About

FoundationDx Logo About

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. We’re a small, self-funded company that’s led by its founders. We provide valuable early insights into process risk and performance improvement factors at modest cost.

Example Use Cases

Description
Identifying anomalies in data that may be of significant business or outcome interest
Mapping multidimensional data, such as population data, into meaningful and manageable sub-populations

Why Use FoundationDx?

Benefit
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

Example Use Cases

Use Case	Description
Identifying At-Risk Populations	By analyzing demographic and laboratory data across a population, anomaly detection can identify groups at higher risk for certain diseases. This includes recognizing patterns related to age, ethnicity, geographic location, and other socio-economic factors that might be associated with specific health risks.
Tailoring Public Health Interventions	Anomalies in data can signal the need for targeted public health interventions. For instance, detecting a higher incidence of a particular health condition in a specific demographic group can lead to tailored health campaigns or preventive measures for that group.
Predicting Disease Outbreaks and Trends	Anomaly detection can help in predicting potential outbreaks of infectious diseases or increasing trends in chronic diseases within a population. This allows healthcare systems to prepare and respond effectively, potentially averting public health crises.
Resource Allocation and Planning	By understanding the health trends and risks in different segments of the population, healthcare systems can better allocate resources, such as focusing preventive care in areas with higher risk or ensuring adequate healthcare facilities in underserved regions.
Improving Screening Programs	Anomaly detection can inform and improve screening programs by identifying populations that might benefit most from early screening for certain conditions, thus enhancing the effectiveness of these programs.
Customizing Health Education and Communication	Based on anomalies identified in demographic data, health education programs can be customized to address the specific needs and concerns of different population segments.
Enhancing Chronic Disease Management	For populations with a high prevalence of chronic diseases, anomaly detection can help in identifying patterns that lead to better management strategies, potentially reducing the burden of these diseases.
Social Determinants of Health	Anomaly detection can uncover correlations between health outcomes and social determinants of health like housing, education, and employment status, providing insights that can guide policy and intervention strategies.
Cost-Effective Healthcare Delivery	By focusing on prevention and early intervention in high-risk groups identified through anomaly detection, healthcare systems can reduce the overall cost burden associated with late-stage treatment.
Global Health Surveillance	In a broader context, analyzing demographic and laboratory data across regions or countries can help in global health surveillance, aiding in the detection and control of potential pandemics or international health concerns.

Media

Special programs are provided for 501(c)(3) organizations