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3 Surprising Ways Data Analytics Could Save Your Life

Kayla Matthews / 3 min read.
November 14, 2018
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People typically talk about big data in the context of increasing efficiency or revenue for businesses, but it has applications across virtually every sector.

Some of those applications could save lives. Here are three surprising ways in which data analytics could help save your life.

1. Preventing Chronic Diseases

Every year, the U.S. spends around $30 billion on preventable hospitalizations. About half of those hospitalizations are due to chronic diseases, namely heart disease and diabetes. These diseases are two of the most prominent health problems Americans face.

Preventing these diseases and the hospital visits they lead to is a significant challenge, but it’s one data analytics may be able to help address.

Boston University’s College of Engineering and Boston Medical Center are teaming up to tackle this issue by developing machine learning algorithms that can identify patients who have a high risk of developing these two diseases, which can allow doctors to intervene early and keep them healthy through personalized health plans.

Data analytics technologies enable the use of many more variables than medical professionals typically use when determining risk. For this project, the researchers plan to use data from electronic health records, implantable devices, wearables and home-based networked diagnostic devices.

2. Improving EMS Efficiency

It’s essential for emergency medical services, or EMS, agencies to respond to incidents quickly, but doing so is a challenge, as agencies have limited resources. They need to allocate personnel, ambulances and equipment appropriately and aim to get patients care as quickly as possible.

By collecting data about each 911 call and response and using software to analyze it, EMS teams can optimize their operations. Software solutions like those from DataTech911 help EMS companies manage and evaluate their data, which allows them to allocate resources more effectively and improve their performance.


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These software solutions can use data about the type of emergency, its location and the resources available to help teams choose which personnel and equipment to send. It can also help them determine the fastest routes to the scene of the emergency and to the hospital. Over time, reviewing this data can help EMS companies predict call volumes and emergency types and allocate their resources accordingly.

3. Preventing Crime and Violent Attacks

Police departments and security agencies are beginning to use data to predict and stop crimes and public safety incidents.

Police departments around the world are now using systems that incorporate information about locations and times of past crimes, criminal records, weather and more to predict when and where crimes are most likely to occur. Hunchlab, an online patrol management system, is one such technology. In addition to predicting crimes, it also allows police departments to track which tactics work best for deterring criminals.

These types of programs, however, have received some criticism for potentially incorporating biases into their algorithms that could lead to over-policing. Hunchlab says it has features that help prevent over-policing and align police activities with the priorities of communities.

Big data may also be able to help stop terrorist attacks. Security agencies can use analytics technologies to scan and assess social media content. Analyzing data about past attacks may also be able to predict future ones. Researchers at Binghamton University recently developed a framework that examines patterns and connections in terrorist behavior to predict activity. According to the researchers, the framework can anticipate many of the characteristics of incidents with more than 90 percent accuracy.

Data Analytics Across Industries

From enhancing preventive health care in improving responses to medical emergencies to preventing terrorist attacks, many of these applications could help save lives. The technologies and processes are not always perfect, especially since they’re relatively new.

Many of them are already demonstrating incredible value, however, and the technologies we have and the ways we use them will continue to develop.

Categories: Big Data
Tags: Big Data, Data analytics, health

About Kayla Matthews

Kayla Matthews is a technology writer covering big data, IoT tech and connected technology issues. You can find her other work on ProductivityBytes.com, as well as on Information Age, KDnuggets, The Week and Digital Trends.

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