FoundationDx builds and automates innovative machine learning solutions in healthcare and underserved organizations that need to efficiently and effectively drive patient satisfaction, healthcare quality and process performance goals in complex data environments.
FoundationDx is offering Quick Turnaround machine learning studies to evaluate the effectiveness of using automated anomaly detection for improving the efficiency and effectiveness of your data driven quality and outcome processes.
Examples that may lead to reduced healthcare costs and uniformity of care:
- Evaluate historical management decisions for a specific cohort of patients and identify those in-process decisions that are highly unusual or unexpected.
- Detect anomalous workflows based on cohort treatment data
- Detect unusal network login behavior
The healthcare industry has yet to seize the opportunity to optimize people, process and technology needed to drive patient experience and healthcare quality in an era of consumerism and precision medicine.
Complex data environments lead to useful information being lost because useful information is not easily identified. Do you know what factors are associated with your outcomes of interest?
Why use FoundationDx?
- Gain insight into what data measures impact positive and negative outcomes
- Operationalize what you learn into Business Process Improvement
- Periodically re-analyze your data to measure performance improvement
- Continually learn new outcome factors and decision rules and repeat
- Leverage text-based notation in the continuous learning process
- Leverage IT staff to produce quality data designed to drive operational excellence as a learning organization
- Perform periodic automated Machine Learning on your data in your own data environment