Thanks to major shifts to modernize traditional medicine over the last few decades, many more key stake holders across health care are better connected. These new connections—among patients, physicians, hospitals, research organizations, and more—will continue to help drive efficiencies, improve collaboration, and provide a clearer path to optimal care.
Technological advancements will help us better understand the massive amounts of patient data now being collected every day. They will support the next scientific breakthroughs, and lead to new patient-centered care models. The goal, of course, is better, more personalized care at lower cost. And at the center of this new world order are data and real-time data analytics.
Unprecedented amounts of data are streaming from a host of sources, from clinical research to electronic health records to consumers tracking their vital signs with wearable devices. All this data must be stored, protected, analyzed, and always available.
Imagine a patient with early stage Parkinson’s describing his or her symptoms to an intake nurse or physician. Even with a daily medical journal, it can be hard for patients to recall symptoms such as hand tremors, to describe how they felt, and report when they occurred. Mobile apps and wearable devices can help monitor physical data, measure disease progression, and offer a clearer path to better care. The data can also offer new insights into the disease and its treatments.
Big Data, Big Challenges
This drive toward faster, more personalized care enhanced by large data sets brings new challenges. These challenges can be broken into four key areas. Some are widely recognized, while others only rise to the forefront once you’re waist-deep in this data deluge.
1) Fragmentation and Data Movement
2) Compliance and Security
3) Scale
4) Variety
Fragmentation and Data Movement
Health data sets reside in multiple systems. Some are built on modern data platforms like NoSQL and Hadoop, while others reside in legacy systems that are twenty years old. For example, the typical Medicare patient in the early 2000s saw an average of two primary care physicians and five specialists across four different practice settings. Just think about the number of systems in use across these varied environments.
All this information may allow practitioners to make personalized treatment decisions more quickly, but only if all these different data sets can be analyzed together. Organizations need to aggregate data in the most effective way.
Data transfer from one system to another brings its own set of challenges. Companies must consider infrastructure issues, and ensure that they have suitable network bandwidth. Companies also have to prevent thousands of data requests from overloading the production systems.
Compliance and Security
The recent spate of data breaches in healthcare and other industries shines a light on the number of ways that confidential data can be captured. Healthcare data is more vulnerable now due to the combination of large data sets, data movement, and multiple access points, including smart phones, tablets, and computers.
While we won’t go through the litany of steps that companies need to take to ensure that data is appropriately secured, companies should
