With the increased penetration of EMR and EHR softwares in the Healthcare industry, there has been an explosion of patient data regarding their conditions, treatments, results and treatment outcomes. We now have access to a lot of health data history, which if analysed and processed, give us a greater understanding of the body’s mechanisms. Having a treatment plan that is backed up by such a massive amount of data will lead to improved patient care and better treatment outcomes. But the problem is that the data is not organised in way that would make sense to us. And due to the sheer amount of data collected, normal means of data processing prove ineffective, when we want to derive findings or identify patterns.

This is where Big Data comes into the picture.

What is Big Data?

Any set of data that is too large to be effectively processed by traditional data management systems falls under the category of Big Data.

How can Big data help?

Better healthcare delivery

With Big Data’s processing power, collecting and curating massive amounts of healthcare data will be easier. We will finally be able to analyse large sets of patient parameters together, and see if any patterns can be observed.

Personalised Healthcare

These patterns can give us a greater understanding of the general patient population, as well as the state-of-health of individual patients and how they react to specific treatments. This will help medical professionals offer a personalized healthcare plan to patients – imagine using a patients medical profile to identify similar cases from throughout the world, and prescribing treatments or medication based on those findings. It can even go up to the extent of identifying patients who are at risk of a disease, based on their parameters.

Genome mapping

Genome Mapping has been very expensive in the past due to the sheer amount of data involved. But Big data costs have gone down, and sequencing a person’s genome digitally has become cheaper. Once a person’s genome is accessible by a clinical analytic system, it can reveal what diseases they are genetically vulnerable to, and will factor in genetic predisposition while picking out the most effective treatment.

Lowered Costs

With the healthcare costs increasing everyday, Big Data’s push for efficiency will mean that it will be a big driver in the future to lower costs through:

Predictive diagnostics:

The Collaborative Assessment and Recommendation Engine (CARE) was built by Nitesh V. Chawla and his student Darcy Davis at the University of Notre Dame. CARE can take on a large amount of health population information, and identify patterns and similarities to generate a personalized disease risk profile for individual patients.

Dr. Chawla stated in the Notre Dame News that, “ … CARE rankings can provide reminders for conditions that busy doctors may have overlooked … CARE can be used to explore broader disease histories, suggest previously unconsidered concerns and facilitate discussion about early testing and prevention, as well as wellness strategies that [are] more familiar with an individual and are essentially doable.” Systems like this can potentially alert doctors about diseases even before the patients begin to suffer from them.

Preventive health care:

Armed with information about impending diseases, doctors will be in a better position to recommend treatments to counteract those early symptoms, and help them address the root cause of the problem. This has potentially life-saving implications while treating heart attacks or infections, which helps doctors to prevent existing conditions from worsening and such instances from occurring in the future. This will help increase the quality of healthcare delivered, while reducing the amount (number of patients visits or treatments performed) at the same time. This will help drive down costs immensely.

Big data in India

Over the past decade, there has been an increased use of EMRs to capture medical data. But industry experts still note that we lag far behind in EMR-adoption. A large number of medical practitioners still use the old pen-and-paper methods for medical records, which isolates and make the data hard to be shared or analysed.

Only if collecting medical data like treatment plans, diagnoses and clinical observations is digitised will it be of any use to a Big Data platform. It is up to the medical practitioners of India to recognise the benefits that this will offer in the future, and make sure that they are ready for it.