The ongoing Ebola outbreak has captivated and horrified the world at large. Thousands of people in West Africa have died of the disease, and cases have been reported in the United States and Spain. Unsurprisingly, this has led to a tremendous amount of panic and even hysteria among the general population.
Health organizations are making significant progress pushing back against Ebola, but the damage has been tremendous and many fear that more outbreaks may be on the horizon. Fortunately, new advances in data mining and analytics are helping some firms to track and predict the behavior of this disease, and may prove invaluable for fighting future epidemics, as InformationWeek reported.
The news source noted that both the U.S. Centers for Disease Control and World Health Organization depend on on conventional approaches to epidemic tracking. This relies largely on hospital and clinic reports of deaths and newly admitted patients diagnosed with the disease.
While relatively effective, this approach has limitations, especially when it comes to predicting where Ebola could potentially crop up next. Considering the fear that Ebola causes, this lack of clarity can be deeply damaging for both individuals and larger populations.
Several organizations have embraced data mining and analytics as a means of delivering a more accurate projection of the disease's trajectory, InformationWeek reported. For example, researchers at Humboldt University in Germany used this technology to analyze global air transportation networks and travel patterns. With this insight, the researchers created a model that identifies the risk that Ebola will appear in any given country.
Additionally, data mining and analytics can identify outbreaks faster than traditional efforts. InformationWeek noted that HealthMap offers early detection and real-time surveillance of developing health threats, including Ebola, by integrating information from a variety of sources. These include social media networks, online news stories, travel websites and official reports. According to the news source, HealthMap gained attention when it covered the outbreak of Ebola in Guinea nine days before government authorities reported the disease's outbreak to the World Health Organization.
"Data modeling can be quite accurate and useful in predicting the outbreak of contagious diseases, but it must be continually refined and cross-checked," said Michael Hendrix, director of emerging research and issues at the U.S. Chamber of Commerce Foundation, the news source reported. "Think of big data as offering a trip wire of sorts for alerting first responders."
Hendrix added that social media, while not always trustworthy in these matters, can be a valuable source of information. However, organizations should not rely entirely on this or any other single channel for data. Rather, the most effective accumulates and makes sense of a broad range of information.
"Big data doesn't replace traditional data sources or surveillance networks in watching for outbreaks – it helps make them better. And when the worst happens, data helps medical professionals and public health experts do their job better," said Hendrix, according to InformationWeek.
Analytics and health
The benefits of analytics and data mining in the health care sector are not limited to Ebola and other outbreaks. As The Straits Times reported, care providers in Singapore now rely on this technology to improve outcomes and performance in a variety of areas, and leading officials are calling for expanded use of these resources.
For example, doctors now receive prompts to help them choose the right medicine when treating patients, as well as alerts when patients' test results come back with unexpected outcomes.
The technology is patient-facing, as well. Whenever a patient has an upcoming appointment, he or she receives a text message reminder. If the patient skips the appointment, the hospital or clinic receives an automatic notification.
Additionally, the insight gleaned from analyzing large amounts of patient data is helping researchers and doctors to identify disease trends and correlations that they would otherwise miss. In the case of a contagious disease, this can allow hospitals to manage their resources more efficiently. In the case of diseases such as diabetes, this can help doctors identify at-risk patients early.
To make the most of these resources, though, health care organizations need to invest not only in data mining and analytics tools themselves, but also the necessary supplemental assets. These include high-performance algorithms that can easily integrate with analytics apps and deliver consistent results.