Access to large volumes of data, computational power to store and analyse the data in real time and sophisticated algorithms that can find patterns in the data and alert authorities to health problems will help to make pandemics less devastating than they have been in the past, SAS Analytics senior business solutions manager Aneshan Ramloo says.
The benefit of analysing social media, blogs, online forums and keyword searches to provide early warnings and indicators can be significant if broadened to encompass the entire healthcare spectrum at national, global, and even individual level.
“In the past, health workers relied on manual, paper-based systems to record infections and deaths during disease outbreaks. Not only was it easy for errors to slip through, but the data was anecdotal and historical, and authorities did not get a complete understanding of the reach and impact of the outbreak with sufficient time to take proactive decisions.”
During the 2014–2016 Ebola outbreak, in West Africa, the US Centers for Disease Control and Prevention adopted a mobile data collection system that enabled healthworkers to instantly submit information to a database through text messages.
“This low-cost method of information gathering not only resulted in fewer errors, but also allowed analysts to draw up detailed maps of population movements, which made it easier to understand how the disease was likely to spread, and where to set up treatment centres,” says Ramloo.
“While this was certainly an improvement on the paper-based systems, the drawback was that mobile data was historic and did not provide researchers with the ability to track developments and population movements in real time. Therefore, the effective use of analytics within the healthcare sector has to grow and mature to better our lives.”
Mobile phones are just one source of data. Health authorities can overlay thousands of data sources – including social media, health and physician reports, keyword searches, media reports, transactional data from retailers and pharmacies, airline ticket sales, geospatial data and more – to not only better manage diseases and outbreaks when they do happen, but also see them coming months in advance, as well as what could happen if no action is taken on the information.
“By mining structured and unstructured data, we can track the movements of infected populations and whom they come into contact with. We can measure the success of containment policies, education campaigns and treatments, as well as [decide] what to do if they are not working. We can also evaluate the effect of weather and other environmental factors on the spread of diseases.”
As the use of intelligent algorithms, machine learning and natural language processing becomes more entrenched in advanced data analytics, technology will increasingly supplement the skills of humans to produce faster and more accurate medical diagnoses.
“We are already seeing successful applications of artificial intelligence in predicting a relapse in leukaemia patients and in distinguishing between different types of cancer.”
Machine learning can extract insights from unstructured data like clinical notes and academic journals to provide even larger datasets that will transform the medical industry into a proactive front against diseases.