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.
Unsupervised learning for Anomaly Detection
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, reduction in systemic risks, better outcomes or improved uniformity of care:
- Identifying transactions that are potentially fraudulent.
- Learning patterns that indicate that unususal network activity is occuring.
- Finding abnormal clusters of patients.
- Checking values entered into a system
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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, such as factors leading to outcomes of interest, are not easily identified.
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
