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.
MLE - The Drive Toward Achieving Business Objectives
Machine Learning Engineering (MLE) is the use of scientific principles including statistics, software engineering, problem solving skills, subject domain knowledge and machine learning algorithms to build computing solutions to complex healthcare and business challenges. Such challenges have either well-defined types of answers; such as yes/no, true/false, positive/negative sentiment, etc., or unknown answers as associated with anomaly detection and clustering to find similarities and differences.
COVID-19 - Opportunities
FoundationDx is offering machine learning studies to qualify risk factors for adverse COVID-19 outcomes. Although general risk factors are exposure, age and comorbidities; there are likely other factors that are yet unknown which also contribute to risk and severity. Metabolic Syndrome, Vitamin D levels, and off-label use of the common drugs such as Metformin, Ivermectin, Fenofibrate and Fluvoxamine, for example, have been cited in multiple academic studies to have an impact on COVID-19 susceptability and/or severity. Are you challenged to understand your COVID-19 outcomes? If so, contact us to learn more about Quick Turnaround Studies to identify associations between your population, health data and COVID-19 outcomes..
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