One of the most significant impacts of data analytics in the world today is in the healthcare industry. Not only has it improved the productivity of health practitioners, but it has also improved decision-making and the outcome of patients’ recovery. The application of big data analytics in the healthcare sector is due to its accuracy in decision-making and use of predictive analysis, all of which lead to an increase in revenue.
At Hoick, our squad of big data maestros engineered a stellar 25% revenue surge for a premier Dubai hospital. Navigating the complex web of multi-country operations and a kaleidoscope of stakeholders with diverse demands, they leveraged our cutting-edge analytics tool to supercharge productivity and financial performance.
The healthcare industry is a very sensitive industry that requires a great deal of expertise and accuracy, hence the significance of using big data analytics at this time. Therefore, in this blog post, we will take you through the significant impact of big data analytics in healthcare and how it has brought about a notable transformation in the sector.
Data analytics has improved the healthcare industry in several ways, producing great outcomes for patients and increasing revenue. Following are the benefits of data analytics in healthcare:
Early detection and prevention of diseases: One of the most profound attributes of big data analytics is the detection of small, negligible details. In healthcare, it brings to light minute but significant details in patients’ data that could pose health risks to an individual and, even more importantly, to a large community of people. By analyzing a vast dataset from diverse sources, such as electronic health records and genomic data, healthcare professionals can identify health risks quickly enough to take proactive actions. Not only does this save the cost of medications, but it also saves the lives of patients early enough. For example, predictive analytics in healthcare can detect a potential disease in a patient and the health risks involved. From the data provided by a patient, healthcare analytics can predict how badly a patient’s condition will deteriorate in 6 months, even though the patient may look visibly healthy.
Enhancing diagnostics with predictive analysis: This is similar to the first point mentioned above. There are cases where a patient’s condition suddenly goes bad after going through a recovery process. Though he has been treated, his condition could suddenly grow worse due to an incomplete or inaccurate diagnosis. Medical professionals understand that the human body is dynamic and that mistakes and omissions could happen in diagnosis. Therefore, there is a strong reliance on big data analytics to provide accurate diagnostics and reveal the potential health risks of patients. This then helps them to make the right decisions that make the patient go through the right and total recovery process.
Real-time monitoring and early detection: Big data analytics is also pivotal in monitoring patients’ health in real-time. Some healthcare analytics devices, such as wearable devices, the Internet of Things, and smart sensors, are used to continuously collect patient’s real-time data and create alert schemes in health-threatening cases. For example, patients with life-threatening health conditions could use wearable devices to continuously monitor their heart rate, blood pressure, or activity rate. When there’s a deviation from the normal pattern that is expected for safer health status, healthcare professionals are alerted to give prompt attention to the case and prevent further health complications. Also, the real-time data collected could be further analyzed to predict future complications that may arise.
Personalized medicine and tailored treatment: The one-size-fits-all model of prescribing medication to patients is now primitive and less effective. Using big data analytics allows medical professionals to tailor medications according to patients’ uniqueness and personal data, which includes genetics, gender, medical history, and more. By analyzing a large set of data that includes a patient’s uniqueness, researchers and healthcare professionals can identify genetic markers that influence a patient’s response to medications—which may not be effective on other patients. This scheme reduces trial and error in prescription, which could be detrimental to the health of the patient.
Obviously, the impact of big data analytics in healthcare is evolving. More researchers are dedicating their lives to the advancement and use of technology in healthcare. Big data analytics in healthcare is undergoing a revolutionary change that will advance precision medicine, where patient data from each individual patient will seamlessly merge to provide individualized care. Improved interoperability will make it easier for data to be exchanged efficiently across various healthcare systems, while AI and machine learning will advance predictive analytics by predicting health patterns more accurately.
The role of big data analytics in the healthcare industry is something to marvel at. From improving the decision-making process to improving patient outcomes and industry revenue, data analytics has made a huge impact. It has also helped in predictive analytics, which solves major health complications and sudden deaths of patients due to poor diagnosis. As technology continues to evolve, the healthcare industry is not left out; tireless individuals continue to make new advancements and discoveries to ensure the preservation of life and the growth of the industry.
