holderman plans to improve disaster management by collecting and analyzing the data created by similar incidents. The goal is to protect lives and decrease the amount of money lost to emergency situations.
By using analytical solutions and machine learning, the more relevant information fed into predictive modeling tools, the better they can compare data to spot trends and prepare users for incidents in the future. For example, studying the effects of storms can help people who operate seaports recognize weather conditions that should cause concern and secure the most likely points of damage.
Plans for emergency rooms
Predictive analytics can be applied to large and small medical emergencies. The Nation Center for Biotechnology Information detailed how organizations use machine learning and data collection to predict the spread of disease and prevent pandemics.
These predictive practices can also help health organizations prepare resources like pharmaceuticals for emergency demand. By studying the current consumption of pharmaceuticals and then comparing existing datasets to emergency models, organizations can make decisions about emergency stockpiles and alternative distribution. The models used in predictive analytics should come from relevant information. In the seaport example, it's important information used for analytics comes from structures of similar size with comparable traffic.
An emergency room can prepare for major disasters or disease outbreaks by comparing information in its healthcare management system to that provided by regulatory agencies and data collected from identical health organizations. Whatever the results, the information must be made available to parties potentially affected by future disaster. This could include doctors, resource managers, ambulance drivers and patients. The information generated by predictive analytics is only helpful if it's visible and used to anticipate emergencies before it's too late.