Cardiolert Systems (Cardiolert) designed a methodology and data structure for capturing diagnostic logic. A single data variable – the patient measurement - drives the diagnostic logic. Simplicity is the handmaiden of feasibility giving confidence in the design. The method defines the diagnosticians’ reasoning in detail and stores the thinking with the results (outcomes). The captured logic is available to process against future sets of patient measurements for advising diagnosticians and eliminating manual “re-processing” of defined reasoning. This empirical cycle of care, “test-treat-retest”, data is the experience feeding deep learning software. Processing past and present datasets identifies correlatives to positive and negative outcomes generating evidence-based inferences for a variety of qualitative uses including practitioner assessment, treatment evaluation, and outcome quality validation.
The data structures support application of machine-learning logic. Cardiolert reviewed the design with a machine learning consulting practice’s chief scientist who concluded “…this problem is “shaped” like a typical predictive machine-learning problem…. With sufficient diagnoses and treatment cycles to feed a machine-learning engine, this design automates significant portions (if not all) of the diagnostic effort.” The Bio-Informatics Department at Arizona State University has validated the design and is collaborating on construction of the machine-learning engine.
The is an “Uber” “Platform” containing the diagnostic methodology and necessary medical data linking the “Crowd” – patient and diagnostician. Access to the “Crowd” solicits diagnostic insights from anyone worldwide. Patients will access for the best care and diagnosticians will access accumulated knowledge of the Crowd and deliver diagnostic insights. The “test-treat-retest” data will fuel a machine learning engine for automating the diagnostic process. Rapid accumulation of treatment histories will accelerate the precision of machine-learning diagnoses.
This implementation is beyond anything conceived today - a monolithic method for defining diagnoses, determining treatment, and delivering best care worldwide. Concentrating the best diagnostic logic into a single repository creates the capability to offer the best treatment available at that moment to the next patient seeking care worldwide. Think about the implication.
The majority of heart failure victims live in less developed countries where skilled diagnoses are nonexistent. Delivery of the very best care to deprived populations is a philanthropic and financial benefit. India, for example, has the highest heart failure prevalence of any country.
A solution for treating 120,000 million heart failure victims and 1.5 billion hypertensives will transform medical care. Uber is transforming transportation. Amazon has transformed the retail industry. The value of the platform will exceed $50 billion.
Cardiolert will transform the way victims of chronic disease receive care. The application of machine-learning will create orders of magnitude in outcomes improving quality of life. Imagine the best treatment every time to everyone.
The philanthropic dividend produced by this solution is new. None of the transformational platforms reduce suffering contemplated by outcome improvements. No institution offers quality of life improvements in this context.