‘Big data analytics’ a widely used term today in our healthcare domain, which take care of nearly everything, any piece of information once it begins its digital life. From flagging drug interactions to predicting sepsis, modeling emergency department use to triggering an automated phone call for a mammogram reminder, healthcare providers are leveraging patient data from the EHR and elsewhere for an astounding array of patient care tasks.
But a worrying number of providers continue to struggle to understand just how huge their big data is, not to mention how to collect and use it most effectively. Organizations are all over the map when it comes to their abilities to use healthcare big data analytics for actionable tasks like population health management and care coordination – many providers are still wrestling with how to get basic patient into their EHRs efficiently, let alone forestall a preventable hospital readmission three months down the line.
What has happened?
Big data is a platform for importing, storing and analysing data to uncover information not previously known. This explosion of the data, changing the way people think about everything. From the cutting edge scientific research to the monetization of social media and exchanging the way people think about healthcare analytics too. However, the health care has not kept pace with big data.
The large Indian health care system needs to harness healthcare’s “big data” and analyse a complex set of data, including electronic medical records and sensor data. This enables clinicians to access and analyse healthcare big data to ascertain quality, determine best practice, assess treatment strategies and identify patients at risk. The promises and potential of Big Data in transforming digital government services, governments, and the interaction between governments, citizens, and the business sector, are substantial. From “smart” government to transformational government, Big Data can foster collaboration; create realtime solutions to challenges in agriculture, health, transportation, and more; and usher in a new era of policyand decision-making.
Big Data raise a large number of information management issues, primarily in the areas of privacy, security, accuracy, and archiving, spanning major issues such as personally identifiable information, security of government data and information, and the accuracy of publicly available data. By fostering collaborations and economic development through private-public partnerships, government agencies appear to be tacitly endorsing the privacy, security, and other policies employed by those private sector entities.
What’s probably going to happen?
We live in the age of big data. The amount of data created in the world up to and including 2005 is now created every two days. Big data is a platform for importing, storing and analyzing data to uncover information not previously known. This explosion of the data changing the way people think about everything. From the cutting edge scientific research to the monetization of social media and exchanging the way people think about healthcare analytics too. However, the health care has not kept pace with big data. Big Data Healthcare is the drive to capitalise on growing patient and health system data availability to generate healthcare innovation. By making smart use of the ever-increasing amount of data available, we find new insights by re-examining the data or combining it with other information. In healthcare this means not just mining patient records, medical images, diagnostic reports etc., for insights, diagnoses and decision support device, but also continuous analysis of the data streams produced for and by every patient in a hospital, at home and even while on the move via mobile devices .
Even today the majority of health care analytics is performed by doing monthly data refreshes in relational databases that produce pre-processed reports. A fair gap is often missing lab test is often 45 days old, as the data flow move from batched data fields to real time fields from transactional systems and streaming data from analytical modelling devices. This old model of analytics will fail. Analysis will need to be done on that spot moment not in the pre-processed form. Data refreshes need to be done in real-time not once in a month. The data analysis tools of today are likely yellow pages phone book in the era of Internet Search Engine. They are becoming more obsolete with each passing day. The traditional health care analytic tools are built on tools developed by IBM in 1970, more than 40 years ago. If all the three parties (payer, provider, pharmaceutical company) work collaboratively and share data/insight, disease management programs will become cost-effective and deliver improved patient outcomes at a scale that will further optimize overall health-care cost structure.
The term “e-health” defined by WHO: “ a new term used to describe the combined use of electronic communication and information technology in the health sector”. e-health is the main driver for three significant changes within the health care environment
- Patients to become better informed
- Patients to become more active and empowered in their health care
- Healthcare to become more efficient.
It is referred in the Cognizant 20-20 insights by Cognizant, Big data solutions attempt to cost effectively solve the challenges of large and fast-growing data volumes realize its potential analytical value. For instance, trend analytics allow you to figure out what happened, while root cause and predictive analytics enable understanding of why it happened and what it is likely to happen in future. All healthcare constituents – patients, payers, providers, groups, researchers, governments etc. – will be impacted by big data, which can predict how these players are likely to behave, encourage desirable behaviour. These applications of big data can be tested, refined and optimized quickly and inexpensively and will radically change healthcare delivery and research. The healthcare domain has been an easy target for people who seek easy money by using fraud methods. Healthcare fraud is expected to continue to rise as people live longer. The white paper by trend analytics reveals that healthcare fraud prevention has resulted savings of nearly $4.1 billion in 2011. A big data platform has ability to sift through a huge amount of historical data in relatively shorter amount of time, so that the business transactions can use fraud detection on real time. Though, the big data analytics in healthcare plays a crucial role to provide better health care services, provide analysis on the historical data to uncover hidden information, the big data analytics has the challenges like Heterogeneity and Incompleteness of data, scale, timeliness, privacy and Human Collaboration. The future research is all about to overcome the challenges and use big data analytics in healthcare to uncover the knowledge from the raw unstructured data.
Making the future work for you
As the Internet of Things creates a new way of looking at health information and machine learning advances and algorithms become almost unnervingly sophisticated in their ability to calculate the behaviors of nearly everything, from consumers choosing products at the grocery store to the minute movements of the stock market, the healthcare industry has an enormous opportunity to take advantage of these decision-making abilities.
The future of prescriptive analytics is nearly unlimited in its scope and depth as developers dream up the technologies of the future. While too many healthcare providers are still trying to claw their way out of locked rooms of unusable EHR data, an industry-wide push towards viewing healthcare big data analytics as the answer to so many critical questions is accelerating the development of an infrastructure capable of becoming the foundation for prescriptive analytics and truly meaningful advances in the quality, timeliness, and effectiveness of patient care.