Real-Time Analytics Can Help Stop Ebola - InformationWeek

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Healthcare // Analytics
11:46 AM
Stanton Martin
Stanton Martin

Real-Time Analytics Can Help Stop Ebola

Data analytics can improve disease-related processes to mitigate human suffering and ensure that future outbreaks are managed more effectively.

In a recent InformationWeek article, Ellis Booker reported on the effect that poor data analytics is having in the fight against Ebola. Implicit in that article is the question: Can analytics tools be used effectively to fight Ebola?

Absolutely. Analytics can help healthcare officials understand the best way to manage infectious diseases and alert medical organizations to the places where equipment and expertise are needed the most. It can assist in helping the public understand what is happening and facilitate the process by which drugs and vaccines that are used to fight the disease are developed and distributed.

Right now, public health officials are using traditional means to fight Ebola. Quarantines date from the medieval era. How is that process working out? When New Jersey and New York governors Chris Christie and Andrew Cuomo announced a mandatory quarantine for healthcare workers flying into other states' airports from West Africa, the most immediate result was the poorly executed detainment and quarantine of Dallas nurse Kaci Hickox, who landed in Newark Airport after treating Ebola patients in Sierra Leone as part of Doctors Without Borders.

[How can we ensure that EHRs do not contribute to medical errors? Read Ebola Misdiagnosis: Experts Examine EHR Lessons.]

In a first-person account of her experience published in the Dallas Morning News, Hickox described a scene of fear, confusion, and disorganization that led to her being treated like a criminal. This situation is unacceptable. US Ambassador Samantha Power summed it up succinctly: "We need to find a way when they [healthcare workers] come home that they are treated like conquering heroes and not stigmatized for the tremendous work that they've done."

So what would an integrated data analytics platform for preventing the spread of Ebola look like? Let's start with case tracking. This labor-intensive process, which ultimately helped the world rid itself of smallpox, is being used against Ebola. But it's hampered by a lack of easy access to hard-hit rural villages, a lack of trained personnel, and cultural resistance.

Public health officials and medical researchers must take a page from the playbook of successful companies that already use data gleaned from social media, financial transactions, Web usage, geospatial information, and other sources to track consumers and optimize their marketing strategies. With African cellphone use now topping 80%, the capability now exists to augment contact tracing. That is, by aggregating reports from health workers with cellphone tower pings and applying choropleth mapping techniques, authorities can generate a real-time visual map of the spread of Ebola. They can then relay this information to medical relief organizations as well as to screeners and customs officials. As a result, temperature-taking protocols could be augmented with analytically derived business rules to determine which passengers are at high risk for infection and thus should be monitored or quarantined.

Risk indices generated from this process could then be passed to planners in the US, so they would know a priori how many cases like nurse Hickox's they can expect. Having that information in hand would allow time to plan a "hero's quarantine," where healthcare workers are isolated in comfortable facilities that not only stop viruses, but also allow for social integration. Those who are quarantined could still work via special telecommunications facilities, their family and friends could visit, and their ordeal could be minimized.

By proactively implementing the analytics infrastructure needed to combat this Ebola outbreak, we can improve our processes and procedures in such a way that mitigates human suffering and ensures that future outbreaks are managed more effectively.

While there's a role for PhD-level data scientists, the real power is in making advanced analysis work for mainstream -- often Excel-wielding -- business users. Here's how. Get the Analytics For All issue of InformationWeek Tech Digest today. (Free registration required.)

Stanton Martin is a solutions architect at analytics software vendor SAS Institute. Before joining SAS, Martin worked as a bioinformatics research scientist at North Carolina State University, where he explored the potential of plant viruses as therapeutic agents in the ... View Full Bio
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User Rank: Author
10/29/2014 | 12:36:49 PM
The analytics of Ebola

I too am fascinated by the analytics of Ebola. I have been reading about the complex modeling that public health professionals are doing to predict the rate of disease transmission outside of Africa however I think the major issue in Africa itself is getting an accurate count of cases since the infrastructure is overwhelmed and so many patients are dying at home because of the lack of hospital beds. That makes the analytics of the disease much more complex because we can't fully get an accurate count of cases, deaths or recoveries. The numbers then become a moving target and modeling becomes almost impossible.

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