Organizations have been generating data for decades; even now large amount of data is being generated daily. Big data is a term often used to represent such data. Well, there is no particular definition for it, but usually it refers to large amount of data which may be structured or unstructured and comes from different sources. For efficient business operations and profits, big data is analysed carefully by the organizations to reach better decisions.

Currently, this technology is being used in wide range of areas but one of the areas where it can bring a hugechange is healthcare.

Need of Big data in Healthcare

Of course, it comes to our mind that why there is a need of big data in healthcare systems, well there are some reasons-

The physicians now-a-days rely more on patient’s clinical health record which means gathering of large amount of data, that too for different patients. Surely, this cannot be easily done with old techniques of storing the data.

There is large amount of data coming in from healthcare systems either from billing systems or from EMR (Electronic Medical Records). There is certainly large variety of data coming from different sources, in different formats driving the need for big data approach to tackle all this.

Challenges towards data-driven Healthcare

Health systems today generate lots of data from different sources such as laboratory tests, clinical notes, patient’s reports, etc. The real challenge is how to collect, analyse and manage such huge information to predict the outcomes and make possible decisions.

Medical data today is spread across different sources governed by different hospitals and departments. So, there is a need for the development of new infrastructure which can integrate all the data from such sources.

Real Life Examples:

1. Predictive Analytics in Healthcare

Everyone is a patient at one time or the other and all need good medical care. We believe doctors are medical experts and what they decide for us is best. But ever thought how difficult it would have been for them to analyse the patient’s entire history and make proper decisions for their treatments?

Predictive analysis leads to patient’s safety and quality care. It keeps doctors informed about the patient’s medical histories and helps predict results for future. For example, the analytics tools would be able to predict which patient is at risk of what disease, so to make decisions accordingly to improve patient’s health. Predictive algorithms using different programming languages can be created to predict the health of a patient over time.

To improve the healthcare systems, a US Research collaborative, Optum Labs collected data of over 30 million patients to create a database for predictive analytics tools that will improve healthcare systems.

2. Electronic Health Records (EHRs)

The volume and details of patient’s record is increasing rapidly and there arises the need of adopting a new approach. Many hospitals have moved over to use Electronic Health Records (EHRs) which is the main application of big data in healthcare. Every patient has his/her own medical records such as laboratory tests results, medical reports, lists of medicines, etc. EHRs make it easier to maintain the data and have access to such data.

A separate file or record is maintained of each patient that can be easily modified time to time by the doctor and these records can be shared safely.

3. Real-Time Monitoring

Healthcare Systems are looking forward to offer better treatments to their patients by constantly monitoring their health in real-time. Many tools are there which analyse the data of the patient and advice the doctors to take respective actions. For example, new wearable sensors can help track patient’s health trends that can be monitored by the doctors. They can be helpful from tracking blood pressure to other illnesses right at home, which in turn will reduce patient’s unnecessary visits to the clinics.

4. Prevention of Unnecessary ER visits

Hospitals want to reduce the number of ER visits or Emergency visits of patients. They believe that it increases healthcare costs and sometimes does not lead to better outcomes for patients. For example, a man suffering with acute abdominal pain comes to an emergency room. The doctor will try to figure out the cause of the problem such as kidney stone or appendicitis or something else. Now if he has a way of knowing the patient’s past medical results, he could begin the treatment as soon as possible. The examination would take less time and would also cost less money.

For this, Almeda county hospitals in California, USA planned to create a program which called PreManage ED. According to this program, the records of the patients are shared with the emergency departments such as, if the patient has already done some tests at other hospitals or earlier what advice were given to the patient. This reduces the time of patient to get the details of previous tests to them and do unnecessary formalities. This is indeed a great application of big data analytics in healthcare area which saves both time and money.

5. Big data can help cure cancer

Cancer is a complex disease where a single tumour can have billions of cells. Hearing this word, we think that it can be cured only at hospitals and not at computer rooms. Well, medical researchers can use analytics to see the recovery rates of cancer patients and the treatment plans to find the treatments that have highest rates of success for this disease. To make this successful, patient’s database from different health institutions need to be linked up keeping in mind confidentiality of patient’s data.

For example, patient’s tumor samples can be examined with their other treatment records which in turn, will help researchers to carry the treatment accordingly. Finding such trends will lead to better results. This approach is not just limited to cancer but can be used to other diseases as well.

These are the few ways in which big data analytics is having impact on healthcare. With the use of advanced big data analytics, healthcare providers can help improve patient outcomes, while lowering the costs at the same time. If you are a startup enthusiast in this space, these are some core areas where new services can be offered.

(This article was authored by Research Nest’s technical writer, Akshita Kapoor)

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