Big Data in Healthcare Sector – Revolutionizing the Management of Laborious Tasks
The last decade has witnessed major advances in the amount of data that is regularly generated and collected in almost everything including the human ability to understand, analyze, and use technology. These trends have together resulted in the emergence of the field of ‘Big Data’.
And in the short time since its inception, Big Data has taken the world by a veritable storm, touching every sector from healthcare to marketing in a myriad different ways, improving productivity, contributing to process efficiency, and helping create an environment where innovations thrive and flourish.
Note: Want to find out what all the fuss is about? Head over here for an article on why and what makes Big Data such a big deal in today's world!
Preparing for a career in Data Science? Take this test to know where you stand!
Big Data In Healthcare
At first glance, it may not seem as though the worlds of Big Data and healthcare could have anything in common, or would go well together.
Nothing could be farther from the truth.
More than contributing to an increase in profits and the cutting down of wasteful overheads, Big Data has found widespread application within the Healthcare industry to predict epidemics, cure disease, improve the quality of life, and avoid preventable deaths. As the world’s population continues to burgeon, the quality of life has improved manifold, and as people live longer, the medical and healthcare sectors have had to transform and adapt quickly to cope with newer models of treatment delivery and transmission.
The decisions that are made due to these changes are driven by data. The focus now lies solely on understanding the patient thoroughly, as early in their life as possible, and hopefully pick up any signs of a serious illness early, making treatment simpler.
As technology strengthens its hold on the healthcare sector, the kinds of data sources and volumes that are available for research and analysis have begun to grow at the same pace. Big Data solutions seek to harness these massive, complex buckets of data to obtain more focused knowledge and insights into the world of healthcare. Big Data attempts to make more sense of this information overload and provide better insights from the expanding volumes and sources of data.
The objective it stands by now is to answer profitability, operational, and clinical questions in real-time. Experts say that big data empowers scientists, caregivers, and management to make informed decisions, empowering them with the capability to save lives, decrease costs, and improve efficiency of operations. Big Data also possesses the capability to revolutionize laborious tasks, such as how healthcare professionals gather, store, and transmit patients’ information.
“The current episode-focused, discrete data limits our ability to be as prescriptive as we should be in delivering quality care and empowering our patients,” says Khorey Lisa, Vice President of data management at University of Pittsburgh medical center. “Medicine will get nearer to action once it is prescriptive, precise and predictive. Big Data allows healthcare professionals to concentrate on standardized care and wellness of the population. It does so by simplifying the once laborious tasks.” She says.
Pro-Tip: If the Big Data industry excites you, you may wish to consider professional training in Big Data to add a certification that will place you at the front of the pack looking to make it big in this industry!
Watch free Big Data and Hadoop training video:
1. The Convergence Of Mobile Tech, Big Data, And Healthcare
Smart phones were only the beginning. The introduction of apps have enabled these phones to be used as anything from pedometers to calorie counters. Millions of people today are using this technology to live a healthier.
The wearable device trend, a recent development, has recently caught-on, as devices like Fitbit, Samsung Gear Fit, and Jawbone are beginning to find their way onto the market. These devices allow the customer to track progress of their daily workout and upload data that is compiled alongside everyone else’s.
In the near future, it is said that a customer can even share this data with their doctors, who can use it as a part of their diagnostic toolbox when one visits them with an ailment. Even if there is nothing wrong with a person, huge, constantly expanding databases of information about the state of health will still be accessible to the general public to spot symptoms of problems before they even occur and prepare in advance.
These developments have led to ground-breaking work by the partnerships between medical and data professionals, infused with a craving to predict the future and identify problems long before they happen. A recent example of this is the Pittsburgh Health Data Alliance – that aims to capture data from various sources like insurance and medical records, wearable sensors, and genetic data to paint a complex picture of the patient, in order to offer a tailored healthcare package.
2. The 3 Promises of Big Data in Healthcare
In the healthcare industry, Big Data can be explained by reviewing its basic qualities, commonly called the 3 Vs; Velocity, Volume, and variety.
- Volume refers to the rapid rate of data-growth in the healthcare sector. In 2020, it is estimated there will be more than 44 times more data than there was in 2009. Big data software and techniques work to manage these large chunks of data and turn them into valuable information.
- Velocity represents the frequency at which data is being transmitted and shared. Technologies such as monitoring and sensing devices, social media, and embedded chips – today added in almost every device from airplanes, refrigerators to bodily implants – all contribute to the expanding mounds of available data. And in the healthcare sector, the velocity of data-sharing continues to rise by the day.
- Variety represents the numerous forms in which data exists today. In healthcare, this includes unstructured data in text format, streams of date from monitoring and sensing gadgets, test or email messages, scanned documents, video or audio, and procedures that add to the variety of unstructured healthcare data.
The real thrill of Big Data technology in healthcare is in its promise to transform laborious task into simple, one-man manageable tasks. In fact, IBM chief scientists, and accomplished Engineer, Scott Schumacher, say that these technologies can enable physicians to devise predictive analytics that can promote both, the design of short-term solutions, and sustainable longer-term mechanisms to aid in palliative care.
He goes on to say, “Technologies based on the first V support the once very laborious task of analyzing large volumes of data needed for meaningful statistical and finer-grained customization. The second V delivers the processing promise of big data via predictive analytics tied to real-time measurements. The third V leverages semantic normalization, natural language processing using advanced ontologies, and video and image extractions to bring varied evidence into analytics systems.”
3. The Key Elements
Here are three key elements that are necessary for the healthcare sector to leverage the power of Big Data effectively:
The integration between large sets of data and business analytics to help organizations better understand every possible aspect about customers, citizens, and clients to apply advanced analysis and computation to modify existing strategies or devise fresh ones.
In a similar fashion, linking these heterogeneous data sets securely to identify patterns has the scope to improve healthcare by identifying the right treatment for an individual’s specific set of needs and circumstances.
Generating New Knowledge
The earliest uses of Big Data was to generate new insights through predictive analytics. In addition to the clinical and administrative data, integrating additional patient data and their environment may provide for better predictions and help target interventions to the right patients.
The predictions may also help identify the areas that need improvement both under efficiency and in quality in health care such as with adverse events, treatments, early identification of worse health states, readmissions, etc.
Another critical area of focus is on finding novel, innovative methods for research in healthcare. One reason why healthcare isn’t keeping pace with the rest of industry is that it has, for too long, relied on standard regression-based methods which have their limits. Other industries have moved forward and incorporated new methods like machine learning and graph analytics to gain better and newer insights. But healthcare has now begun to catch-up quite rapidly.
Artificial intelligence techniques like natural language processing have become more mainstream even though they are useful mostly in harvesting the unstructured text data that is found in medical records, social media, or a physician’s notes.
Mayo Clinic is said to have teamed up with IBM’s cognitive computer called Watson. Watson is being trained to analyze clinical trial criteria to determine the appropriate matches for the patients to the studies. The computer may also help in the location of patients that are hard-to-fill’s for trials like those that involve a rare disease.
Transforming Knowledge Into Practice
Though the standardization of data collection and fresh analytical approaches to the big data revolution in healthcare are important, it’s their practical application that will get it across the line.
This is a very necessary and important cultural challenge for those who generate as well as for the ones who consume the new knowledge. It is important for the users to be involved from the beginning. The research team needs to have a clear vision of how the new knowledge must be translated into practice.
The insights that are obtained from big data have the potential to revolutionize multiple areas of healthcare like the comparative outcomes that are achieved with different delivery models, the evidence of safety and the effectiveness of various treatments, and the predictive models for diagnosing, treating, and delivering care. Big Data also has the capability to enhance human understanding of the effects of consumer behavior, which will help companies design the benefits packages.
How Does Big Data Help Simplify Tasks In Healthcare?
Big Data has immense potential to simplify laborious tasks in healthcare settings. The techniques and solutions of Big Data can be used by organizations to engage patients, personalize care, reduce costs and variability, and to improve quality of services.
Once Big Data is well managed and integrated, healthcare facilities will be able to apply analytics and gain insights into the operational status of their business, current trends, and to predict what might happen in future with a trusted level of reliability.
Pro-Tip: If you're already a Big Data professional and are looking to break into the healthcare industry, here is a comprehensive 10-week program on Big Data for Health, offered by the Imperial College, London.
Big Data revolutionizes the following laborious tasks in healthcare:
1. It allows providers to give patients very specific, customized care and follow best practices in the palliative healthcare industry
Healthcare providers have huge volumes of unstructured data in the form of scanned documents, images, progress notes or encounters. Big data solutions allow providers to process these unstructured data in their premature state, correlate them with other data sources, and address priorities based on systematic knowledge and findings.
Priorities include care patterns that help in process modifications, predictive identification of risk elements to avoid sentinel events and untoward outcomes. It also covers comparison of procedures, images, and surgeries to enhance education, research and care.
Wilson Kristen – Jones, VP of Data and Online Services at Sutter Health, explains Big Data as a way of providing the organization with mass personalization principles to healthcare in a manner similar to those applied in consumer product design and manufacturing.
“Big Data will enable traditional procedure and claim data to be integrated with other data sources outside of healthcare to break down information barriers,” Wilson tells. “For instance, data from social media, grocery store purchases, and personal preferences can be incorporated to understand what impacts an individual or population health.”
“These new insights, which are practically impossible to attain without applying Big Data techniques and solutions, can boost health at many intervals” Wilson Jones says. With Big Data Solutions, Best practices, are readily identified, costs and variability decreases and quality care is delivered to patients. It also makes it possible to offer fully personalized care.
2. Payers Leverage Data Pool
Payers have huge volumes of claim data they would wish to harness to provide insights that enhance wellness, fraud detection, patient compliance, and enable early warning to negative patient trends. Whether they are government or private payers, payers increasingly utilize incentive programs to reward better outcomes while at the same time controlling costs.
Many of them also look to utilize social media wellness, patient’s interactions and tools that drive lifestyle changes to reduce costs and enhance care. Big data resources enable payers to execute the laborious task of integrating volumes of different varieties and sources of data to allow diverse initiatives.
3. Research Enabled With Unprecedented Reach
Research that needs integration of huge volumes of data in healthcare has for a long time been underserved due to the infinitely laborious computations that would otherwise be involved. However, with big data solutions, medical researchers can now contextually integrate and correlate huge amounts of information automatically to obtain faster insights.
For instance, the SUNY (the State University of New York at Buffalo) has launched a Big Data Solution to help understand the interlinked and complex causes of multiple sclerosis. This system integrates and analyzes variables such as living, exercise, diet and working conditions, as well as genetic and clinical data. Research of this magnitude used to take hundreds of workers days of computing time, but with big data, it only takes minutes due to the advanced computing power of modern systems.
Real World Examples
For more articles on Big Data and Hadoop, please visit the Big Data aisle in our Online Library of Free Resources.
Get a taste of our Big Data and Hadoop certification course. Here is a 147-second preview of our world-class Big Data training course!
About the On-Demand Webinar
About the Webinar