Big data is every organization’s new best friend. And in recent years, it has taken the healthcare world by storm. According to a 2022 report, the global market for this technology has reached $34.27 billion.
By collecting and analyzing large amounts of data, healthcare providers can identify trends and make predictions that can improve patient care. But how does big data analytics work, exactly? There are four main steps in using big data analytics in healthcare, and below is a closer look at how each one works:
Data Collection
Collecting as much data as possible is the first step in every data analytics. This information is the necessary raw material for big data analytics. And without this data, there can be no analysis.
There are many sources of data in healthcare, including electronic health records, claims and billing data, clinical trial data, patient surveys, and social media. Data can also come from devices like wearable fitness trackers and apps that patients use to track their health. However, the most popular source of information is market research.
Many health organizations opt for market research recruitment services to obtain their sample. In this process, a market research firm works with the healthcare organization to identify the ideal respondents to help meet their goals.
Through this process, healthcare organizations can collect data from thousands of patients in a short amount of time. It helps them to identify trends and make predictions. As a result, they can improve patient care and make better decisions about their health.
Data Cleaning
The next step is to clean the data. It involves identifying and removing any inaccuracies or inconsistencies. This step is crucial because it ensures that the data is accurate and can be used to make reliable predictions.
The process works by identifying the source of the error, correcting it, and then ensuring that the data is consistent across all sources. Many times, data cleaning is done by hand or manually. However, there are also software programs that can automate the process. It might seem time-consuming, but ensuring the information is usable is essential.
With data cleaning, healthcare organizations can be confident that the information they are using is reliable. This way, they can avoid making false decisions that could jeopardize patient care.
Data Analysis
Once the data has been collected and cleaned, it can be analyzed to identify trends and patterns. It’s the most critical step in the process because it allows healthcare organizations to make or break predictions.
There are many ways to analyze data. The most common method is to use of statistical analysis. This approach looks for relationships between variables and uses them to make predictions. For example, if a healthcare organization wanted to predict how many heart attacks a patient might have in a year, they would look at the patient’s age, weight, and cholesterol levels.
Other data analysis methods include data mining, machine learning, and predictive modeling. These techniques are used to find hidden patterns in the data that can be used to make predictions.
Either way, healthcare organizations can benefit from analyzing their data. By understanding the trends and patterns, there’s more chance of making an accurate prediction. It can help to improve patient care and make the healthcare system more efficient.
Decision-Making
The final step is to use the insights gained from the data analysis to make decisions that can improve patient care.
For example, suppose a big data analysis predicts an increase in hospital admissions for a certain disease. In that case, steps can be taken to prevent or treat the disease before it becomes a problem. This step is essential because it’s the only way to put the data to use. Without taking action, all the work that went into collecting and analyzing the data would be for nothing.
There are different levels of decision-making, from small changes to large-scale initiatives. The important thing is that the decisions are based on data, not guesswork. With this approach, healthcare organizations can be confident that they are making the best decisions for their patients.
By understanding how big data analytics works, healthcare providers can make better-informed decisions that improve patient care. Big data analytics is transforming healthcare by helping providers to understand trends and predict potential problems. As more and more healthcare organizations embrace big data analytics, the quality of patient care will continue to improve. So, to improve your healthcare organization, start by understanding how big data analytics can help. It’s the first step to making better decisions that will enhance patient care.