Big data analytics has changed how we manage, analyze, and leverage data across industries. One of the most notable sectors where data analytics is making significant changes is healthcare.
When implemented correctly, data analytics in healthcare can reduce treatment costs, offer a comprehensive insight into patients and conditions that affect or have the potential to affect them, and improve the quality of life in general.
Healthcare data analytics combines historical and real-time data to predict trends, reveal actionable insights, achieve medical advances, and drive long-term growth.
As per a report by Allied Market Research, the global healthcare analytics market size is projected to reach $96.90 billion by 2030 from $23.51 billion in 2020, at a CAGR of 15.3%. Various factors such as the need for laboratory automation and efficiency and the rising prevalence of chronic diseases have led to a surge in the adoption of big data in the healthcare industry.
Apart from the aforementioned factors, healthcare organizations are now turning to big data analytics to cut down overspending on poor stock management, patient care, and staff deployment.
In this article, we are majorly going to focus on the ways data analytics is helping medical institutions and professionals assist with patient care and reduce costs.
But before moving forward, let’s dive into the main types of healthcare analytics and how they can drive a change in the healthcare industry.
What are the types of data analytics in healthcare?
There are four types of healthcare analytics that can be applied based on the goals and needs of healthcare professionals and institutions. Each type is crucial for making the most of healthcare data, depending on the situation.
Descriptive analytics: Descriptive analytics provides a historical view of data that allows healthcare providers and management to determine if current practices are efficient and make recommendations, if necessary.
Predictive analytics: Predictive analytics uses modeling and forecasting to determine what is likely to happen next. It helps healthcare providers to calculate risk scores for each patient and identify which patients may require additional attention. However, predictive analytics does not indicate preventing adverse events such as hospitalization.
Diagnostic analytics: Diagnostic analytics helps understand why something happened so that actions can be taken to address the problem. It is useful in figuring out what events and factors led to a specific outcome. Just like descriptive analytics, diagnostic analytics also involves an investigation of historical data.
Prescriptive analytics: This type of analytics allows us to understand what actions are needed to change the prediction. It uses advanced algorithms to help determine the effects of specific actions and provide solutions by applying historical data to get a specific result. It is the most useful and powerful type of analytics, providing guiding recommendations to healthcare professionals.
Top ways to reduce costs in healthcare using data analytics
Applying data analytics and visualization in the right way can lead to an increase in patient access to services, resulting in lower costs, more revenue, and improved patient satisfaction. In this section, we will delve into different ways to reduce costs in the healthcare sector using data analytics tools and solutions.
