Data is flooding the planet. Just to put that into perspective, did you know that Forbes reported a 5000% increase in the growth of data between 2010 and 2020? And that is not the end of it. And the importance of data and data analytics really hits home when you become aware of the fact that Netflix uses predictive analytics to influence 80% of movies and TV shows that 100 million of its subscribers selected to watch.
It is important to note that much like how extracting value from natural resources requires significant effort, making data useful also takes some work. Applying advanced analytics allows people to transform raw data into something valuable. Data analytics has the potential to change nearly every industry for the better by taking raw data and converting it into useful information. However, no sector will be more affected by this technology than healthcare, which is constantly changing and evolving. This statement is supported by data according to Grand View Research which claims that worldwide data analytics in healthcare was worth $26 billion in 2019, with a predicted annual growth rate of 7.5% from 2020 to 2027.
What Is Data Analytics And Why It Is Important?
Data analytics is the study of raw data in order to draw conclusions about it. Many of the methods and processes used in data analytics have been automated into mechanical procedures and algorithms that operate on raw data for human consumption.
Data analytics is essential for businesses because it helps optimize their performances. When a company implements data analytics into its business model, the company can then help reduce costs by finding more efficient ways of doing business and storing large amounts of data. Additionally, data analytics allows companies to make better decisions concerning customer trends and satisfaction, which eventually leads to creating new—and improved—products and services.
The Importance of Healthcare Data Analytics
Although it’s possible to gather all the data you need, it is no good if one doesn’t know what to do with that information. Therefore, it is important to have a systematic way of collecting, storing, and analyzing data so that it can be used as an advantage.
In recent years, the gathering of information in healthcare settings has gotten considerably more streamlined. The data not only aids in improved day-to-day operations and patient care but it can also be utilized in predictive modeling. Rather than examining historical data or current information, datasets may now be used to analyze trends and make predictions. They may also be utilized to establish preventive strategies and follow outcomes.
The fee-for-service model of healthcare is on its way out. There has been a significant shift toward predictive and preventive measures in public health in recent years, as consumers have become more interested in value-based medical treatment. This is made possible by data. Instead of just treating the symptoms that present themselves, practitioners are now able to identify individuals who are at risk of developing chronic diseases and help to address a problem before it emerges. Identifying the first causes of an issue, as well as how to prevent it or eliminate it once identified, is a critical component of preventing long-term complications and costly hospitalizations.
Data analytics doesn’t just have an effect on improving patient care outcomes and lowering costs in hospitalization cases, but it can also be used to predict risks of infection or deterioration. A great example of this is the COVID-19 pandemic. The data being collected was analyzed immediately to understand how the virus behaved and spread so that future outbreaks could theoretically be prevented altogether.
What Are The Different Types of Healthcare Analytics?
There are various data analytics methods in healthcare that can be useful in answering the questions of many healthcare issues
Descriptive analytics is a method of data analysis that involves looking at past events to see if there are any similarities or patterns. This form of analysis is best for determining what has already occurred while also allowing a look into the past. By studying the available or gathered data in a dataset, analyzing the data of the symptoms or the other characteristics available can lead to putting forward a descriptive analysis that helps healthcare overall.
Predictive analytics is a method for predicting outcomes by analyzing present and past information. The algorithms created with this approach are best utilized to forecast what may happen in the future. A very good example of predictive data analytics in healthcare is the ability to predict any possible outbreaks by studying medical data over the years and using that to make predictions.
Analytical insights from prescriptive analytics may also make predictions about future events. Machine learning is an important component of this kind of analysis. The data provided can assist in making the best decision possible. With prescriptive analytics, it is possible to arrive at the best course of action that achieves the greatest possible result.
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Why Choose IntoneSwift For Data Analytics In Healthcare?
The requirement for data analytics in healthcare cannot be stressed enough given the current state of benefits that big data analytics can provide this industry with. The growing use of sophisticated analytics approaches and the continued exponential growth in the amount of data available to analyze will significantly impact virtually all aspects of healthcare, including administrative processes’ automation, and the use of artificial intelligence (AI) in diagnostics. And in more diverse areas such as cancer treatment, drug discovery, and disease prediction, data analytics is guaranteed to revolutionize the provision of healthcare treatment.
Intone has stepped up to the task of providing the healthcare industry with the opportunity to access the incredible possibilities that data analytics can afford healthcare organizations with the help of IntoneSwift. A state-of-the-art data integration solution that is trusted by industry pioneers and leaders. We offer
- Generates knowledge graph for all data integrations done.
- 600+ Data, Application, and device connectors.
- A graphical no-code low-code platform.
- Distributed In-memory operations that give 10X speed in data operations.
- Attribute level lineage capturing at every data integration map.
- Data encryption at every stage.
- Centralized password and connection management.
- Real-time, streaming & batch processing of data.
- Supports unlimited heterogeneous data source combinations.
- Eye-catching monitoring module that gives real-time updates.
Contact us to learn more about how we can help you!