Automating the Heart failure Diagnosis and treatment

Imagine delivering the best heart failure treatment to each victim worldwide every time. This dream is possible today. 

  Cardiolert Systems (Cardiolert) has developed a comprehensive solution to improving heart failure outcomes that is implementable worldwide. 

Machine-learning and AI are transforming every industry. Recently, Alphabet’s DeepMind subsidiary demonstrated the power of machine-learning by beating the world Go Champion. While games prove a point, dollars drive innovation. Medicine is a major focus. Congestive Heart Failure (Heart failure) treatment is a prime target. It is labor intensive, costly, and afflicts 100 million worldwide. In the US, hospitalization alone exceeds $30 billion; 100 million victims worldwide are a burden on every economy.

Cardiolert is Commercializing Heart Failure Treatment over the Web.

  

Heart failure is a chronic disease of the circulatory system. It is a lethal condition with a five-year mortality expectation. A weakened pump deprives the body of oxygen; blood pools in the lungs diminishing the oxygen supply; oxygen deprivation exhausts the victim and destroys vital organs. Quality of life deteriorates faster. Cardiolert’s proprietary technologies free the victim by improving outcomes and cutting the cord between the physician and the patient through a Web interface offering the best diagnoses available at that time. The diagnostic platform opens diagnostic analysis to the crowd linking victims to skilled diagnosticians worldwide. Victims are no longer hostage to their family physician. Treatment data fed into a machine-learning engine will produce diagnoses superior to any single diagnostician’s interpretation.

Cardiolert’s plan is implementable in 18 months.

Improving outcomes decreases cost.

    

Improving outcomes is the key to decreasing costs. Improving diagnoses improves outcomes. Diagnoses relies on diagnosticians possessing wide variation in skills and training. As one might expect, skill variations produce outcome variations. The diagnostic methodology is a complex assimilation of dozens of patient measurements into specific medication recommendations. The objective of these treatments is reduced strain on the heart to increase blood flow. A system dependent on individual skill is a flawed system.  This simplicity of structure gives confidence in this design and solution. It is the design capable of accommodating automated diagnoses.

Machine Learning is the answer.