We build machine learning algorithms to predict the state of human health. Our solutions generate accurate, real-time predictions to help diagnose disease by synthesizing a multitude of clinical and biological patient data inputs. In the world of artificial intelligence-powered healthcare tools, we have the experience, infrastructure, clinical and regulatory expertise to tackle complex challenges with industry leaders.
One of our core values is “Patients and partners come first.” We measure our impact by patient outcomes and partner success.
Clinically validated tools
Agile engineering & flexible implementation
Dascena’s CORE clinical database was seeded with data from inpatient settings at health systems & academic partnerships. As the breadth and depth of our database continues to expand, we are able to target a wider variety of diseases with even greater accuracy.
How we can help: Validated clinical decision support tools that recognize patient risk of disease or deterioration, improving patient care delivery and saving costs.
InSight is a CDS (clinical decision support) tool that uses patient vitals to predict sepsis onset earlier than standard of care rules-based criteria.
How we can help: Combining biomarker, historical, and real time patient data to increase the predictive power of traditional point of care diagnostic tools.
How we can help: Optimizing drug development by identifying patients most likely to benefit from a targeted therapy. The predictive power of our tools can lead to smaller, shorter, and more cost-effective clinical trials.
Dascena developed an algorithm that helped Takeda link reductions in a surrogate endpoint (ΔGRV) to improvements in patient outcome (mortality).
This result helped Takeda determine development next steps by tying existing surrogate endpoint results to clinical probability of success.
Dascena developed an algorithm that used readily available EHR data to identify patients most at risk for Pulmonary Hypertension (PH) and Pulmonary Arterial Hypertension (PAH).
Dascena’s algorithm accurately predicted 80% of PH patients 12 months before diagnosis.
How we can help: Tools that quantify patient and population level risk to drive better care, which can be measured both clinically and financially.
Payers face ever growing pressure from employers to provide best in class healthcare at more affordable prices. Cost sharing and access restrictions have created a greater level of financial burden for both patients and their employers, making healthcare treatment options more difficult. In addition, the impact of COVID is driving value based healthcare where preventative measures (keeping patients healthy) are of top priority.
Care is now well positioned to evolve with the advancement of big data and machine learning. Algorithms can be used to optimize care for chronic conditions, and identify risk and benefits of treatments for patients. This allows Payers to make informed decisions, mitigate risk, and use innovation to drive good health.