The following is a guest blog post by Mike Serrano from NETSCOUT.
It’s a well-known fact that Facebook, Google, and our phone companies collect a lot of information about each of us. This has been the case for a long time, and more often than not it’s to improve the user experience of the services we rely on. If data is shared outside the organization, it’s anonymized to prevent the usage of any one individual from being identified. But it’s understandable while this practice has still sparked a passionate and longstanding debate about privacy and ‘big brother’-style snooping.
What is often forgotten, however, or more likely drowned out by the inevitably growing chorus of privacy concerns, is the opportunity within the big data community for this valuable information to be used for social good. The potential is already there. The question, though, is how different organizations, and particularly the healthcare sector, can take advantage of anonymized user data to benefit society and improve the human condition.
When it comes to healthcare, data from mobile networks holds the biggest opportunity for the patient experience to be dramatically improved. To truly understand how real-time traffic and big data, in the form of historical network usage and traffic patterns, can be used for social good, let’s look at a few possible scenarios – two of which can be accomplished without needing to disclose individual user information at all.
Public health – Getting ahead of an outbreak
What a decade ago would have seemed impossible is very much a reality today. The pervasiveness of the smartphone and how people are using it has fundamentally changed our ability to leverage real-time communications data to the benefit of our society. For many people, smartphones have replaced computers as the primary device to search for information. This has value in itself, as when people use a smartphone it’s possible to place them in context of their community and travel patterns.
Zika is a recent example of a parasite spread by mosquitos that produces flu-like symptoms and can have grave consequences on a developing fetus, causing microcephaly. To control the mosquito population, local vector control agencies place field traps to capture mosquitos and periodically test the mosquitos they collect. This approach has value, but it’s slow and reactive.
What we have learned from flu epidemics is there’s typically an increase in Google searches of “flu symptoms” that emerge just before or at the same time as an outbreak of influenza. Since Zika is a mosquito-born pathogen, it will occur outside of times of the normal spread of influenza, but the initial symptoms are very similar to the common flu.
By monitoring mobile searches for any of a number of unique search terms, it is possible to quickly identify real-time locations where outbreaks may be occurring; thus allowing for a more targeted response by both vector control and public health agencies. In addition, it’s then possible to identify the extent to which migration through the area has occurred, and to where that population has traveled.
When merged with environmental data such as wind patterns, temperature, and precipitation, public health agencies can be extremely targeted about where to deploy resources and the nature of those resources to deploy. Such a targeted and immediate response is only available through the use of real-time network traffic data.
Public safety and medical deployments – disaster response
Recent earthquakes have emphasized the potential death and destruction that natural disasters can create. When buildings collapse first responders’ rush in to look for survivors, putting themselves in harms way as a series of aftershocks could cause additional damage to already weakened structures. But it’s a calculated risk. The search for life must happen quickly, which often means first responders are operating with no knowledge of the potential number of causalities within a building.
To ensure the appropriate allocation of response teams, public safety agencies working in tandem with healthcare organizations could leverage mobile network data. When a mobile phone is turned on, it automatically registers to the mobile network. At this point, the operator knows the number of devices in a certain area based on the placement of the cell tower and the parameters of that tower.
By comparing the last known number of registrants against historical network usage, the operator could guide public safety and relief agencies by understanding the number of known mobile phones in an impacted area. If needed, the operator could also assist in the identification of precisely who may still be in a damaged structure, should that level of detail be required.
Pandemic control – removing the guesswork
All major health organizations understand the next major pandemic is only a plane ride away from arriving on their doorstep. For example, when an international flight lands from a country that’s had a recent outbreak of flu or disease, there could potentially be hundreds of infected passengers on board. Those passengers will exit the plane, grab their luggage, and quickly head into the community – travelling far from the airport and growing the transmission radius significantly.
In a situation such as this, the challenge of containing or managing an outbreak is intrinsically tied to knowing where those passengers end up. How far have they travelled, how did they diffuse into the existing population, and how many circles of control need to be established in order to mitigate the risk?
Big data can address this issue. By working with mobile network operators the local healthcare community can quickly react, taking advantage of big data to deploy public health resources more effectively than they could otherwise. Operators already have access to this information, including where subscribers join the network and their current location, and this data is tremendously valuable when placed in the hands of healthcare professionals looking to stem a viral outbreak. The airline involved could also assist by providing any the phone numbers of passengers once the risk was identified.
The future of big data analysis for healthcare
Understanding human movement and social activity, powered by big data pulled from mobile networks, will have a fundamental role to play in more efficient healthcare response in the future. National, state, and local public health officials should all look to implement initiatives based on the use of big data for social good.
When you compare the use of big data against the current approach – where patient zero arrives at hospital and the local healthcare body has to try and identify who else is at risk based on the patient’s travel patterns and limited information they can provide – the benefits of this new approach are obvious.
As the conversation around the use of big data for healthcare purposes evolves, there will inevitably be new questions over individual privacy. While the examples outlined above do take advantage of subscriber behavior and individual insights – be that search terms of location information – the purpose is to understand populations or communities, not to identify any one subscriber. With this in mind, it is easy to mask subscriber identifiers while preserving the information about the population. Ultimately, the goal is to provide a more efficient utilization and allocation of society’s resources as we work to improve the human condition, not to undermine any one person’s right to privacy.
About Mike Serrano
Mike has over 20 years of experience in the communications industry. He is currently responsible for Service Provider Marketing at NETSCOUT. He began his career at PacBell (now part of at&t) where he designed service plans for the business market and where he was responsible for demand analysis and modeling. His career continued with Lucent technologies where he brought to market the first mobile data service technology. At Alloptic, he was responsible for marketing the industry’s first EPON access solution and bringing to market the first RFOG solution. At O3B Networks, Mike headed up marketing bringing to market the first MEO based constellation of satellites for serving internet service to the Other 3 Billion on the planet. Mike’s work continued at Cisco where he helped to define MediaNet (Videoscape) and the network technology transformation for cable operators. Mike holds a B.S. in Information Resource Management from San Jose State University and an MBA from Santa Clara University