Big data analytics in healthcare?

Big data analytics in healthcare?

Big data has changed our lifestyle, the way we manage, analyse and leverage data in any business organisation. One of the biggest application of big data can be seen in the field of health care. health care analytics have huge potential to reduce the cost of treatment, predicated avoids the harmful diseases, and improve the quality of life in advance. read more about Big data analytics in healthcare?

Average human life period is increasing along with the rapid increase in the world population. Health professionals such as doctors, health entrepreneurs and data scientist have the large capacity to collect a large sample of data and look for the best strategies to use those optimised data.

The application of big data analytics in health care has lots of positive outcomes in human lives. Big data refers to large quantities of information created by different digital sources such as cell phones, computers, etc.

Then they get modified, analysed and optimised by different tools and technology for better predictions. Applied to health care, big data will use specific health-related data of the population and help to prevent various diseases and find better medicine that will be useful in future.

Big data has changed the flowchart of the treatment. Doctors want to understand more and more in details about the patients and as early in their life a possible. They also want to understand the possible warning signs of serious illness and treating them in the early stage and charge less for the medicine to the patients.

This is the business organisation attempt to solve various problems related to patients data- Collected every bytes and byte and archived in hospitals, clinics, laboratory etc with the possibility to communicate properly. Indeed for many years, a huge amount of data have been gathered for the medical purpose which is very costly and time-consuming. With advanced technology, it becomes easier to collect data and convert it to relevant insights, that can be used for various cases. This is the importance of digital healthcare data analytics- using data-driven methods to find and solve a problem before it gets too late.

Why do we need Big data analytics in health care

There is a big need for big data in healthcare because of the rapid increase in the population in countries like India. According to one report, after more than 15 years of steady increase, health care now costs 10 per cent of the GDP and this percentage is going to increase in a few years.

In other words, we can say that costs are much higher as they should be they have been rising for the past 15 years. Hence, we are in need of some smart technology that can change the way of thinking. As several authors have argued that financial incentives matters and other incentives that prioritise patient health over large amounts of patients are good.

Some health care providers have no direct incentives to share patients data for the research and another purpose which creates problems for analytics. Now that many of them are getting paid for the basis of patients data, this creates a better way to improve the lives of patients while cutting the cost of several other insurances companies. Finally, physician decisions are becoming better and better day by day, meaning they have a large amount of data for clinical and research purpose. In many organisations, data collection and optimisation is huge and professionals need help in every matter. This means there is a rapid demand for big data analytics in health care facilities.

Big Data Applications in health care:

Patients predictions for improved staffing and proper management

Let?s consider a very practical question that every shift manager faces in every hospital- How many people do I assign as my staff at any given period of time? If they put too many workers, they would face the risk of having unnecessary labour cost. Too few workers will results in poor service- which is not good for the hospital reputation and it is fatal for the industry. Big data is helping to solve this problem.

Few hospitals have been using data for a variety of purpose to come up daily and monthly predictions on how many patients are expected to visit the hospitals. One of the key application is deduced by using big data analytics using time-series analysis. This analyses optimises the data and predict the admission rates of the patients in any hospital.

Electronic Health Records

Big data has a wide application in every field of medicine. Every patient has their own medical records which include laboratory tests, medical history, allergies, etc. These records are shared via different electronic modes and are widely available for public and private enterprises. Every record comprises of multiple files, which indicate that doctors have made several changes over time and no further paperwork is required when they visit the same doctor again.

Electronic health record provides warnings and safety precautions when the patient visits for new laboratory tests and take prescription when they see the doctor again. Many hospitals have implemented EHR concepts for their patients They have fully implemented the system called HealthConnect through which they can easily share all the necessary data across all the facilities and easier to use ERS.

Using health data for strategic planning

The application of big data analytics in health care allows for strategic planning for patients records. Acre management can easily analyse checkups results among the specific group in the different demographic group and identify what factors are affecting the lifestyle of the people. Many universities use Google Maps and local health data to prepare different heat zones at targeted issues such as population growth and another harmful disease. Additionally, they compare this data with the availability of medical services in those areas and these insights help to modify their delivery strategy.

Real-Time Alerting

One of the biggest examples of big data analytics in real-time alerting. In hospitals, Clinical Decision Support software is used to analyse medical data on spot, providing health practitioners to advise their staff for different requirements. Generally, Doctors wants patients to stay away from the hospitals so that they can make house visits and charge more. Many big data software have wide potential to become a part of this new strategy.

Different devices are capable of collecting the medical and health record of their patients. Also, this information can be easily accessed through the database on the state of the health of the general public, which allows doctors to compare these data and modify accordingly. Clinics and hospitals will use advance and sophisticated tools to monitor such massive data.

For example, if the patient sugar level is increasing alarmingly, the system will send an alert in real-time to the doctor and they will take the necessary action and administer precautions so that they can lower their sugar level.

Increasing patients participation

Many people have an interest in smart devices that record every step, heartbeats, pulse rates, sleeping habits etc. on a permanent basis. All these information are stored in the cloud. This vital information can cope up with other data so that harmful disease can be easily identified. Patients are directly involved indirectly monitoring their own health with the help of these devices. Some advanced devices come with special features such as tracking specific health trends and storing them to the cloud so that physician can monitor easily. Patients suffering from asthma and flu could directly get benefitted from them.

Big Data might just cure some harmful disease

Another interesting application of big data in health care is the treatment of various diseases. Many hospitals and institutions came up with several programs that have the goal of accomplishing Malaria free India or Tuberculosis free area by 2025. Medical research teams can use a wide amount of data on a treatment plan and recovery rate of different patients have gone to at the top with the highest rate of success across the world.

For example- researchers can examine how sewage and water bodies are affecting the growth rate of mosquitoes and how many different cases have arisen in the past decade related to malarial in that particular region.?These data can also lead to unexpected benefits such as finding an appropriate medicine to cure disease in less time.

But, there are a lot of obstacles in the way, including:

Incompatible data systems. This is perhaps the biggest technical challenge, as making these data sets able to interface with each other is quite a feat.

Patient confidentiality issues. There are differing laws state by state which govern what patient information can be released with or without consent, and all of these would have to be navigated.

Simply put, institutions which have put a lot of time and money into developing their own cancer dataset may not be eager to share with others, even though it could lead to a cure much more quickly.

Predictive analytics in health care

We have already seen the use of predictive analytics as one of the biggest Business Intelligent technology which has potential application not restricted only to the business but also further application in the future. Many types of research have concluded that predictive analytics will improve the delivery of care and proper medicine to patients. The goal of predictive analytics is to help doctors to make data-driven decisions and improve patients treatment.

This is particularly useful in cases where patients has went through several operations and medical histories and still suffering from critical diseases. Advanced tools will help to predict the normal problems such as blood pressure increase and risk of diabetes and hence advise the doctors to take necessary health care measures.

Improve security

Some study has concluded that there are maximum chances of data breaching in the industry. The reason is very simple and obvious- personal data is very important in the industry as well as in black markets. Thus, any breach will have severe consequences. Hence, many organisation has started to use analytics to improve security threats by identifying changes in network traffic. Of course, big data plays a large application in security issues. But advances in security such as encryption technology, firewalls, anti-virus software, etc, answer that need for more security, and the benefits brought largely overtake the risks.

Likewise, it can help prevent fraud and inaccurate claims in a systemic, repeatable way. Analytics help streamlines the processing of insurance claims, enabling patients to get better returns on their claims and caregivers are paid faster.


Telemedicine has been present on the market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices, and wearables, has it been able to come into full bloom. The term refers to the delivery of remote clinical services using technology.

It is used for primary consultations and initial diagnosis, remote patient monitoring, and medical education for health professionals. Some more specific uses include telesurgery ? doctors can perform operations with the use of robots and high-speed real-time data delivery without physically being in the same location with a patient. Clinicians use telemedicine to provide personalised treatment plans and prevent hospitalisation or re-admission. Such use of healthcare data analytics can be linked to the use of predictive analytics as seen previously. It allows clinicians to predict acute medical events in advance and prevent deterioration of patient?s conditions.

By keeping patients away from hospitals, telemedicine helps to reduce costs and improve the quality of service. Patients can avoid waiting for lines and doctors don?t waste time for unnecessary consultations and paperwork. Telemedicine also improves the availability of care as patients? state can be monitored and consulted anywhere and anytime.