Predictive analytics are powerful tools that can provide your organization with actionable insights regarding your biggest challenges. Insights can include clinical predictions like disease onset and hospitalization, financial predictions like high-cost members and group cost, and more.
Making predictive analytics part of your organization’s normal operations can help your organization identify and mitigate risk, but some leaders may be hesitant to make the investment of time and money. Here’s how you can start with predictive analytics in your organization.
Find a valuable use case
When starting with predictive analytics, you don’t have to try to solve your organization’s biggest problem first. Starting small and achieving success quickly may help you gain support from others in your organization and may lead to approval for additional, larger projects. When implementing predictive analytics, choose an existing problem – one that solving will provide clear ROI and be of real value to your organization. Choosing a use case that has clear ROI can also help get leadership on board. Additionally, choose a problem that can be solved in a timely manner and will be easy to test.
There are numerous use cases that can benefit from predictive analytics, so choose one that aligns with your organization’s goals.
With Lumiata’s predictive analytics, you can:
- predict high-cost members to help you set optimal pricing and increase competitiveness
- predict disease onset to help you identify high-risk patients and provide risk-based care
- prediction suspicious claims before they’re paid to ensure timely and accurate billing
- and much more.
Determine which data you need and prepare it
Once you decide which problem you’d like to solve with help from predictive analytics, you need to determine which data to use and prepare that data for machine learning. The better the data, the better the predictions. Your data might consist of claims data, EHR data, and other structured and unstructured data. Take note of what assets you have and what’s missing – you may be able to supplement your data with data from an external source, like Lumiata. After you’ve collected your data, it’s important to clean it and prepare it for machine learning so you can generate accurate, actionable insights.
With Lumiata’s Prism, you can make clinical and financial predictions with as little as 50 patient records. Our deep learning model is built on 130+ million claims and EHRs that predict costs and more than 130 diseases over precise timelines. We can also prepare your data for machine learning by using our smart cleaning algorithms to identify and remove data that are incomplete, incorrect, improperly formatted, or duplicated.
Set up a team
To help your first predictive analytics project succeed, create a team of key people to guide the predictive analytics process and lead the project from start to finish. This might include a business owner, IT or software expert, actuary, case manager, or domain expert. Include stakeholders and allow them to provide feedback. Additionally, determine who will be responsible for building models and generating predictions; Lumiata offers tools for data science teams as well as business teams. Ensuring each person is clear on their specific role as well as the team’s overall goals will help the project flow more smoothly and make team members feel involved.
Generate predictions
Once you’ve identified the problem you want to solve, gathered and prepared your data, and created a project team, it’s time to generate predictions. To do this, your team may decide to build your models, use pre-built models, or have models customized to fit your specific needs. With Lumiata, all of these are options. We can also generate predictions in minutes rather than the hours required by similar machine learning models.
Depending on your use case, you might make predictions about the onset or progression of diseases within the next 12 months, high-cost claims, group cost, hospital admission and readmission, medical events like surgeries, and more.
Put the predictions to work
When you have predictions, it’s time to take action. Based on the predictions generated, what steps can you take towards solving your organization’s chosen problem? How can you implement changes – even small ones – to your existing processes and workflows? For example, if your goal is to set better pricing and you used predictive analytics to predict costs, you can adjust your prices to help cover these potential costs. If your goal is to reduce readmissions and you’ve predicted patients who are likely to end up back at the hospital, you can adjust their care management plans to help keep them healthy enough to stay home.
Track the results
Once you’ve implemented process or workflow changes based on the predictions generated, it’s important to track how those changes are impacting the problem you’re trying to solve. Create a study to see whether or not an AI model or process is leading to better results or improving existing solutions, and review model performance regularly. Remember to focus on your goals and the problem you want to solve, determine how you will define success, and measure the outcomes of your predictions-informed actions. See how your results compare to the original predictions.
Make changes as needed
The final step in getting started with predictive analytics is to adjust your approach as needed. After you’ve made predictions, made changes based on those insights, and tracked the results, take a look at what’s working and what’s not. Are you seeing a big improvement or only minor improvements? Are your changes helping you solve the problem you chose in step 1, or do you need better results? If the changes you implemented aren’t providing the results you wanted, consider what you can do differently going forward. Then, continue the process of predicting, testing, and tweaking to continuously improve.
Get started with Lumiata’s predictive analytics
Ready to get started with predictive analytics in your organization? Prism by Lumiata is a revolutionary self-service deep learning approach that requires minimum data for maximum results. You can predict high-cost claims and members, when diseases will onset or progress, and more, as well as prevent future encounters with 60-80% accuracy.