Ensemble is a customized artificial intelligence enterprise solution that leverages multiple learning algorithms to achieve better predictive performance than standalone machine learning algorithms. Another important aspect of Ensemble is its ability to benchmark your organization's historical performance against data from industry peers to identify areas to make better data-driven decisions to increase revenues and improve patient care.
Ensemble analyzes large amounts of data to make predictions about insurance coverage and reimbursement, which improves the accuracy of the billing process and reduces the risk of errors.
Ensemble uses a combination of supervised and unsupervised learning techniques, including decision trees, random forests, and deep neural networks, to achieve better predictive performance than any of the constituent learning algorithms alone.
The platform compares data from similar healthcare organizations in the same industry to identify opportunities for improvement, allowing practitioners to identify areas where performance can be optimized to improve care and revenues.
Ensemble automatically optimizes parameters of the algorithms to achieve the best performance. This feature is especially useful for organizations that do not have the expertise or resources to manually tune the parameters of the algorithms.
Ensemble offers a streamlined and automated process for checking patient eligibility and collecting payment for services. The platform works with over 8,600 payors to provide real-time and batch eligibility checks as part of the automated claim submission process, ensuring accurate patient information and returning patient benefit information.
Ensemble is also designed to be highly customizable to meet the specific needs of different organizations. The platform can be configured to analyze data from various sources, including electronic health records, financial transactions, and other patient data.
Ensemble allows healthcare organizations the ability to gain a comprehensive understanding of their clinical operations and to make data-driven decisions that will improve performance and increase revenues.
