Exclusive Article By Dennis Hung at EMRIndustry
“Big data” is a term you hear a lot. Data-driven companies boast about how much information is collected and what they’re able to learn from it. Every business hopes to analyze its growing heap of information for new insights.
But what exactly is big data, and how can it benefit healthcare?
Traditionally organizations handle data by entering it into relational databases and querying it into aggregated reports. Big data means not only high volumes of information, but a variety of different data types, and better and faster ways to use it.
Growing Healthcare Data
People may think of big data as figures amassed by big banks and international corporations. But as the population ages more healthcare data is collected. The Administration on Aging predicts there will be 98 million Americans considered elderly by 2060.
Unlike data in other institutions, a patient’s medical history is relevant their entire lives, and perhaps beyond. Hereditary factors are relevant to their offspring, but patient data may be of value to medical research and insurance actuaries for many years after they’re gone. A patient with numerous medical problems amasses a sizeable file, covering each office visit, procedure, and prescription, along with all the attendant symptoms, test results, and billing information. Along with all that data comes the demand for more and better software and hardware to contain, preserve, and process it over coming decades.
Healthcare – a Need for Speed
As fast as patient information can accumulate, there is also the need to use it in a timely manner. Life and death can often depend on a speedy and accurate diagnosis, prescription, or procedure. New tools for analyzing reams of data, or a single patient’s history of symptoms, are always welcome. Medical research certainly requires a large amount of data, often taken over a span of years, and that this data have a high level of integrity, accuracy, and relevance. Very often that data must shared with other facilities and specialists, and checked and re-checked for it is of any real value. None of this can be accomplished with anything like efficiency without the assistance of big data-oriented hardware architecture and analytic software.
Variety of Information
There are three major types of information in healthcare, each quite different: billing, clinical, and diagnostic imaging. Finance is perfectly suited to big data analytic models, as is clinical information such as demographics and specific conditions and symptoms, which can all be categorized for analysis. Digital image files are rather different; there is no easy way to collect information from images until they have been evaluated and codified by medical professionals. Special data formats, protocols, and network architecture are being developed and implemented throughout healthcare just for storing and sharing diagnostic images. Management of images has led to development of radiology PACS (picture archiving and communication system) and similar systems to accommodate this data. As techniques for cataloguing and evaluating images are streamlined they become more and more an essential part of healthcare’s big data.
Data Insights
The whole purpose of analytics and big data is that it can discover insights that create advantages, market insights, and resultant business value. Healthcare has been relatively late to the use of analytics, but the potential has always been there. The ability to improve patient interaction, time management, and financial benefits are important considerations. Insights into medical care could wind up saving thousands of lives. Predictive models could forestall the next epidemic or provide direction on hereditary diseases.
None of this may be possible without big data and analytics. Fortunately for us all, the healthcare system is moving in the right direction.
