Big data is getting bigger
Technology is prevalent everywhere in our lives. For instance, we use increasingly sophisticated smartphones for communication, our cars contain more and more sensors and microprocessors to make driving safer and today many companies depend on technology like the Internet as a major part of their business model.
While data collection is relatively cheap, the art of harnessing large datasets – or: big data – still remains a great challenge. Companies like Google, Amazon & Co. collect lots of data to gain deeper insights into complex problems and their success heavily depends on how they exploit big data. Google did not invent search, Amazon did not invent retail, Netflix did not invent movie rental and Facebook did not invent social. However, due to the increased popularity, prevalence and progress of the Internet as well as their outstanding efforts and success in dealing with big data, these companies were able to create tremendous added value for their customers. They and their competitors work hard to find new ways to leverage big data to improve their products and services.
Big data and healthcare
Big data can be extremely helpful if we know how to deal with it. Despite the fact that we are collecting massive data like never before – about patients, hospitals, treatments etc. – in the healthcare domain, the question how we can extract meaningful information from this data on a domain wide basis remains unsolved.
Fortunately, we see promising examples such as patientslikeme.com or ginger.io that demonstrate how big data can leverage improvements in and create new useful use-cases for technology for the healthcare domain. Another impressive example is the case of physician Jeffrey Brenner, who discovered several patterns in medical data from the three main hospitals of Camden, New Jersey. For instance, he revealed that a single building in Camden sent more persons with serious fall injuries than any other building, namely 57 elderly in two years resulting in three million dollars in healthcare bills. Such insights render efficient improvement possible. And this is just one of several findings Brenner made while studying the data but it gives an idea of the possibilities and power of harnessing big data.
More and more data becomes publicly available. For instance, as a priority Open Government Initiative for President Obama’s administration, the website data.gov provides an increasing number of datasets collected by the Federal Government for the public. The Department of Health and Human Services released several datasets such as Medicare Cost Reports, Product Recall Data or Chronic Conditions Data. In the context of Open Data Initiatives all around the world, governments start to release large datasets for useful applications in various domains. Some private companies use open data, other build proprietary datasets. But the vision is clear: excavate the hidden treasure in large datasets to learn about and improve healthcare.
Joshua Rosenthal, who is “Products Engagement Guru” at Eliza Corporation, develops data-driven approaches and programs that engage people in interactive discussions about their health. He is experienced with handling qualitative and quantitative data. We are proud to have him on our speakers list and excited about what he has to say about big data. Use the chance to talk, discuss and think about healthcare, big data and related topics with experts like him at our conference in a creative and inspiring atmosphere.
Big data + healthcare = lots of open questions and opportunities
The phrase “killer app” commonly refers to applications that demonstrate the core value of a technology. Admittedly, in health care, this term sounds odd but the question remains: What is the “paramedic app” for big data in healthcare? How can we improve healthcare by using technology and incorporating big data? How can we disrupt the broken healthcare system and create a new, better one? Wich legal, technological and organizational issues do we have to deal with in order to use big data effectively and efficiently?