If you want to effectively gauge and manage the quality of your healthcare benefits program, as well as associated costs and outcomes, you need analytics tools and an insights-based strategy. After all, the ability to predict healthcare performance and analyze program effectiveness can lead to healthier, happier, and more productive and engaged employees. That, in turn, leads to better organizational performance.
Here’s what you should know about improving healthcare with data analytics – and more.
What is Meant by Data Analytics?
This is basically the process of evaluating data to spot trends, derive conclusions, and pinpoint ways to improve.
In healthcare, data analytics is used to gain insights and in making informed decisions. Further, the use of data analytics promotes better and more personalized patient care, more accurate diagnoses, and preventive measures. For organizations, the use of data analytics can mean a reduction in costs as well as simplified internal operations.
Why Healthcare Data Analytics is Important
Yes, data collection in healthcare makes for improved patient care as well as more streamlined day-to-day operations. But what’s more, rather than examining either historical OR current info, both datasets can now be utilized to find trends and make predictions. That means that we can now take preventive steps as well as track outcomes. It also means lower costs all around.
Think about the impact of healthcare data analytics on the pandemic. Scientists can, in real-time, analyze data to better understand COVID-19’s effects. They also are better able to predict pandemic trends to help contain the outbreak and to prevent further spread.
What Kinds of Healthcare Data Analytics are There?
Some situations call for different kinds of analyses. Those types of analytics include:
- Predictive. This type uses existing and historical data to make predictions about the future.
- Descriptive. With this type, historical data is used to make comparisons or uncover patterns. It’s best used for figuring out what has already occurred.
- Prescriptive. This type, which leans heavily on machine learning, also helps with predictions about future outcomes. Here, insight can be gained about the optimal course of action.
Can Data Analytics Reduce Costs?
This is an important question as healthcare costs continue to spiral upward. And the answer is a decided yes.
Healthcare organizations and clinicians can use predictive and prescriptive analytics to create nuanced models for reducing costs as well as patient risk. Analytics can also be used to cut down on missed doctor appointments, decrease fraud, prevent equipment problems, manage supply-chain costs, and more.
What is Predictive Modeling?
This is the process of analyzing existing and historical data to identify trends and predict future outcomes, including potential risks for chronic illness, for example. Compiled data can be used for readmission prediction and prevention, risk scoring, and for predicting infection and condition deterioration. And that’s just on a patient level. On a larger scale, predictive modeling can foretell viral outbreaks, for example, which can lead to preventive measures.
Predictive modeling also has administrative applications, in that it can heighten efficiencies and reduce costs across the board.
Ultimately, improving healthcare with data analytics empowers your people to live healthier, happier lives – IF you have the right approach. As it is, a JAMA study indicates that a quarter of U.S. healthcare spending is considered wasteful.
We recommend the leading global healthcare consultant Mercer, which employs proprietary analytics tools with insight-driven healthcare benefits strategies. Mercer identifies cost drivers such as high-cost claims as well as specific interventions and predicts risk for healthcare costs and performance. And through continuous monitoring and analysis, can help define your next steps.
