Mobile analytics tools track perimeter of Ebola
The Ebola outbreak has spurred development of software and systems designed to help track, diagnose and even predict the behavior of pandemic illnesses. From IBM to third-party developers, health tech firms are working away on tools and apps to stop the virus.
In a recent announcement, IBM announced several projects to help prevent the spread of Ebola, including a citizen engagement and analytics tool that enables communities to transmit data via SMS or voice calls to the Sierra Leone government.
The tool links IBM supercomputing resources, cloud services and an analytics program to study SMS datasets and create opinion-based heat-maps that correlate public sentiment to location information.
The maps have been able to identify regions where Ebola cases are on the rise and products and services used to treat the disease, such as soap, electric power and burial services, are in demand.
Live intelligence on the behavior of the disease has also given local governments the information and credibility they need to approach the international community for support in the epidemiological battle.
“We saw the need to quickly develop a system to enable communities directly affected by Ebola to provide valuable insight about how to fight it," said Dr. Uyi Stewart, chief scientist, IBM Research – Africa.
Airtel, a mobile telephone services firm, has set up a toll free number where citizens can send SMS messages that help fill out the analytics maps further.
"Mobile technology is Africa's most powerful communications platform,” said Airtel managing director Sudipto Chowdhury, “providing an important channel for reaching large numbers of the population."
The SMS data is anonymized by Kenyan start-up Echo Mobile, which specializes in using mobile phones to provide basic communications in underserved communities.
"We're working to make sure that the stream of messages from patients, health workers and the general public can be used to augment the response effort and provide a direct and near real-time view of the situation on the ground," said Jeremy Gordon, product director, Echo Mobile.
IBM is now looking to analyze mobile phone signal data in order to monitor and track population movements, helping scientists to predict the spread of disease. In doing so, it recently donated its Connections technology to the Nigerian government.
Connections is a secure platform for online content sharing, and it has been used to help health workers securely share documents, identify experts, exchange video, chat and audio messages via mobile devices and hold virtual meetings.
Elsewhere, IBM has been working to deploy its analytics tools in clinical settings.
Last month the company announced a deal with San Antonio mobility provider AirStrip and the University of Michigan to build an acute care system.
The new solution brings together data from electronic medical records, body sensors and other sources with predictive analytics to create the AirStrip mobile Acute Care Early Warning System (mACEWS), that could be use to transmit diagnostic insights to doctors’ mobile devices.
The AirStrip system would translate structured and unstructured data via the AirStrip ONE platform, and deliver real-time analytics on that data using IBM’s InfoSphere Streams. InfoSphere is an analytic platform that allows customer-developed applications to quickly analyze and correlate millions of datapoints per second as they arrive from thousands of real-time sources.
The AirStrip mACEWS system’s resulting predictive care insights would then be ready for consumption by clinicians who use AirStrip’s mobile applications on Apple, Android and Windows devices.
"Predictive analytics has the potential to provide clinicians the ability to see and take action on much more of the potentially available data on their patients and course-correct sooner when a complication presents," Sean Hogan, vice president of IBM Healthcare, said in the statement.
Such tools could also be applied to patients being monitored both inside and outside the hospital to detect clinical deterioration from pulmonary disease, diabetes, congestive heart failure or other chronic diseases.
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