Big Data – the technology of processing massive datasets is now widely used in healthcare for the past few years. Initially, large organizations were using this technology to analyze their raw data for making accurate business decisions, but now, its impact on healthcare is extraordinary.
With this technical advancement, hospitals are using ML and big data-like technology to process data and get useful results through pattern recognition, predictive analytics, and many other ways. The worldwide big data market in healthcare is projected to reach $9.5 billion by 2023 at a CAGR of 11.5 percent.
Here are the valuable services big data provides to this sector:
Electronic Data Maintenance:
The biggest advantage of big data analytics can be seen in Electronic Health Reports (EHR) maintenance. EHR contains patient data, including check-up reports, historical data, allergic reports, lab tests, etc. This information is shared across a secure communication channel with the healthcare sectors to provide them with useful insights from patient data.
Doctors can make necessary changes to this data according to their research or tests performed on the patient- eliminating extra paperwork. They can also monitor patient activity through EHR to check whether a patient is taking doctors’ suggested prescriptions.
For a long-term goal, a few organizations implement advanced blockchain technology in healthcare to maintain security and log this data to be used as valuable input for future research.
Predictive Analytics:
The role of technologies like IoT, machine learning, and big data in healthcare has increased drastically in the last decade, playing an essential role. The billions of sensors used in hospitals throw a significant amount of data every minute.
This unstructured data is analyzed with big data technology to organize it effectively. Further, the machine learning models are trained with this data to get valuable insights such as patient behaviour, which helps doctors decide.
Machine learning algorithms are beneficial in predictive analytics- once the data is organized. Whether it is waste management or inventory, or patient care- this data utilizes well. This technology of designing intelligent models can be easily understood with a Machine Learning Course, where you can go through the development of various complex models for accomplishing such works. These models are trained with patient data to provide insights for decision-making and various other tasks such as drug discovery, pattern recognition, etc.
Healthy Lifestyle – Patient Engagement:
Modern healthcare innovations have introduced smart gadgets such as wearables, which are very useful for providing health-related data. These smart devices provide information like heart rates, which are further used for monitoring disorders in people, resulting in identifying the disease.
One good thing about such devices is- a few patients show interest in wearing these gadgets. Thus, they are involved in their own health monitoring activity leading towards a healthy lifestyle without making any weekly clinical visits.
Strategic Planning:
Big data technology helps healthcare specialists make strategic plans based on insights from patient data. Doctors can analyze the report from various locations to identify the obstacles patients face in their health care. Thus, they can deliver better hospitality to patients from various remote locations.
Real-Time Patient Monitoring:
Both public and private hospitals use real-time data analyzed from various sources to provide useful details about the patient to doctors. The clinical decision support applications organize the data in real time and assist doctors in making prescriptive decisions instantly.
This way, doctors can manage treatment remotely, eliminating costly treatment at home. Since all this information is stored in the cloud, it can be easily accessed anywhere, anytime. Apart from the cloud, a few organizations are using edge and fog computing-like technologies for the devices used to collect data. Through it, they can eliminate unnecessary information at the network node, saving the extra cost for storage and effort pushed by big data for organizing this raw or unstructured data.
With the help of historical data, doctors can tell about the delivery and status of a patient. In some cases where instant action is needed, such as sudden fluctuation in the heart rate of a patient, the administrator is informed quickly to take positive steps to control this scenario.
Drug Discovery:
Our medical centers are still involved in performing higher and more advanced academic research to recognize the cure for various fatal diseases such as cancer. Incredible tools like Watson assist these scientists and researchers in diagnosis, cancer treatment, and finding the cure for such diseases.
And the only requirement is pure data input from historical research, patient treatment, etc. Researchers can find a cure for such diseases and discover new medicines that provide relief to patients by examining this data.
Final Call:
These outstanding works are not limited to hospitals; even big organizations like Google and IBM are helping this sector add a new page in human history by providing solutions to this complex problem through automation. Maybe one fine day, they will find the cure for diseases like cancer, which has taken lives from the human heart.
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