Organizations have been generating data for decades; even now large amount of data is being generated daily. Big data is the term to represent such data although there is no specific definition for it. Usually big data refers to large amount of data which may be structured or unstructured and comes from different sources. For efficient business operations and profitability, organizations choose big data consulting to reach better decisions. 

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

Need of Big data in Healthcare 

Physicians now-a-days rely more on patient’s clinical health record which means gathering large amounts of data from different patients. Surely, this cannot be easily handled with traditional data storage technologies.

There are large amounts of data coming from healthcare systems, either from billing systems or from EMR (Electronic Medical Records). There is certainly a large variety of data coming from different sources, in different formats. This drives the need for big data approach in healthcare.

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, analyze and manage such huge information to predict the outcomes and make possible decisions. 

Medical data is generated from different sources and 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 needs good medical care. We rely on doctor’s medical expertise and we think what they decide for us is the best. But have you ever thought how difficult it would be for them to analyze the patient’s entire history? And, what would be the challenges involved in making proper treatment decisions?

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

2) Electronic Health Records (EHRs)

The volume and details of patient records is increasing rapidly. There is a need for adopting a new approach. Most hospitals have moved to Electronic Health Records (EHRs). This necessitates the application of big data in healthcare considering the sheer volume of patient data. 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 for each patient. It can also 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 offering better treatments to their patients by constantly monitoring their health in real-time. Many tools are there which analyze 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. This in turn will reduce the patient’s unnecessary visits to the clinics. 

4) Prevention of Unnecessary ER visits 

Hospitals want to reduce the number of ER (Emergency Room) visits by patients. Study proves 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. It could be due to kidney stone or appendicitis or something else. Now if the doctor can know 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 example, the records of the patients are shared with the emergency departments. And, they would know if the patient has already done some tests at other hospitals. They can also know what advices were given to the patient earlier. This reduces the time to get the details of previous tests and avoids unnecessary formalities. 

5) Big data can help cure cancer 

Cancer is a complex disease where a single tumor 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. In turn, they can 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 securely. 

For example, a patient’s tumor samples can be examined with their other treatment records. And, it 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 applied to other diseases as well. 

These are some of the ways in which Big Data is impacting healthcare. With the use of advanced big data analytics, healthcare providers can help improve patient outcomes, while lowering the costs.