How data analytics is transforming the Health care industry
There is an estimated 50 Petabytes of data in the health care realm, predicted to grow to 25,000 Petabytes by 2020, reported by a new info-graphic from Oracle. From this astonishing data report, we can see that the healthcare industry is generating a huge amount of data, driven by clinical records, medical care and compliance & regulatory requirements.
Luckily, big data analytic application has been widely used in the health care industry to extract insights from the wealth of data. Being able to accurately identify the association, trends and patterns had empowered such data analytic techniques to save more people’s lives and lower their medical care costs. These large amounts of data has been extensively applied to support a wide range of health care services, including clinical decision, population management, disease detection, real time statistical analysis, pharmaceutical research, etc. Thus, to know more about how data analytics is working while implementing a health care program will spur a sound and well-rounded health care development.
With the wild expansion of public health information, we can use data analytic technique to crawl and filter out varied types of public health info data. Thanks to the data analytic methods, medical workers are able to manage large amount of unstructured data and then explore the insights from these data. Note that there are multiple channels to collecting population health information. Officially, lots of medical data now comes from the hospital information system (HIS), which includes electronic medical record system (EMRS), laboratory information system (LIS), picture archiving & communication system，(PACS), radiology information system (RIS), clinical decision support system (CDSS), etc. Apart from these data sets, many other medical care appliances can also help to record the life symptom information, like ECG data, blood oxygenation, blood pressure, pulse, and body temperature. You can even get health information from some social media platforms or search engines. All of these data can be helpful for medical workers or researchers to make a meaningful therapy decision.
As known to all, Google has successfully predicted the influenza A (H1N1) outbreak almost 2 weeks earlier ahead of US Centers for Disease Control and Prevention (CDC) in 2009. It was the big data technique that Google had used to crawl the relevant searching results from its users, and detected the outbreak of the influenza. More specifically, there are two categories of gleaning infectious disease information: positive collection and passive collection when concerned about the user data.
